§لماجستير.pdf · Detection and Analysis of Coronal Mass Ejection (CME) from SOHO/LASCO...
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Detection and Analysis of Coronal Mass Ejection (CME) from SOHO/LASCO Coronagraphs Data A Thesis Submitted to the Department of Astronomy and Space, College of Science, University of Baghdad In Partial Fulfillment of the Requirement for the Degree of Master of Science in Astronomy and Space By Zeinab Fadhil Hussein AL-Hakeem (B.Sc. in Astronomy and Space 2002) Supervised by Dr. Ahmed Abdul Razzaq Selman 2015 A.D 1436 H Republic of Iraq Ministry of Higher Education & Scientific Research University of Baghdad - College of Science Department of Astronomy and Space
Transcript of §لماجستير.pdf · Detection and Analysis of Coronal Mass Ejection (CME) from SOHO/LASCO...
Detection and Analysis of Coronal Mass Ejection (CME) from SOHO/LASCO
Coronagraphs Data
A Thesis
Submitted to the Department of Astronomy and Space, College of Science, University of Baghdad
In Partial Fulfillment of the Requirement for the Degree of Master of Science
in Astronomy and Space
By
0TZeinab Fadhil Hussein AL-Hakeem
(B.Sc. in Astronomy and Space 2002)
Supervised by
Dr. Ahmed Abdul Razzaq Selman
2015 A.D 1436 H
Republic of Iraq Ministry of Higher Education & Scientific Research University of Baghdad - College of Science Department of Astronomy and Space
ماء بروجا تبارك الذي جعل في الس وجعل فيها سراجا وقمرا منيرا
صدق هللا العظيم
)٦۱الفرقان (األية
I
Dedication
I dedicate this research to my family who supported me in
everything, to my friends who helped me finished this
project, and most of all to the Almighty God who gave me
the strength and good health while doing this.
Zeinab
II
Supervisor Certification
I certify that this thesis is prepared by Zeinab Fadhil Hussein AL-
Hakeem under my supervision at the Department of Astronomy and
Space, College of Science, University of Baghdad, as a partial of
fulfillment of the requirement needed to award the degree Master of
Science in Astronomy and Space.
Signature :
Name : Dr. Ahmed Abdul-Razzaq Selman
Title : Lecturer
Address : Department of Astronomy and Space, College of Science,
University of Baghdad
Date : / / 2015
Certification of the Head of the Department
In view of the available recommendation, I forward this thesis for
debate by the examining committee.
Signature :
Name : Dr. Alaa Bakir Kadhim
Title : Assis. Prof.
Address : Head of Astronomy and Space Department, College of
Science, University of Baghdad
Date : / / 2015
III
Examining Committee
We, members of the Examining Committee, certify that after reading this
thesis and examining the student (Zeinab Fadhil Hussein AL-Hakeem) in its
contents, in our opinion that it is adequate for the award of the degree of
Master of Science in Astronomy and Space.
Signature:
(Chairman)
Name: Prof. Dr. Kamal M. Abood
Title: Professor
Date: / / 2015
Signature:
(Member)
Name: Dr. Najat M.R. AL-Ubaidi
Title: Assis. Prof.
Date: / / 2015
Signature:
(Member)
Name: Dr. Amjad AL-Sawad
Title: Lecturer
Date: / / 2015
Signature:
(Supervisor and Member)
Name: Dr. Ahmed Abdul-Razzaq Selman
Title: Lecturer
Date: / / 2015
Approved by the University Committee of Graduate studies.
Signature:
Name: Dr.Fadhil Abd Rasin
Title: Assis. Prof.
Address: Dean of College of Science, University of Baghdad.
Date: / / 2015
IV
Acknowledgment
First of all I would like to thank Allah for the wisdom and
Perseverance that he has been bestowed upon me during
this research project, and indeed, throughout my life.
I would like to express my sincere gratitude to my advisor
Dr. Ahmed Abdul Razzaq, for making this research
possible. His support, guidance, and advice throughout
the research project, are greatly appreciated.
The efforts of the Dean of the College of Science is kindly
appreciated. I also would like to thank the Chairman and
staff of Department of Astronomy and Space for their
support during this research.
My most appreciation goes to my family for their
continuous support, patience and encouragements while
preparing this thesis.
Zeinab
V
Abstract There is a continuous need for an automated, computer based
detection code that is able to isolate Coronal Mass Ejections (CME) from
various observatories. Being bright, hot and fast plasma with large
masses, CMEs are ejected from active regions of the solar corona in a
variety of directions with an occurrence that implies that the solar corona
is changing continuously. It is thought that CMEs are ejected into free
space due to a sudden instability. Although the exact mechanism of CME
generation is not clear yet, many studies focused on their behavior which
require development of automatic detection utilities.
In the present work, this task has been taken, where a computer code
was written that aimed to detect and analyze CME events using images
taken from the Large Angle and Spectrometric Coronagraph (LASCO)
detector on board the Solar and Heliospheric Observatory (SOHO). Few
selected examples of CMEs were studied by means of the present method
using LASCO archived images. Selected CMEs were a group of 20
events from the years 2000, 2002, 2003, 2007 and 2013.
The detection process was made using a Matlab program called
cmeDetect. This program contained various functions that were written
during this work for the task of analyzing CME events. The main
detection method used in this work was based on bulk detection of CME
events then track their motion. Analysis depended on LASCO images
with resolution 512x512 pixels. Final analysis included CME heights,
velocities, accelerations, masses, energies and directions of the detected
events. Comparison with CDAW library was extensively made.
The present cmeDetect code was able to detect and recognize well-
defined CMEs but if they were with faint density the detection is either
lost or decreased in efficiency. This was explained because the outer edge
-the most important CME part- is about 3 to 7% less than the actual one.
VI
The results revealed that this shortage of efficiency will cause most CME
height values to behave with similar behavior to the reference values of
CDWA, yet with generally higher values.
The results showed that there is a general good agreement with
CDWA catalog CME results with the angle of the detected CMEs. Less
agreement is found in height measurements and little agreement is found
with speed and acceleration measurements. Some of the present values
were in perfect agreement with CDAW values.
A remarkable behavior seen from few results was that the altitude
region 5 to 15 Rs (solar radius) acted as an acceleration area. Such
behavior, however, did not repeat for other CME examples.
Some of the CME height results were fitted linearly utilizing a least
square fitting and the calculated values of speed were improved. The
shorthand of some of the results were discussed and the conclusion from
these discussions was that the present code should be developed to
include few modifications.
Furthermore, there was an attempt to relate the presently calculated
CME areas with the archived CME masses in order to perform a mass-
area calibration. Two relations were suggested, the linear and non-linear
dependence of mass on CME area. Both suggestions were considered.
From this, the masses of few halo CMEs were approximately calculated.
It was found that the kinetic energy from the non-linear fit were higher
than those from linear fit.
VII
Contents
Dedication i Supervisors Certification ii Examination Committee Certification iii Acknowledgments iv Abstract v Contents vii List of Symbols x
Chapter One: Outline of SOHO Mission 1.1 Introduction 1 1.2 The Solar and Heliospheric Observatory (SOHO) 2 1.2.1 SOHO Objectives 2 1.2.2 Mission Lifetime 3 1.3 SOHO's Instruments 3 1.4 The LASCO Instrument 7 1.5 Detailed Scientific Goals of LASCO 10 1.5.1 Coronal Heating and Acceleration of the Solar Wind 10 1.5.2 Coronal Evolutions (Coronal Mass Ejections and Magnetic
Field) 11
1.6 Literature Survey 13 1.7 Aim of the Present Work 19
Chapter Two: Description of Coronal Mass Ejections (CMEs) 2.1 Introduction 20 2.2 The Structure of the Sun 21 2.2.1 The Solar Interior 21 2.2.1.1 The core 21 2.2.1.2 The radiative zone 22 2.2.1.3 The tachocline 22 2.2.1.4 The convection zone 22 2.2.2 The Solar Exterior 22 2.2.2.1 The photosphere 22 2.2.2.2 The chromosphere 22 2.2.2.3 The transition region 23 2.2.2.4 The corona 23 2.3 The Plasma 23 2.4 Solar Energetic Particles (SEP) 24 2.4.1 Coronal mass ejection (CME) 25
VIII
2.4.2 Solar flare 29 2.5 Properties of CMEs 30 2.5.1 CME Morphology and mass 31 2.5.2 Angular width 32 2.5.3 Occurrence rate 32 2.5.4 Velocity and energy 33 2.6 CME identification and measurement 33 2.7 Observational Features 34 2.8 CME Detection Catalogues 35 2.8.1 CDAW 35 2.8.2 CACTus 35 2.8.3 SEEDS 36 2.8.4 ARTEMIS 36 2.9 Theoretical CME Models 36 2.9.1 Catastrophe Model 37 2.9.2 Toroidal Instability 37 2.9.3 Breakout Model 37 2.10 A Complete Description of CDAW CME Catalog 37 2.11 CME Near Real Time Libraries 41
Chapter Three : Methods of Analysis
3.1 Image Processing 43 3.2 Image Enhancement 43 3.3 Image Resolution 43 3.4 Image Representation 44 3.5 Edge Detection 45 3.6 Image Filtering 45 3.7 Noise Filtering 45 3.8 Basics of Spatial Filtering 45 3.8.1 Smoothing Spatial Filters 46 3.8.2 Smoothing Linear Filters 46 3.9 Processes that are applied to the image to determine the edges 47 3.10 Hough Transform 48
Chapter Four: Results and Discussions 4.1 General Description of the Code cmeDetect 49 4.2 Computing Details of the Program 52 4.2. A Reading Filenames and Date 52 4.2.B Measuring the Solar Radius 54 4.2.C Filtering the Images 59 4.2.D CME Detection 65
IX
4.3 Numerical Results 68 4.3.1 The CME event of 02/12/2002 at time 17:16:00 76 4.3.2 The CME event of 04/12/2002 at time 01:33:00 4.3.3 The CME event of 28/12/2002 at time 12:31:00
80 83
4.3.4 The CME event of 28/12/2002 at time 16:26:00 87
4.3.5 The CME event of 01/01/2003 at time 17:13:00 90 4.3.6 The CME event of 01/03/2003 at time 12:21:00 4.3.7 The CME event of 18/03/2003 at time 06:52:00
93 96
4.3.8 The CME event of 24/01/2007 at time 13:41:00 98 4.3.9 The CME event of 02/05/2013 at time 03:47:00 101 4.3.10 The CME event of 17/05/2013 at time 08:37:00 103 4.4 CME Mass 4.4.1 Mass-Area Calibration 4.4.2 Halo CME Mass Chapter Five: Conclusions and Future Work
106 106 108
5.1 Conclusions 110 5.2 Suggestions for Future Development
112
References 113
Appendices
X
List of Abbreviations
Symbols Definition 2D two-dimensions
AR Active Region
ARTEMIS Automatic Recognition of Transient Events and Marseille Inventory from Synoptic maps
C/P Coronagraph/Polarimeter
C1 inner part camera
C2 middle part camera C3 outer part camera CACTus Computer Aided CME Tracking catalogue
CCD Charged Coupled Device CDAW Coordinated Data Analysis Workshop
CDS Coronal Diagnostic Spectrometer CELIAS Charge, Element, and Isotope Analysis System CMD Central Meridian Distance
CME Coronal Mass Ejections CMFD Computational Magneto Fluid Dynamics
COR1 Inner Coronagraph of STEREO
COR2 Outer Coronagraph of STEREO
COSTEP COSTEP DSF disappearing filament
EIT Extreme ultraviolet Imaging Telescope ERNE Energetic and Relativistic Nuclei and Electron
experiment ESA European Space Agency EUV Extreme Ultraviolet
FOV field of view
XI
GOLF Global Oscillations at Low Frequencies GSFC Goddard Space Flight Center HT Hough Transform
HVS Human Visual System
ICMEs Interplanetary Coronal Mass Ejections
IPM InterPlanetary Medium
ISTP International Solar Terrestrial Physics
LASCO Large Angle and Spectrometric Coronagraph MDI/SOI Michelson Doppler Imager/Solar Oscillations
Investigation MESEPs Multi Eruption Solar Energetic Particles Events
MHD Magneto Hydrodynamics
min minute
MLSO Mauna Loa Solar Observatory
MLSO Mauna Loa Solar Observatory
NASA National Aeronautics and Space Administration OCR Optical Character Recognition
OSO-7 Orbiting Coronagraph
RGB Red, Green and Blue
RMS Root Mean Square Rsun Sun radius
SEEDS The Solar Eruptive Event Detection System
SEP solar energetic particle event
SEP Solar Energetic Particle Events
SEP Single Energetic Particle
SMM Solar Maximum Mission
SMM Solar Maximum Mission (SMM) satellite
XII
SOHO Solar and Heliospheric Observatory
SSN Sunspot number
STEREO Solar Terrestrial Relations Observatory
SUMER Solar Ultraviolet Measurements of Emitted Radiation SWAN Solar Wind Anisotropies US United State UV Ultraviolet
UVCS Ultra-Violet Coronagraph Spectrometer VIRGO Variability of Solar Irradiance and Gravity Oscillations
1
Chapter one
Outline of SOHO Mission
1.1. Introduction
Coronal Mass Ejections (CMEs) are of interest for both scientific and
technological reasons. Scientifically they are of interest because they remove
built-up magnetic energy and plasma from the solar corona, and technologically
they are of interest because they are responsible for the most extreme space
weather effects at Earth, as well as at other planets and spacecraft throughout the
heliosphere [1]. A CME represents a significant phenomenon out of many
phenomena taking place at the corona and photosphere layers of the Sun,
because CME has the ability to transfer a large amount of solar plasma in a
certain direction. Such plasma is usually and considerably more dense than the
continuously ongoing flow of the solar wind.
The CME is defined as a large erupted plasma mass with magnetic field
from the corona of the Sun. It appears as a dense object leaving the Sun when
imaged with a white-light coronagraph. In extreme CME events, the total mass
of a single CME can reach up to 1016 gm with speed that may exceed 3000
km/sec. If such mass of solar plasma was directed toward Earth, notable
turbulences will occur in the geomagnetic field of the Earth. Modern life in its
dependence on electricity, communications systems and multi-task satellites is
more sensitive to such events if they are directed to it either directly or
indirectly. Therefore, CME studies represent an important field of space weather
in an attempt to predict solar effects on Earth. Furthermore, the secrets revealed
by CME generation and classifications also provide a very useful resource about
the solar physics, because these events may provide a wealth of information
about the structure of the solar surface.
The data are collected as images from one of the most useful space
missions to observe the Sun, the SOHO observatory. SOHO has many detectors
2
on its board, the data used in this work were only from C3 coronagraph of
LASCO detector.
In present chapter, details about SOHO are provided. All its instruments are
categorized as provided in the literature, and listed with brief description.
Because the present work makes use of LASCO C3, this detector is described
with elaborated details.
1.2. UThe Solar and Heliospheric Observatory (SOHO)
SOHO is a project of international cooperation between The European Space
Agency (ESA) and The National Aeronautics and Space Administration
(NASA) to study the Sun, from its deep core to the outer corona, and the solar
wind. Together with ESA’s Cluster mission, SOHO is studying the Sun-Earth
interaction from different perspectives. SOHO has easily accessible, spectacular
data and basic science results have captured the imagination of the space science
community and the general public alike [2].
1.2.1.U SOHO Objectives
SOHO was designed to answer the following three fundamental scientific
questions about the Sun [2]:
• What is the structure and dynamics of the solar interior?
• Why does the solar corona exist and how is it heated to the extremely
high temperature of about 1 000 000°C?
• Where is the solar wind produced and how is it accelerated?
Clues on the solar interior come from studying seismic waves that are
produced in the turbulent outer shell of the Sun and which appear as
ripples on its surface.
Details of SOHO design, specifications, and highlights to date can all be
found in [2]. SOHO is operated from NASA’s Goddard Space Flight Center
(GSFC) near Washington. There an integrated team of scientists and engineers
from NASA, partner industries, research laboratories and universities works
3
under the overall responsibility of ESA. Ground control is provided via NASA’s
Deep Space Network antennae, located at Goldstone (California), Canberra
(Australia), and Madrid (Spain).
1.2.2. Mission Lifetime
SOHO was designed for a nominal mission lifetime of two years. Because of its
spectacular successes, the mission was extended five times (in 1997, 2002,
2006, 2008, and 2010). This allowed SOHO to cover an entire 11-year solar
cycle (23) and the rise of the new cycle 24. SOHO was approved through the
end of 2012. However, till date (2014) the mission of SOHO is still active[2].
1.3. SOHO's Instruments
The scientific payload of SOHO comprises 12 complementary instruments,
developed and furnished by 12 international consortia involving 29 institutes
from 15 countries. Nine consortia are led by European scientists, the remaining
three by US scientists. More than 1500 scientists in countries all around the
world are either directly involved in SOHO's instruments or have used SOHO
data in their research programs. The instruments of SOHO are listed in Table
(1.1). The following is a brief description for these instruments [2].
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Instrument Name Full Name Manufacturer
CDS Coronal Diagnostic Spectrometer Rutherford Appleton Laboratory, United Kingdom
CELIAS Charge, Element, and Isotope Analysis System
Universitat Bern, in Switzerland
COSTEP Comprehensive Suprathermal and Energetic Particle Analyzer
University of Kiel, Germany, in German
EIT Extreme ultraviolet Imaging Telescope
NASA/Goddard Space Flight Center, USA
ERNE Energetic and Relativistic Nuclei and Electron experiment University of Turku, Finland
GOLF Global Oscillations at Low Frequencies
Institut d'Astrophysique Spatiale, France
LASCO Large Angle and Spectrometric Coronagraph
Naval Research Laboratory, USA AND
Max Planck Institute for Solar System Research, Germany
MDI Michelson Doppler Imager Stanford University, USA
SUMER Solar Ultraviolet Measurements of Emitted Radiation
Max Planck Institute for Solar System Research, Germany
SWAN Solar Wind Anisotropies FMI, Finland
AND LATMOS, France
UVCS Ultraviolet Coronagraph Spectrometer
Harvard-Smithsonian Center for Astrophysics, USA
VIRGO Variability of Solar Irradiance and Gravity Oscillations
Institut d'Astrophysique Spatiale, France AND
Physikalisch Meteorologischen Observatorium Davos, Switzerland
a) Coronal Diagnostic Spectrometer (CDS)
CDS detects emission lines from ions and atoms in the solar corona and
transition region, providing diagnostic information on the solar atmosphere,
especially of the plasma in the temperature range from 104 to more than 106°C.
b) Charge, Element, and Isotope Analysis System (CELIAS)
CELIAS continuously samples the solar wind and energetic ions of solar,
interplanetary and interstellar origin, as they sweep past SOHO. It analyses the
Table (1.1) The Instruments of SOHO [2].
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density and composition of particles present in this solar wind. It warns of
incoming solar storms that could damage satellites in Earth orbit.
c) Comprehensive Suprathermal and Energetic Particle Analyzer (COSTEP)
The COSTEP instrument detects and classifies very energetic particle
populations of solar, interplanetary, and galactic origin. It is a complementary
instrument to ERNE.
d) Extreme ultraviolet Imaging Telescope (EIT)
EIT provides full disc images of the Sun at four selected colors in the extreme
ultraviolet, mapping the plasma in the low corona and transition region at
temperatures between 8x104 and 2.5x106 °C.
e) Energetic and Relativistic Nuclei and Electron experiment (ERNE)
ERNE measures high-energy particles originating from the Sun and the Milky
Way. It is a complementary instrument to COSTEP.
f) Global Oscillations at Low Frequencies (GOLF)
GOLF studies the internal structure of the Sun by measuring velocity
oscillations over the entire solar disc.
g) Large Angle and Spectrometric Coronagraph (LASCO)
In this work the used data are mainly acquired from this instrument. LASCO
observes the outer solar atmosphere (corona) from near the solar limb to a
distance of 21 million kilometers, that is, about one seventh of the distance
between the Sun and the Earth. LASCO blocks direct light from the surface of
the Sun with an occulter, creating an artificial eclipse, 24 hours a day, 7 days a
week. This instrument consists of three coronagraphs, namely: C1, C2 and C3;
all of which will be described later in this thesis. LASCO has also become
SOHO’s principal comet finder.
h) Michelson Doppler Imager/Solar Oscillations Investigation (MDI/SOI)
MDI records the vertical motion “tides” of the Sun's surface at a million
different points for each minute. By measuring the acoustic waves inside the
Sun as they perturb the photosphere, scientists can study the structure and
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dynamics of the Sun’s interior. MDI also measures the longitudinal component
of the Sun’s magnetic field.
i) Solar Ultraviolet Measurements of Emitted Radiation (SUMER)
The SUMER instrument is used to perform detailed spectroscopic plasma
diagnostics (flows, temperature, density, and dynamics) of the solar atmosphere,
from the chromosphere through the transition region to the inner corona, over a
temperature range from 104 to 2x106°C and above.
j) Solar Wind Anisotropies (SWAN)
SWAN is the only remote sensing instrument on SOHO that does not look at the
Sun. It watches the rest of the sky, measuring hydrogen that is ‘blowing’ into the
Solar System from interstellar space. By studying the interaction between the
solar wind and this hydrogen gas, SWAN determines how the solar wind is
distributed. As such, it can be qualified as SOHO’s solar wind mapper.
k) Ultra-Violet Coronagraph Spectrometer (UVCS)
UVCS makes measurements in ultraviolet light of the solar corona (between
about 1.3 and 12 solar radii from the centre) by creating an artificial solar
eclipse. It blocks the bright light from the solar disc and allows observation of
the less intense emission from the extended corona. UVCS provides valuable
information about the microscopic and macroscopic behavior of the highly
ionized coronal plasma.
l) Variability of Solar Irradiance and Gravity Oscillations (VIRGO)
VIRGO characterizes solar intensity oscillations and measures the total solar
irradiance (known as the ‘solar constant’) to quantify its variability over periods
of days to the duration of the mission.
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1.4. The LASCO Instrument
This is the detector that was used in the work of the present research. The details
below are provided from the official developers of this detector.
The Large Angle Spectrometric Coronagraph (LASCO) is a wide-field
white light and spectrometric coronagraph consisting of three optical systems
having nested fields of view, that together observe the solar corona from just
above the limb at 1.1 Rsun, out to very great elongations. LASCO was developed
jointly by the Naval Research Laboratory (USA), the Max-Planck-Institut fur
Aeronomie (Germany), The Laboratoire d'Astronomie Spatiale (France), and the
University of Birmingham (UK), and was carried by SOHO flight in 1995 [2].
There are many types of data provided by SOHO [4]. To take an example,
the focus shall be introduced to the LASCO instrument. LASCO instrument
consists of three chronographs that image the corona of the sun in angle range
from 1.1 to 32 solar radii (solar radius Rsun is assumed ~700,000 km, or 16 arc-
minutes). In this instrument, distance is compared to the solar radius for more
accurate measurements and images. This instrument is designed as a set of
(coronagraphs) because it aims to study the minor emissions of the solar corona.
A chronograph is a telescope that is designed to block light coming from the
solar disk, in order to see the extremely faint emission from the region around
the Sun, called the corona. The coronagraph is also considered as an (occulting
device) because it blocks the direct light from the solar surface, i.e. the solar
sphere is not seen in images taken by a coronagraph.The three coronagraphs or
telescopes comprising LASCO are named by[3]:
i. The C1 Coronagraph (or Camera), is the instrument used to study the
inner corona. Its coverage (or field of view, FOV) varies from 1.1 to
3.0 Rsun.
ii. The C2 Coronagraph, with FOV extending from 2.0 to 6.0 Rsun. This is
the middle part instrument.
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iii. The C3 Coronagraph, with FOV covering the outer corona from about
3.7 to 32 Rsun. C3 represents the outer part instrument.
C2 telescope was made to overlap parts of both C1 and C3 in order to
provide satisfactory comparisons for data collected by the three devices. The full
details of structure of the three devices is assumed as an externally occulted
instrument – See below. In addition, the C1 is fitted with an imaging Fabry-
Perot interferometer, making possible spatially resolved high-resolution coronal
spectroscopy in selected spectral emission and absorption lines, between 1.1 and
3.0 Rsun. High definition CCD (Charged Coupled Device) cameras in each
telescope provide detailed images with an exceptional dynamic range, while
large digital memories and a high-speed microprocessor support extensive on-
board image processing and image data compression by large factors, that will
allow transmission of up to 10 full coronal images per hour. LASCO instrument
is carried by SOHO in the same container with the EIT instrument, i.e., both
instruments share some electronic circuits with each other [4]. There are
important tasks currently made by LASCO such as [5]:
• The heat source of the corona and how heat is distributed over the coronal
area and reasons behind coronal ejections and activities. This is made by
observing time sequences of coronal dynamical events, especially processes
that occur in coronal mass ejections, and the conditions that make them
happen.
• The distribution and properties of the zodiacal dust cloud, and what are
the effects on it of the small "sungrazing" comets. This is addressed by
measuring the spatial distribution and properties of circumsolar dust particles,
including those newly released from sungrazing comets.
• Acceleration mechanism of the solar wind. To achieve this, LASCO was
designed to be able to measure basic distributions properties of the plasma
9
parameters, such as: temperature, density, bulk and non-thermal (turbulent)
velocities, and direction of the magnetic field.
LASCO has an important feature, that it automatically obtains the required
observations simultaneously assuming there are three parts of the solar corona,
namely [5]:
i. The inner part using (C1) camera.
ii. The middle part using (C2) camera.
iii. The outer part using (C3) camera.
Therefore LASCCO will be able to find both the origin and direction of
coronal structures. This makes it possible to detect and monitor time
development of coronal activities.
In addition to quantitative measurements of temperature, density, velocity,
and magnetic field direction, C1 will also provide a new and important link
between white light outer coronal images (C2, C3). The SOHO spectrometers
(CDS and SUMER) observing on the disk and near the limb will benefit from
LASCO imagery depicting the global setting to which their measurements
apply. Their off-limb spectral measurements, as well as those by the (UVCS)
Figure (1.1) An example of LASCO C3 image [4].
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instrument of the ultraviolet corona, are all available during scientific analysis,
and perhaps for mission operations planning, independent measurements of the
electron density by LASCO[5].
1.5. Detailed Scientific Goals of LASCO
1.5.1. Coronal Heating and Acceleration of the Solar Wind
To determine how the solar corona is heated is assumed as the major unsolved
problem in solar coronal physics. Current theories of coronal heating center on
either heating by waves guided by the magnetic field, or heating by small-scale
reconnection. If wave heating is taking place in the corona, it should be possible
to detect it by measuring Root Mean Square (RMS) velocity fluctuations in
emission lines formed at coronal temperatures. UV coronal observations show
that these velocities should be in the range 20- 30 km/s. We would like to
measure the nonthermal velocities of large active region loops, which frequently
extend to heights > 100,000 km, with an accuracy of at least 10 km/s.
Measurement to this accuracy will provide a critical test of wave heating
theories. In addition to being able to measure velocities, it is necessary to image
the corona with sufficient spatial resolution to distinguish the major structural
elements of the low corona. Images from Skylab, SMM, and Yohkoh in soft X-
rays and the EUV show that a large active region loop has a cross-sectional
diameter of about 10,000 km. Thus, detailed measurements along a loop requires
a spatial resolution in the low corona of roughly 10 arc sec (7200 km). For an
isolated large loop, a spatial resolution of 20 arc sec should be adequate for
comparing line widths with the typical quiet corona[5].
If heating by small-scale reconnection is taking place, then theory predicts
that the heating rate should drop off rapidly with loop length. Testing this model
requires the ability to measure the temperatures, densities, velocities and, hence,
energy losses of the coronal plasma as a function of height. To determine the
lengths of the loops being observed, it is important to be able to distinguish the
large scale structure of the inner corona. As with the wave heating observations,
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this requires measurements with a spatial resolution in the low corona of 10 to
20 arc sec [4].
1.5.2. Coronal Evolutions (CME and Magnetic Field)
In addition to the fundamental questions of what heats the corona and what
accelerates the wind, there are a host of additional important questions that
LASCO will address. These concern primarily the large-scale structure and
evolution of the outer corona and its extension into the interplanetary medium.
Four such questions are [3]: In order to answer the question about what is the
effect of emerging magnetic flux on large scale coronal features? then one
should mention that observations of the lowest extent of the corona can be made
from the ground. To understand fully the effect of emerging flux, it is necessary
to go from observations of emerging flux at the surface, through the innermost
corona, to the far outer corona. Thus, LASCO must be able to bridge the gap
between the low coronal observations made from the ground and the traditional
space borne coronagraphs, by observing the inner and middle corona at the same
time.
Moreover, it is vital to image the corona outward as far as possible to track
the propagation of plasma disturbances which are expected to accompany
reconnection processes. It is also important to be able to compare coronagraph
observations with simultaneous images obtained at X-ray and EUV
wavelengths. Following the effects of emerging flux on large scale coronal
features, therefore, requires simultaneous imaging over the solar corona from
just above the limb to the far outer corona. The innermost observable distance
above the limb should be close enough to the limb to provide some overlap with
observations from other SOHO imaging instruments (about 1.1 Rsun), while the
outermost observable distance from the limb should be as far out as possible to
provide the maximum radial extent to track plasma disturbances (about 30 Rsun).
12
Another question is about the properties of helmet streamers ? Streamers are
evolving structures. By comparing observations of individual streamers over a
large range of radii as a function of time, we will be able to determine the extent
to which the streamer pattern is affected by individual new "condensations" in
the lower corona. In addition, by obtaining profiles in the emission lines
observable in the inner corona, we will be able to determine the density,
temperature, and flow speeds in the legs of helmet streamers. At higher
atmospheric levels, changes in the widths and shapes of streamers should reveal
the possible existence of magnetic neutral points. Thus, we desire both extended
spatial resolution to allow measurements of the shapes of streamers (10 to 20 arc
sec in the inner corona, 20 to 30 arc sec in the middle corona), and also the
ability to measure line profiles with the same precision outlined above.
Another question is about the physical processes are responsible for coronal
evolution? The close spatial relation between the global pattern of coronal
intensity and the large-scale surface magnetic field clearly shows that coronal
evolution reflects evolution of the field. Observations in the inner corona are
necessary to determine whether material ejected in a coronal mass ejection
(CME) originated in hot coronal condensations over new active regions, or in
larger-scale structures which evolved gradually. Coverage of both a large
azimuthal and radial extent of the corona is necessary to obtain a complete mass
budget of coronal material, and to show the origin of ejected material. To
identify the physical mechanism responsible for coronal mass ejections, it is
critical to trace the ejected mass back to its source in the low corona.
Finally there is also a question about how does a CME evolve as it moves
into the heliosphere? A field-of-view extending from the inner corona near 1.1
Rsun to the far outer corona at about 30 Rsun, will allow us to address key issues
about the CME mechanism, and the ultimate assimilation of the CME into the
solar wind. For example, it should be possible to determine whether fast CMEs
continue to accelerate out to 20-30 Rsun, as radio-scintillation observations have
13
suggested. Increased spatial coverage and increased sensitivity will help to
resolve the question of the existence of "forerunners," as well as the occurrence
and relative positions of associated shock waves. Moreover, increased spatial
coverage makes it possible to determine whether slow CMEs, with speeds less
than 400 km/s within 10 Rsun, can accelerate to super fast-mode speeds at greater
radial distances[3].
1.6. Literature Review
In 1976, Mac Queen et.al. was one of the first to show some results on Coronal
Transients – the most rapid variations observed. Characteristic mass and
energies involved in mass ejection transients, their temporal and spatial
distributions, their associations with surface phenomena and possible
interplanetary signatures, and finally their role in coronal evolution are briefly
noted [6].
Later on, Macqueen R. M. in 1980, concluded that at present, no
compelling evidence is available to distinguish between transient driving
mechanisms, but future observation of the corona and interplanetary medium
may resolve the present ambiguity [7].
Few developments were made and continued till Richard A. Harrison in
1995 found that most models to be unphysical and all represent a gross over
simplification of solar conditions. In conclusion he set up a cartoon model which
best fits the observations and which he feel should be further developed [8].
Again in 1995, Gosling J. T. pointed out that variety of solar and solar wind
observations are consistent with the concept of sustained 3-dimensional
reconnection within the magnetic legs of CMEs close to the Sun [9].
On the other hand, Dere K. P. et al. in 1997 showed that out of particular
interest is the fact that this large-scale event, spanning as much as 70 deg in
latitude, originated in a volume with dimensions of roughly 35" (2.5 x 104 km).
Further, a disturbance that propagated across the disk and a chain of activity
14
near the limb may also be associated with this event as well as a considerable
degree of activity near the west limb [10].
In 1998 Antiochos S. K. et al. presented numerical simulations which
demonstrate that his model can account for the energy requirements for CMEs.
He discussed the implication of the model for CME/flare prediction[11].
Further developments were provided by Andrew M.D. et al. in 1999 who
stated that there are two parts to the MHD model. The pre-event corona was
calculated using a 2-dimensional bi-modal model. The CME is simulated using a
time dependent perturbation at the base of the corona. The model successfully
reproduces the observed morphology, velocity profiles, and change in coronal
mass. The observed velocity asymmetry is a natural consequence of the structure
of the pre-event corona. Animations have been generated from both the data and
model to illustrate the good agreement between the observations and
simulation[12].
In 2000, David F. Webb reviewed some of the well-determined coronal
properties of CMEs, what we know about their source regions, and what their
manifestations are in the solar wind. One exciting new type of observation is of
halo-like CMEs, which suggest the launch of a geo-effective disturbance toward
Earth [13].
In 2001, Andrews and Howard pointed out that the causes and origins of
(CMEs) remain among the outstanding questions in Space Physics. The
observations of CMEs by the LASCO coronagraphs on SOHO suggest that there
are two distinct types of CMEs. The two types of events can be most easily
distinguished by examining height-time plots. The Type A (Acceleration) events
produce curved plots that often indicate a constant acceleration. The Type C
(Constant speed) events show a constant speed. These events are usually
brighter, larger, and faster than Type A events and may be associated with X-ray
flares. While the two types of events can be distinguished in other ways, the
15
height-time plots are a simple and unambiguous way to make this identification
[14].
In 2002, Wu Y.Q., et al. pointed out by using observations from the
satellites of the International Solar Terrestrial Physics (ISTP) Observatories, the
relationships among the coronal mass ejection (CME), the helmet streamer and
the disappearing filament (DSF) have been studied. His main conclusions are as
follows: (1) The DSF disrupted the streamer, thus resulting in the restructuring
of coronal field and causing the mass in the helmet streamer to form the CME.
(2) The DSF under a helmet streamer and the sigmoid soft X-ray loop are
possibly the precursors of the 6 January 1997 CME. (3) The energy stored in the
filament circuit and the energy of the CME (include kinetic, potential and
magnetic energies) are estimated and it is found that there was enough energy
stored in the filament to provide the CME of 6 January 1997. (4) The CME's
speed in response to the DSF is calculated. It is showed that the DSF can drive
the CME to the observed speed[15].
In 2003, Vourlidas A. Pointed out he employed a simple MHD simulation
using the LASCO measurements as constraints. Both the measurements and the
simulation strongly suggest that the white light feature is the density
enhancement from a fast-mode MHD shock. In addition, the LASCO images
clearly show streamers being deflected when the shock impinges on them. It is
the first direct imaging of this interaction[16].
In 2004 , Zhang Jie. presented a statistical study on the acceleration of
CMEs. This study is based on 23 CME events best observed by SOHO
LASCO/C1 coronagraph, which observes the inner corona from 1.1 to 3.0 RS .
The kinematic evolution of a CME has a distinct acceleration phase that mainly
takes place in the inner corona. He found that the acceleration duration
distribution ranges from 10 to 1100 min with a median (average) value at 54 min
(169 min). The acceleration magnitude distribution ranges from 6 m s −2 to 947
16
m s −2 with a median (average) value at 209 m s −2 (280 m s −2). He also find a
good correlation between CME acceleration magnitude A (in unit of m s −2) and
acceleration duration T (in unit of min), which can be simply described as
A=10000/T [17].
In 2005 Qiu, K. P., et al. pointed out that using the observations of the
EUV Imaging Telescope (EIT) and the Large Angle Spectrometric Coronagraph
(LASCO) on the Solar and Heliospheric observatory(SOHO) and solar soft X-
ray flux and radio bursts data,He study the low coronal signatures of a solar limb
coronal mass ejection (CME) on November 4, 2003. The two prominent
dimmings in EIT difference images were closely related to two large loops in
this event. The onset time and height of the CME and the lower limit of the
masses loss from dimming regions are estimated[18].
In 2006, Iyer K. N., et al. pointed out that description the space weather
effects of a major CME which was accompanied by extremely violent events on
the Sun. The signatures of the event in the interplanetary medium (IPM) sensed
by Ooty Radio Telescope, the solar observations by LASCO coronagraph
onboard SOHO, GOES X-ray measurements, satellite measurements of the
interplanetary parameters, GPS based ionospheric measurements, the
geomagnetic storm parameter Dst and ground based ionosonde data are used in
the study to understand the space weather effects in the different regions of the
solar-terrestrial environment. The effects of this event are compared and
possible explanations attempted [19].
Al-Sawad A. (2007) concluded that a combination of many solar energetic
particle events, each one of which is associated with single eruption, can create
one complex intensity- time profile that will result in masking the observation of
the first injected particles of the participated eruption near the Earth. These
events are defined as Multi Eruption Solar Energetic Particles Events
(MESEPs). Al-Sawad (2009 a,b) and Kocharove (2009) concluded that is a
coronal acceleration in the multi eruption intensity-time profile during the
interplanetary phase [20].
17
In 2008, Goussies Norberto, et al. pointed out that this work presents a
novel method for the detection of CMEs as recorded by the LASCO instruments
onboard SOHO. The algorithm we developed is based on level sets and region
competition methods, the CMEs texture being characterized by their co-
occurrence matrix. The texture information is introduced in the region
competition motion equations, and in order to evolve the curve, a fast level set
implementation is used [21].
In 2009, Bachtiar Anwar, pointed out that the intensity profiles of selected
area at several locations around the occulting disk are extracted from the running
difference images. CME event will be identified when there is abruptly change
in the intensity profile at particular position around the occulting disk. The time
and location are recorded into a file for further verification. As an initial
experiment, the procedures were applied to LASCO data taken in November
2003. his paper describes the methods, software development as well as the
preliminary results of CME detection [22].
In 2010, Norberto A. Goussies, et al. pointed out in this work presents a
novel method for the detection and tracking of CMEs as recorded by the
LASCO instruments on board SOHO. The algorithm they are developed is based
on level set and region competition methods, the CMEs texture being
characterized by their co-occurrence matrix. The texture information is
introduced in the region competition motion equations, and in order to evolve
the curve, a fast level set implementation is used[23].
In 2011, Carla Jacobsa, and, Stefaan Poedtsa, pointed out the state-of-the-
art in CME simulations, including a brief overview of current models for the
background solar wind as it has been shown that the background solar wind
affects the onset and initial evolution of CMEs quite substantially. They mainly
focus on the attempt to retrieve the initiation and propagation of CMEs in the
framework of computational magnetofluid dynamics (CMFD). Advanced
numerical techniques and large computer resources are indispensable when
18
attempting to reconstruct an event from Sun to Earth. Especially the simulations
developed in dedicated event studies yield very realistic results, comparable with
the observations. However, there are still a lot of free parameters in these models
and ad hoc source terms are often added to the equations, mimicking the physics
that is not really understood detail[24].
Al-Sawad A. et al. (2012) observed two narrow CMEs with high velocity
with SOHO/LASCO were accompanied by two impulsive SEP events registered
by SOHO/ERNE. They found that Most of the narrow CMEs cannot be
investigated from the SEP events association point of view because they are
mostly overwhelmed by the previous wide CMEs [25].
In 2013, Giordano S., et al. pointed out that the number of events detected
in UV is about 1/10 of the LASCO CMEs, and about 1/4 of the halo events.
They are found that UVCS tends to detect faster, more massive and energetic
CME than LASCO and for about 40% of the events events have been possible to
determine the plasma light-of-sight velocity[26].
In 2014, Valori G., et al. pointed out that observations of a filament
eruption, two-ribbon flare, and coronal mass ejection (CME) that occurred in
Active Region NOAA 10898 on 6 July 2006. The filament was located South of
a strong sunspot that dominated the region.. they are found that the twisting
leads to the expansion of the overlying field. As a consequence of the
progressively reduced magnetic tension, the flux rope quasi-statically adapts to
the changed environmental field, rising slowly. Once the tension is sufficiently
reduced, a distinct second phase of evolution occurs where the flux rope enters
an unstable regime characterized by a strong acceleration. Our simulation thus
suggests a new mechanism for the triggering of eruptions in the vicinity of
rotating sunspots [27].
19
1.7. Aim of the Present Work
In this thesis the aim is to write a computer code that automatically detects
CMEs from SOHO/LASCO images using Matlab. After detection is made, the
required code must be able to measure the basic properties of these CMEs,
namely: their height, time of evolution, speed, acceleration, total area and
direction. The code is to be written using Matlab. The results are then to be
compared with the well-known CME catalog library:
cdaw.gsfc.nasa.gov/CME_list/
In this work, the aim is focused on CME generation and detection using
an automated computer code.
۲۰
Chapter Two
Description of Coronal Mass Ejection
2.1. UIntroduction
Coronal mass ejections (CMEs) consist of large structures containing plasma and
magnetic fields that are expelled from the Sun into the heliosphere. They are of
interest for both scientific and technological reasons. Scientifically they are of
interest because they remove built-up magnetic energy and plasma from the solar
corona, and technologically they are of interest because they are responsible for the
most extreme space weather effects at Earth, as well as at other planets and
spacecraft throughout the heliosphere [1]. Most of the ejected material comes from
the low corona, although cooler, denser material probably of chromospheric or
photospheric origin is also sometimes involved [28].
The first spacecraft coronagraph observations of CMEs were made by the
OSO-7 coronagraph in the early 1970s [29]. These were followed by better quality
and longer periods of CME observations using Skylab, and SMM . In late 1995,
SOHO was launched and two of its three LASCO coronagraphs still operate today.
Finally late in 2006, LASCO was joined by the STEREO CORs [30]. These early
observations were complemented by white light data from the ground-based Mauna
Loa Solar Observatory (MLSO) K-choronameter viewing from 1.2 – 2.9Rsun and
green line observations from the coronagraphs at Sacramento Peak, New Mexico
and Norikura, Japan [31].
Figure (2.1). Timeline of the history of spacecraft relevant to CME study[1]
۲۱
2.2. UThe Structure of the Sun The Sun is a G2V main sequence star of luminosity LRsun R= 3.85 × 10P
26P W, mass
MRsunR = 1.99×10P
30P kg and radius RRsun R= 6.96×10P
8P m. It was born from the
gravitational collapse of a molecular cloud approximately 4.6×10 P
9P years ago, is
currently in a state of hydrostatic equilibrium ( ∇𝑃 = −𝜌𝑔), and predicted to enter
a red giant phase in another ~ 5 billion years before ending its life as a white dwarf
[31][32].
2.2.1. UThe Solar Interior This is the inner part of the Sun which contains most of the solar mass. It cannot be
seen directly because solar surface is not transparent to visible electromagnetic
radiation. This is because of the plasma environment inside the Sun which causes
photons to be scattered or absorbed before they can travel very far [34]. This part of
the Sun holds the following regions:
2.2.1.1. UThe core This is a dense and hot region. Solar core radius is around 20% of the solar radius.
The temperature also varies throughout the core, from around 15 million Kelvin at
the center to around 5 million Kelvin at the edge of the core. Within it, hydrogen
nuclei are fused in a reaction which mainly produces helium nuclei, neutrinos and
Figure (2.2) Structure of the Sun [33].
۲۲
photons. The output power is about 3.8 × 10P
26P W [34].
2.2.1.2. UThe radiative zone The next region represented by a layer extending from 0.2RRsunR to 0.7RRsunR is known
as the radiative zone because the primary method of energy transport here is by
radiation [34].
2.2.1.3.U The tachocline The tachocline is a narrow transition layer between the radiation and convection
zones. Charbonneau et al. (1999) reported that the thickness of the tachocline is only
around 4% of the solar radius. It was discovered using the relatively young field of
helioseismology which allows us to probe the solar interior using sound waves. It is
a region of very large shear as the rotation rate of the radiation zone is not equal to
the rotation rate of the convection zone. The change in rotation rate over this short
distance is a possible mechanism for the generation of large scale magnetic field and
has shown improvements in solar dynamo models [34].
2.2.1.4. UThe convection zone Once the photons reach a solar radius of around 0.7RRsunR, there is a change in how
the energy is transported through the interior of the Sun. For energy to be
transported by radiative processes alone [34].
2.2.2. UThe Solar Exterior
The Solar Exterior can be divided into:
2.2.2.1. UThe photosphere
The photosphere marks a very important boundary between the solar interior and
exterior. There is a large change in density and the solar plasma changes from being
opaque to transparent. The plasma is only weakly ionized in the photosphere and
much of the chromosphere, where temperatures are a few thousand Kelvin to ten
thousand Kelvin [34].
2.2.2.2. UThe chromosphere
Also called ‘sphere of color’, is a layer of the solar atmosphere that sits above the
photosphere. At this point the density of plasma drops dramatically, to as low as
10P
−11P kg mP
−3P. The chromosphere also contains a temperature minimum. For the first
500 km above the photosphere, the temperature continues to drop but then begins to
rise as the distance from the center of the Sun increases. Up until this point, the
۲۳
temperature has decreased in every layer of the Sun from the core [33].
2.2.2.3. UThe transition region The transition region is a very thin layer of the solar atmosphere which is an
interface between the chromosphere and corona. It has a large temperature gradient
with the temperature jumping from thousands to millions of Kelvin in only a few
thousand kilometers [33].
2.2.2.4. UThe corona It can be seen in visible light but only during a total solar eclipse as its intensity is
very low compared to that of the photosphere. The corona is a heavily structured
region due to the magnetic fields that permeate it. The size of the corona is not well
defined, as it becomes tenuous at large distances from the Sun. Technically, the
Earth sits within the solar corona, although by that point the structures are more
commonly referred to as part of the solar wind. The plasma in the corona is very
hot, rising to millions of Kelvin and is an irregular and dynamic structure which
changes on very short timescales as the magnetic fields constantly shifts and
reorient themselves [33].
2.3. UThe Plasma [35]U Plasma is the matter where all atoms or molecular are ionized, and it has basic
characteristics such as temperature, pressure, energy, particle velocity, density,
number of particles and magnetic field. Plasma is consisting of positively and
negatively charged particles (usually ions and electrons) which are subject to
electric, magnetic and other forces, and which exhibit collective behavior. Ions and
electrons may interact via short range atomic forces (during collisions) and by long
range electro-magnetic forces due to currents and charges in the plasma. Plasma
can also contain some neutral particles (which interact with charged particles via
collisions or ionization). Examples include the Earth’s ionosphere, upper
atmosphere, interstellar medium, and molecular clouds. Plasma pervades
intergalactic space, interstellar space, interplanetary space, and the space
environments of the planets.
Plasma generates cosmic rays, stellar flares, interstellar and interplanetary
shock waves, coronal mass ejections and magnetospheric storms. Plasma absorb
energy which is flowing steadily from the nuclear reactions within stars and from
۲٤
angular momentum shed by spinning magnetized bodies and release it explosively
as X-rays and energetic particles. Some examples of astrophysical plasma can be
listed in the following items:
1- Earth’s (and other planets’) ionosphere (above 60 km) and magnetosphere.
2- Sun’s and other stars’ atmospheres, and winds.
3- Comet’s ion tail.
4- Cosmic Rays (galactic and extra-galactic energetic particles).
5- Interstellar medium.
6- Jets in active galaxies - radio jets and emission.
7- Pulsars and their magnetosphere.
8- Accretion disks around the stars.
2.4. USolar Energetic Particles (SEP) The SEP events are one of the most effective phenomena in solar physics, which
have been widely observed near the Earth with energy ranges varying from some
keV/nucl to some GeV /nucl and they might have different sources such as solar
flare in the low corona, coronal shock and interplanetary shocks driven by CMEs,
which had been first observed in the early 1940s [35]. SEP events have always been
associated with events taking place at the Sun, such as flares, filament
disappearances and coronal mass ejections (CMEs) [36]. SEPs are so called because
of their high energy solar origin, and behavior as single particles. SEPs consist of
electrons, protons, alpha particles, 3He nuclei and heavier ions up to Fe [37].
The importance of SEP for solar physics is evident as they transport a large
fraction of the flare energy to other sites, carrying information on the properties of
their source plasma as well as of the acceleration process. The SEP can be measured
indirectly from their radiation signatures, extending over the entire electromagnetic
spectrum, as well as directly in the interplanetary medium by means of space born
instruments. It is customary to divide the solar energetic particle events, on the basis
of their duration of soft X-ray emission, into two major classes: gradual and
impulsive events. The duration of the accompanying soft X -ray emission, however,
is not the only difference between the above two classes.
۲٥
The gradual or eruptive events are associated with Types II and IV radio
bursts and CMEs that can produce coronal and interplanetary shocks. The energetic
particles observed during gradual events are dominated by protons. These large
proton events are seen over a wide range of solar longitudes relative to the
associated flare and have extended time profiles that can last for day’s. Gradual
events have small 4He/H ratios and do not exhibit 3He/4He or Fe/C enhancements.
The elements are observed in ionization states similar to those in the solar wind,
corresponding to an ambient coronal temperature of 2 × 10 P
6 PK. It must be pointed
out that some gradual SEP events have no connection with solar flares but are
associated only with CMEs.
The impulsive events are associated with Type III radio bursts and hard X-ray
and radio bursts, which are produced by high-energy electrons. The impulsive
events are dominated by electrons and are characterized by large 3He/4He ratios and
enhancements of heavier ions, such as Fe/O. It is believed that the 3He-rich, Fe-rich
ions are a common property of all impulsive solar flares. The impulsive events are
associated with flares observed in a narrow range of the western solar longitudes
[38].
There are two majority sources of SEPs from the Sun: the solar flare and
CMEs. Earlier, it has been thought that the solar flare is the major reason for SEPs
observed on Earth. But with the development of technology and space physics,
CMEs entered this field strongly [39]. CMEs and solar flare can be explained in
details as:
2.4.1. UCoronal mass ejection (CME)U A CME is a large eruption of plasma and magnetic field from the Sun. It can contain
a mass larger than 10P
13P kg and may achieve a speed of several thousand kilometers
per second. A typical CME has a mass of around 10 P
11P–10P
12 Pkg and has a speed
between 400 and 1,000 km/s. It also typically spans several tens of degrees of
heliographic latitude (and probably longitude) [40]. Many white-light CMEs display
a characteristic three-part structure: a bright leading edge, a dark void (cavity) and a
bright core. Figure (2.3) shows an example of a three-part CME recorded by
SOHO/LASCO. The frontal structure is coronal material, the cavity also is coronal,
۲٦
but may have higher magnetic fields and lower density, and the bright core is the
eruptive prominence. The three-part structure is seen in only about 30% of CMEs,
yet this is viewed as the “standard CME” configuration in observational and
theoretical studies [41].
CMEs are primarily detected by coronagraphs that block out the majority of
light from the Sun leaving the relatively faint surrounding corona. The most
successful coronagraph to date for CME detection has been the LASCO on board
SOHO, which has detected well over 10P
4P CMEs since its launch in 1995. LASCO
detects the CME by observing the white light scattered off the electrons within the
plasma of the CME. More recently, other spacecraft-based coronagraphs have
joined that of LASCO. These are the COR coronagraphs on board the STEREO
spacecraft and work on a similar principle to that of LASCO [40].
The outer leading edge may be a region where an expanding magnetic
bubble-like shell has compressed the overlying gas, piling the corona up and
shoving it out like a snowplow. The bright outer edge has also been pictured as an
expanding magnetic loop filled with dense, shining gas. The cavity or void is an
Figure (2.3): Illustrates a SOHO/LASCO image (with an EIT 195 image superposed) obtained on 20 December2001 showing the three-part structure of a CME above the southwest limb
[41].
۲۷
expanding, low-density region whose high magnetic pressure and strong magnetic
field might push coronal material aside. The filament core is the brightest part of the
coronal mass ejection, because of its high density [42].
The coronal mass ejections arrive at the Earth 1–4 days after a major eruption
on the Sun. They can result in strong geomagnetic storms with accompanying
auroras and the threat of electrical power blackouts [42].
CMEs represent an important source of solar variability from the point of
view of plasma and magnetic field. CMEs remove billions of tons of magnetized
plasma from the Sun and dump them into the Sun-Earth connected space once every
other day during solar minimum and several times per day during solar maximum.
CMEs also provide dramatic variable energy input to the magnetosphere, in addition
to and sometimes in combination with the high speed streams that originate from
coronal holes. CMEs are the source of major disturbances in the interplanetary
medium, and can be directly observed up to 32 RRsunR from the Sun, thanks to the
sensitive coronagraphs on board SOHO. LASCO have unprecedented dynamic
range and large field of view obtaining coronal images of very high quality [43].
Sheeley et al. (1999) devised a new method of generating CME height-time
maps and applied it to LASCO CMEs. They then proposed the following two types
of CMEs:
i. Gradual CMEs apparently formed when prominences and their cavities rise up from
below coronal streamers and characterized by slow speeds and weak, continuous
acceleration of less than 20m/ sP
2P. When seen broadside, their leading edges
accelerate gradually to speeds in the range 400–600 km/s before leaving 30RRsunR.
ii. Impulsive CMEs often associated with flares and Moreton waves (large-scale solar
coronal shock wave) on the visible disk. The extremely impulsive events with high
speeds and strong, rapid acceleration of more than 1000 m /sP
2P .When seen
broadside, these CMEs move uniformly across the 2–30 RRsunR at speeds higher than
750 km/s [44].
CMEs that appear to surround the occulting disk of the observing
coronagraphs in sky plane projection are known as halo. Halo CMEs, as shown in
figure (2.4) are fast and wide on the average and are associated with flares of greater
۲۸
X-ray importance because only energetic CMEs expand rapidly to appear above the
occulting disk early in the event. Extensive observations from SOHO mission’s
LASCO have shown that full halos constitute ∼3.6% of all CMEs, while CMEs with
width ≥120◦ account for ∼11%. Full halos have an apparent width (W) of 360◦,
while partial halos have 120◦ ≤ W < 360◦. Halo CMEs are said to be front sided if
the site of eruption (also known as the solar source) can be identified on the visible
disk usually identified as the location of H-alpha flares or filament eruptions [45].
When CMEs originate at a larger Central Meridian Distance CMD, they
appear asymmetric with respect to the occulting disk. The asymmetry can be
geometric (outline) or in brightness. The main body of the CME is moving toward
Earth with a slight western bias, so the brightness is mostly to the west. The diffuse
eastern part must be the disturbance that surrounds the main body of the CME. The
outline asymmetry is obvious in CMEs originating from close to the limb (either in
front of the limb or behind) [46].
2.4.2 USolar flareU Solar flares are the most energetic and interesting phenomena in the solar system,
releasing up to 10P
32P ergs of energy on time scales of several tens of seconds to
several tens of minutes [47]. The flare is still considered as one of the main sources
for SEPs.
During the flare, annihilation of magnetic field will transfer the energy to
kinetic energy of energetic particles, and this indicates the importance of the flare as
Figure (2.4): An image of a halo CME in 28 October 2003 left: SOHO/LASCO C2 coronagraph image & right: SOHO/LASCO C3 coronagraph image [43].
۲۹
a source of SEPs [39]. The energy released in solar flares is in the form of
suprathermal electrons and ions, which remain trapped at the Sun and produce a
wide variety of radiations (Ramaty and Murphy, 1987) as well as escape into
interplanetary space (Reames, 1990). The radiation from trapped particles consists
in general of (1) continuum emission, which ranges from radio and microwave
wavelengths to soft (~ 1–20 keV) X-rays, hard (~ 20–300 keV) X-rays, and finally
gamma rays (above 300 keV), which may have energies in excess of 1 GeV; (2)
narrow gamma-ray nuclear de-excitation lines between 4 and 8MeV; and (3) high-
energy neutrons observed in space or by ground-based neutron monitors.
The solar flare events are divided into two classes: impulsive and gradual.
Gradual events are large, occur high in the corona, have long-duration soft and hard
X-rays and gamma rays, electron poor, well associated with coronal and
interplanetary shocks, associated with Type II radio emission and coronal mass
ejections (CMEs) and produce energetic ions with coronal abundance ratios.
Impulsive events are more compact, occur lower in the corona, produce short
duration radiation, have high e/p ratio, and are never associated with interplanetary
shocks and exhibit dramatic abundance enhancements in the energetic ions [47].
The output radiation of flares covers throughout the electromagnetic
spectrum, from gamma rays to X-rays, through visible light out to kilometer-long
radio waves[20].
2.5. UProperties of the CMEs The measured properties of CMEs include their occurrence rates, locations relative
to the solar disk, angular widths, speeds and accelerations, masses, and energies.
There is a large range in the basic properties of CMEs, although some of this scatter
is likely due to imaging projection effects. Their speeds, accelerations, masses, and
energies extend over 2 – 3 orders of magnitude , and their angular widths exceed by
factors of 3 – 10 the sizes of flaring active regions. Table(2.1) summarizes the
statistical properties from all of the near-Earth space borne coronagraph
observations of CMEs [1].
۳۰
CMEs can exhibit a variety of forms, some having the classical “three-part”
structure, usually interpreted as compressed plasma ahead of a flux rope followed by
a cavity surrounded by a bright filament/prominence. Other CMEs display a more
complex geometry. Some CMEs appear as narrow jets, some arise from pre-existing
coronal streamers (the so-called streamer blowouts), while others appear as wide
almost global eruptions. CMEs spanning very large angular ranges are probably not
really global, but rather have a large component along the Sun-observer line and so
appear large by perspective. These include the so-called halo CMEs [1].
Figure (2.5): Shows Schematic magnetic field configuration and flow pattern for a coronal mass ejection and flare system [1].
۳۱
___________________________________________________________ Coronagraph OSO-7 Skylab Solwind SMM LASCO
___________________________________________________________________________________
Epoch 1971 1973 – 74 1979 – 81 1980, 84 – 89 1996 – present
FOV (𝑅RsunR) 2.5 – 10 1.5 – 6 3 – 10 1.6 – 6 1.2 – 32
Total # CMEs 27 115 998 1351 > 10000
___________________________________________________________________________________
Speed (km sP
–1P) – 470 472 349 489
Acceleration (m sP
–2P) – – – – –16 to +5
Width (deg) _ 42 45 46 47
Mass (10P
15P) g – 6.2 4.1 3.3 1.3
2.5.1. UCME Morphology and Mass CMEs present many different shapes, and much of the variety is believed simply
due to the projection effects. However, fundamental difference can be found
between narrow CMEs and others. The narrow CMEs show jet-like motions
probably along open magnetic field, whereas normal CMEs are characterized by a
closed frontal loop. The typical morphology for normal CMEs is the so-called three-
part structure, i.e., a bright frontal loop, which is immediately followed by a dark
cavity with an embedded bright core. The bright core corresponds to the erupting
filament [48].
The three-part structure is considered to be the standard morphology for
CMEs, although observations indicate that only ~30% of CME events possess all
the three parts.
Typically, the mass of a CME falls in the range of 1 × 10P
11P – 4 × 10P
13P kg,
averaged at 3 × 10P
12P kg. About 15% of the CMEs have a mass less than 10 P
11P kg [29].
The determination of mass in the ‘‘CDAW SOHO LASCO Catalog’’ is based
on the assumption that the mass of the CME is localized in the plane of the sky and
that the integrated line of sight intensity is equal to the CME intensity at the point P
being measured. LASCO CME mass is estimated using measurements of the
brightness of the CME and the theory of Thomson scattering. Thomsonscattered
white light from the Sun is maximized when the observer-P vector is orthogonal to
Table (2.1) Average statistical properties from near-Earth space borne coronagraph observations of CMEs [1].
۳۲
the Sun-P vector (e.g., in the plane of the sky for a limb CME), and when this is not
the case only a component of the scattered light at P is observed.
This problem could be solved by e.g. Morphology and a linear structuring
element, or by correlation. Then we would need to handle rotation, zoom,
distortions etc. Hough transform can detect lines, circles and other structures if their
parametric equation is known. It can give robust detection under noise and partial
occlusion[49].
2.5.2. UAngular width The angular width of CMEs projected in the plane of the sky ranges widely from ~
2° to 360°, with a significant fraction in the low end (e.g., < 20°) and a small
fraction in the high end (e.g., > 120°). The CMEs with the angular width less than
~10° can be called narrow CMEs , and others are sometimes called normal CMEs.
Note that halo CMEs, with an apparent angular width of or close to 360°, are
because CMEs, probably with an angular width of tens of degrees, propagate near
the Sun-Earth line, either toward or away from the Earth [29].
2.5.3. UOccurrence rate During the solar cycle 23, the Large Angle and Spectrometric Coronagraph
(LASCO) on board the SOHO satellite provided unprecedented observations of
CMEs. The occurrence rate of CMEs was found to basically track the solar activity
cycle, but with a peak delay of 6- 12 months. Before the SOHO era, the averaged
occurrence rate was found to increase from 0.2 per day at solar minimum to 3.5 per
day at solar maximum. With the increased sensitivity and wider field of view, the
SOHO/LASCO coronagraph assembly, including C1, C2, and C3 components with
different fields of view, detected CMEs more frequently[29].
۳۳
2.5.4. UVelocity and energy Without special declaration, the CME velocity general means the radial propagation
speed of the top part of a CME frontal loop. However, it should be noted that this
velocity measures the motion of the CME frontal loop projected in the plane of the
sky, therefore, it can be called projected velocity. There are continuous attempts
trying to correct the propagation velocity for the projection effects. The CME
projected velocity ranges from ~ 20 km s P
–1P to > 2000 km s P
–1P, occasionally reaching
3500 km s P
–1P. The averaged velocity increases from 300 km s P
–1P near solar minimum
to 500 km s P
–1P near solar maximum[44]. It is found that the kinetic and potential
energies of a typical CME amount to 10P
22P – 10P
25P J, which is similar to that of solar
flares [29].
2.6. UCME identification and measurement Traditionally CME observations were obtained by visual inspection of coronagraph
images, and many of these “manual” catalogs of CMEs observed by the
P78/Solwind, SMM C/P ,and LASCO C2 and C3 coronagraphs are now on-line.
These catalogs have in recent times been augmented by additional on-line catalogs
of CMEs detected by automatic methods [1].
Figure (2.6) White-light images of two types of typical CMEs (from SOHO/LASCO database). (a) A narrow CME; (b) a normal CME [29].
۳٤
2.7. UObservational Features
According to the original definition, CMEs are an observable change in the coronal
structure that involves the appearance and outward motion of a new, discrete,
bright, white-light feature in the coronagraph field of view. Further observations
indicate that CMEs can also be observed in other wavelengths, such as soft X-rays ,
extreme ultra-violet , radio, and so on.
The white-light emission of the corona comes from the photospheric radiation
Thomson-scattered by free electrons in the corona, and any enhanced brightness
means that the coronal density somewhere along the line of sight is increased [29].
Figure (2.8): Daily SOHO LASCO CME rates for Cycle 23 [1]
Figure (2.7). Images of the same Earth-directed CME obtained from three different viewing locations within an hour: a) from STEREO/COR2-B, b) from
LASCO/C2, and c) from STEREO/COR2-A [1].
۳٥
2.8. UCME Detection Catalogues [50] Current methods of CME detection have their limitations, mostly since these diffuse
objects have been difficult to identify using traditional image processing techniques.
These difficulties arise from the varying nature of the CME morphology, the
scattering effects and non-linear intensity profile of the surrounding corona, the
presence of coronal streamers, and the addition of noise due to cosmic rays and solar
energetic particles (SEPs) that impact the coronagraph detectors. The images are
also prone to numerous instrumental effects and possible data dropouts. The
following standard preprocessing methods are usually applied to optimise the
images for CME studies. The coronagraph images are normalized with regard to
exposure time in order to correct for temporal variations in the image statistics. A
filter may be applied to remove pixel noise, for example to replace hot pixels with a
median value of the surrounding pixel intensities, or to reduce the effects of
background stars in the image. A correction for vignetting effects and/or lens
distortion may be applied to the images.
2.8.1.U CDAW In this work the results are compared mainly with this catalog. The CME catalog
(Figure 2-9) is hosted at the Coordinated Data Analysis Workshop (CDAW) Data
Center grew out of a necessity to record a simple but effective description and
analysis of each event observed by SOHO/LASCO. The catalogue is wholly manual
in its operation, with a user tracking the CME through C2 and C3 running-
difference images and producing a height-time plot of each event.
This catalogue is the most important reference for comparison with the results
of the present work because it catalog Manual.
2.8.2.U CACTus The Computer Aided CME Tracking catalogue was the first automated CME
detection algorithm, in operation since 2004. It is based upon the detection of CMEs
as bright ridges in time-height slices at each angle around a coronagraph image.
2.8.3. USEEDS The Solar Eruptive Event Detection System is an automated CME detection
algorithm for tracking an intensity thresholded CME front in running-difference
images from LASCO/C2.
۳٦
2.8.4. UARTEMIS The Automatic Recognition of Transient Events and Marseille Inventory from
Synoptic maps are automated CME detection algorithm that works by identifying
signatures of transients in synoptic maps.
2.9. UTheoretical CME Models [50] It is well known that CMEs are associated with filament eruptions and solar flares
but the driver mechanism remains elusive. Several theoretical models have been
developed in order to describe the forces responsible for CME initiation and
propagation, all of which are based on the idea that some form of instability must
trigger the eruption. These models may be explained in terms of the following
mechanical analogues. The Thermal Blast Model proposes that the increased
thermal pressure produced from are overcomes the magnetic field tension and blows
it open to cause a CME. Observations, however, have shown that not all CMEs are
preceded by are, nor even necessarily associated with a are at all. The Dynamo
Model introduces the idea of magnetic flux injection or stressing of the field on a
time-scale that is too fast for the system to dissipate the magnetic energy before it
builds to a critical point and erupts.
The Mass Loading Model is concerned with the amount of material included in the
eruption. Prominences or regions of relatively higher electron density in the corona,
overlaying a volume of lower density will erupt due to the Rayleigh-Taylor
instability. The Tether Release Model is based on the restraining of the outward
magnetic pressure by the magnetic tension of the overlying field. As `tethers' are
removed a loss-of-equilibrium occurs due to the magnetic pressure/tension
imbalance and the system erupts. The tether straining and release models are
generally accepted as the most Likely scenarios for CME initiation, being able to
reproduce numerous observations of CMEs through the development of detailed 2D
and 3D flux rope models, as discussed below.
۳۷
2.9.1. UCatastrophe Model The 2D flux rope model is driven by a catastrophic loss of mechanical equilibrium
as a result of foot point motions in the photosphere. The description of the model's
evolution in time may be split into a storage phase and an eruption phase.
2.9.2. UToroidal Instability An extension of the flux rope model to three-dimensions. The eruption of the flux
rope is triggered by an increase in the poloidal magnetic flux of the structure.
2.9.3. UBreakout Model In the magnetic breakout model the CME eruption is triggered by reconnection
between the overlying field and a neighbouring flux system through the shearing of
a multipolar topology.
2.10. UA Complete Description of CDAW CME Catalog [51] This catalog contains all CMEs manually identified since 1996 from the Large
Angle and Spectrometric Coronagraph (LASCO) on board the Solar and
Heliospheric Observatory (SOHO) mission. LASCO has three telescopes C1, C2,
and C3. However, only C2 and C3 data are used for uniformity because C1 was
disabled in June 1998. At the outset, we would like to point out that the list is
necessarily incomplete because of the nature of identification. In the absence of a
perfect automatic CME detector program, the manual identification is still the best
way to identify CMEs. This data base will serve as a reference to validate automatic
identification programs being developed.
The top-level of the catalog is a year-month matrix, each element giving the
monthly lists of CMEs. The monthly list contains most of the information assembled
from measurements and compilation from online data bases. Entries in this list have
links to additional information on CMEs. At the top of the monthly lists, simple
explanation is provided for getting information from additional layers. Link to the
list of data gaps during the month is also provided. Data gaps of duration 3 h or
more are listed. The data-gap list must be consulted before deciding the existence or
nonexistence of CMEs. If there is a data gap, it is difficult to say there was a CME
or not during the data gap.
Each row in the monthly list corresponds to one CME. The first three
columns of the monthly list serve as an ID for each CME: the date and time of first
۳۸
appearance in the LASCO/C2 field of view (FOV) and the Central Position Angle
(CPA). More than 10 CMEs can occur on a single day, and many CMEs can appear
at the same time in the C2 FOV. The CPA can essentially distinguish these CMEs
appearing simultaneously. CMEs an apparent width of 360 deg are marked as Halo
in the CPA column. Halo CMEs can be symmetric (S) or asymmetric with respect to
the occulting disk. Brightness Asymmetry (BA) and Outline Asymmetry (OA). The
halo CMEs are accordingly labeled as Halo (S), Halo (BA), and Halo (OA). Column
4 is the sky-plane width of CMEs, which is typically measured in the C2 FOV after
the width becomes stable (early on, the width often increases). Information as to
when the width was measured (#WDATA) is available in the text data containing
original measurements as a sub-layer of column 2.
Each CME is characterized by three speeds: (1) the linear speed obtained by
fitting a straight line (aka linear or first-order polynomial fit) to the height-time
measurements, (2) quadratic speed obtained by fitting a parabola (aka quadratic or
second-order polynomial fit) to the height-time measurements and evaluating the
speed at the time of final (last possible) height measurement, and (3) speed obtained
as in (2) but evaluated when the CME is at a height of 20 solar radii. Since the time
of final height measurement varies from event to event, the 20 solar radii speed is
useful for comparing different speeds. Caution must be exercised in dealing with
CMEs that fade away before reaching 20 solar radii. For some CMEs, which show
significant acceleration, the linear fit is not suitable. However, the linear speed
serves as an average speed within the LASCO FOV. Clicking on any of the speeds
displays the height-time plots with the fitted curves superposed. The actual height
time measurements are also in the text file underlying the first-appearance column.
It must be pointed out that the measurement is made at a single PA in 2-dimensional
images. This means there is more information in the original data than presented in
the catalog.
The acceleration of a CME can be positive, negative or close to zero meaning
CMEs speed up, move with constant speed or slow down within the LASCO FOV.
A minimum of three height-time measurements are needed for an estimate of the
acceleration, but the accuracy increases when there are more measurements.
۳۹
Accelerations with just three measurements are not reliable and are marked with a
superscript, *1.
Each CME is also characterized by a mass and a kinetic energy. There are
generally large uncertainties in these numbers. Estimation of CME mass involves a
number of assumptions, so the values given should be taken as representative. For
example, most CMEs show an increase in mass when they traverse the first several
solar radii, and then the mass reaches a quasi-constant value. This constant value is
taken as the representative mass. The mass estimates of halo CMEs are also very
uncertain. The kinetic energy is obtained from the linear speed and the
representative mass. Mass and kinetic energy values subject to such uncertainties
are superscript with *2.
The next column gives the position angle at which the height-time
measurements are made (MPA for Measurement Position Angle). Ideally, the MPA
and CPA must be the same. However, some CMEs move nonradially so the two do
not coincide. The last column of the monthly list contains some remarks regarding
the number of data points and other limitations, as well as links to the halo CME
alerts from the LASCO operator.
The regard is for the linear speed, width, CPA, and acceleration as the basic
attributes of a CME. The text file linked to the first appearance time contains the
actual height-time measurements, which may be useful for over plotting with other
data. The text file also contains the CME onset times obtained by extrapolating the
linear fit (#ONSET1) or quadratic fit (#ONSET2) to the solar surface (height = 1
solar radius). Note that these extrapolations are accurate only for limb events. For
disk events, the estimated onset is likely to be after the actual onset time. There is a
quality index listed in the text file for each CME, on a scale of 1-5, 1 being poor and
5 being excellent.
٤۰
Figure (2-9). CDAW SOHO LASCO CME catalog [51].
Figure (2-10). Description: SOHO LASCO CME catalog [51].
٤۱
2.11. UCME Near Real Time Libraries 0TBelow are the0T 0Tthree0T 0Tlibraries0T 0Tto get the0T 0Tdata from the 0T 0TSOHO0T. The library 0Tused0T in this
0Twork0T is the first one, shown in Figure (2-11 to 13).
Figure (2.11). A Screen shot of LASCO/SOHO library from (accessed on
20/11/2014): 1TUhttp://sohodata.nascom.nasa.gov/cgi-bin/data_queryU1T
Figure (2.12). A Screen shot of LASCO/SOHO library from (accessed on 20/11/2014): http://sohowww.nascom.nasa.gov/data/archive/
٤۲
Figure (2.13). CME Libraries (accessed on 20/11/2014):
http://solar.to.astro.it/search3.php
٤۳
Chapter Three
Methods of Analysis
3.1. UImage Processing
In the present work, few image processing techniques were used in the code.
The aim of this work is to write a code that detects any possible CME then
analyze its properties and measures the basic quantities, therefore, there is a
need to use image processing fundamentals in the main code. The present
chapter briefly introduces the subject of the techniques used in this work.
Image processing is a computer imaging where application involves a
human being in the visual loop. In other words the images are to be examined
and a acted upon by people. The major topics within the field of image
processing include [52]:
a) Image restoration.
b) Image enhancement.
c) Image compression.
In this work CME is to be detected from LASCO images, therefore, image
enhancement was used.
3.2. UImage Enhancement This process involves taking an image and improving its properties [52]. In the
present research the enhancement was based on transformation of image mode
so that the written code (cmeDetect) is to be able to detect the CME from
LASCO C3 images. The used methods are discussed in Chapter Four, where
their results are listed.
3.3. UImage Resolution The resolution has to do with ability to separate two adjacent pixels as being
separate, and then we can say that we can resolve the two. The concept of
resolution is closely tied to the concepts of spatial frequency. Spatial frequency
concept, frequency refers to how rapidly the signal is changing in space, and the
signal has two values for brightness-0 and maximum. If we use this signal for
٤٤
one line (row) of an image and then repeat the line down the entire image, we
get an image of vertical stripes. If we increase this frequency the strips get closer
and closer together, until they finally blend together [52].
3.4. UImage Representation
We have seen that the Human Visual System (HVS) receives an input image as
a collection of spatially distributed light energy; this form is called an optical
image. Optical images are the type we deal with every day –camera captures
them, monitors display them, and we see them [we know that these optical
images are represented as video information in the form of analog electrical
signals and have seen how these are sampled to generate the digital image I(r,c).
The digital image I(r,c) is represented as a two- dimensional array of data, where
each pixel value corresponds to the brightness of the image at the point (r, c). in
linear algebra terms , a two-dimensional array like our image model I(r,c) is
referred to as a matrix, and one row ( or column) is called a vector. The image
types we will consider are [52]:
a) Binary Image Binary images are the simplest type of images and can take on two values,
typically black and white, or ‘0’ and ‘1’. A binary image is referred to as a 1
bit/pixel image because it takes only 1 binary digit to represent each pixel.
These types of images are most frequently in computer vision application
where the only information required for the task are general shapes, or outlines
information. For example, to position a robotics gripper to grasp an object or in
Optical Character Recognition (OCR) [52].
b) Gray Scale Image Gray scale images are referred to as monochrome, or one-color image. They
contain brightness information only brightness information only, no color
information. The number of different brightness level available. The typical
image contains 8 bit/ pixel (data, which allows us to have (0- 255) different
brightness (gray) levels [52].
٤٥
c) Color Image Color image can be modeled as three band monochrome image data, where each
band of the data corresponds to a different color. The actual information stored
in the digital image data is brightness information in each spectral band. When
the image is displayed, the corresponding brightness information is displayed on
the screen by picture elements that emit light energy corresponding to that
particular color. Typical color images are represented as Red, Green ,and Blue
or RGB images .using the 8-bit monochrome standard as a model, the
corresponding color image would have 24 bit/pixel – 8 bit for each color bands
(Red, Green and Blue ) [52].
3.5. UEdge Detection Detecting edges is a basic operation in image processing. The edges of items in
an image hold much of the information in the image. The edges tell you where
items are, their size, shape, and something about their texture [52].
3.6. UImage Filtering Images are often corrupted by random variations in intensity, illumination, or
have poor contrast and can’t be used directly Filtering transform pixel intensity
values to reveal certain image characteristics [53].
i. Enhancement: Improve contrast
ii. Smoothing: remove noises
iii. Template matching: detects known patterns
3.7. UNoise FilteringU Basic Idea: replace each pixel intensity value with new value taken over
neighborhood of fixed size, the size of the filter controls degree of smoothing
the noise filtering types we will consider are [53]:
a) Mean filter
b) Median filter
c) Enhancement filter
3.8. UBasics of Spatial Filtering Some neighborhood operations work with the values of the image pixels in the
neighborhood and the corresponding values of a subimage that has the same
٤٦
dimensions as the neighborhood. The subimage is called a filter, mask, kernel,
template, or window, with the first three terms being the most prevalent
terminology. The values in a filter subimage are referred to as coefficients,
rather than pixels. The concept of filtering has its roots in the use of the Fourier
transform for signal processing in the so-called frequency domain [53].
3.8.1. USmoothing Spatial Filters Smoothing filters are used for blurring and for noise reduction. Blurring is used
in preprocessing steps, such as removal of small details from an image prior to
(large) object extraction, and bridging of small gaps in lines or curves. Noise
reduction can be accomplished by blurring with a linear filter and also by
nonlinear filtering [53].
3.8.2. USmoothing Linear Filters The output (response) of a smoothing, linear spatial filter is simply the average
of the pixels contained in the neighborhood of the filter mask. These filters
sometimes are called averaging filters.
The idea behind smoothing filters is straightforward. By replacing the
value of every pixel in an image by the average of the gray levels in the
neighborhood defined by the filter mask, this process results in an image with
reduced “sharp” transitions in gray levels. Because random noise typically
consists of sharp transitions in gray levels, the most obvious application of
smoothing is noise reduction. However, edges (which almost always are
desirable features of an image) also are characterized by sharp transitions in gray
levels, so averaging filters have the undesirable side effect that they blur edges.
Another application of this type of process includes the smoothing of false
contours that result from using an insufficient number of gray levels. A major
use of averaging filters is in the reduction of “irrelevant” detail in an image. By
“irrelevant” we mean pixel regions that are small with respect to the size of the
filter mask [53].
٤۷
3.9. UProcesses that are applied to the image to determine the edges To determine the edges of any image procedure several operations, namely [53]:
i. Smoothing: Smoothing is used for two purposes essential : first in order to give
a special effect to the components of the image The second order to get rid of
the confusion in the picture. Smoothing may occur during treatment for the loss
of some of the information and the picture here occur swap Between the loss of
information or to get rid of confusion [53].
ii. Differentiation:1T It is1T 1Tthe simplest1T 1Tof1T 1Tthe techniques used1T 1Tto detect1T 1Tedges1T 1Tand1T
1Tfind1T 1Tthe amount of1T 1Tvariation1T 1Tin the value of1T 1Tthe intensity1T 1TChromaticity1T 1Tof the1T
1Timage data1T 1Tand the direction of1T 1Tvariation1T 1Tas well1T. 1TThe1T 1Tvalue is calculated1T
1Tcovariance1T 1Tbetween the point1T 1Tat the site1T (1Ti, j) 1T 1Tand1T 1Tbetween1T 1Tneighboring1T 1Tpoints1T
1Tin both directions1T 1T( 1Thorizontal and 1Tvertical) 1T 1Tand1T 1Tas shown1T 1T in1T4T 1T4TEquations1T4T (1T4T3.11T4T)
1T4Tand (3. 2), respectively1T[48]:-
𝛁𝒙𝑮(𝒊, 𝒋) = 𝑮(𝒊, 𝒋) − 𝑮(𝒊 − 𝟏, 𝒋) ………….. (3.1)
𝛁𝒚𝑮(𝒊, 𝒋) = 𝑮(𝒊, 𝒋) − 𝑮(𝒊, 𝒋 − 𝟏) ………….. (3.2)
1TThe resulting value1T 1Tmay be 1T 1Tpositive or negative1T 1Tdepending1T 1Ton the
direction of1T 1Tcovariance 1T 1T( 1Tnegative 1Tdirection1T 1Tor1T 1Tpositive direction1T) 1Tcan make 1T 1Tthe
result of the1T 1Tpositive1T 1Talways 1T 1Tusing the1T 1Tabsolute value of1T 1Tto calculate1T 1Tthe value of1T
1Tthe variation1T 1Tin both1T 1Tdirections,1T 1Tand1T 1Tthe direction of1T 1Theterogeneities1T 1Tcan1T 1Tfind1T
1Ttheir expense1T 1Tand1T 1Tas 1T 1Tshown1T 1TIn 1T 1Tequations (0T1T3.3) and (0T3.4), respectively[53].
𝑮(𝒊, 𝒋) = �𝛁𝒙𝟐 + 𝛁𝒚𝟐 …………….(3.3)
𝛉 = 𝐭𝐚𝐧−𝟏 𝛁𝒚𝛁𝒙 … … … … … . (𝟑.𝟒)
iii. Thresholding: 1TAfter applying1T 1Tthe process of1T 1Tdiscrimination and1T 1Tshow1T 1Tthe
variation1T 1Tin the1T 1Tvalues and1T 1Tmarking1T 1Tpoints1T 1Tthat can be1T 1Tpart of the1T 1Tedge1T 1Tis then1T
1Tconnecting1T 1Tthese points1T 1Tto form a 1T 1Tborder 1T 1Tthat characterize1T 1Tcomponents1T 1Tand1T
1Tseparated from the 1T 1Tbackground1T 1Tand more 1T. (Threshold Value) 1Timage.1T 1TThis1T 1Tis 1T
1Tafter selecting 1T 1Ta specific value1T 1Tcalled1T 1Tthe threshold value1T 1Tactions1T 1Tcan be defined1T
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٤۸
1Tclassification1T 1Tinto two regions1T 1Trepresent the entity1T 1Tand background1T, 1Tand1T 1Ta 1T
1Tmathematical representation1T 1Tas follows1T[53].
𝑭(𝒊, 𝒋) = �𝟏 𝑰𝑭 𝑮(𝒊, 𝒋) ≥ 𝒕𝒉𝒓𝒆𝒔𝒉𝒐𝒍𝒅𝟎 𝒐𝒕𝒉𝒆𝒓𝒘𝒊𝒔𝒆
……….. (3.5)
1T Mostly1T 1Tthey are1T 1Tuseful in1T 1Tthe application1T 1Tto1T 1Ta specific image 1T, 1Tand1T 1Tcan be
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conditions1T [53].
3.10. UHough Transform The Hough Transform (HT) can be used to detect lines, circles or other
parametric curves if their parametric equation is known.. It was introduced in
1962 and first used to find lines in images a decade later. The goal is to find the
location of lines in images.
This problem could be solved by e.g. morphology and a linear
structuring element, or by correlation. Then we would need to handle rotation,
zoom, distortions etc.
In this work, Hough transformation was not used due to its complexity,
nevertheless this is an accurate method used in most CME automatic detection
codes. In Chapter Four the details of the present code will be described with
details[53].
٤۹
Chapter Four Results and Discussions
4.1. UGeneral Description of the Code cmeDetect This chapter lists the numerical results of the present work, originated from the
Matlab code cmeDetect. The following results are classified into two main
groups:
i. Initial results of the code. In these results the specifications and detailed
parts of the code cmeDetect are listed and discussed.
ii. Resultant properties of actual CME events including the CME height, speed,
acceleration, and mass. These represent the main results of this work, and
they are compared with standard CME catalog from CDAW [51].
The following describes the initial results that were used to explain
important details of the present computer code. In this work, a computer code
was written to analyze LASCO images and to find the results of detected CMEs.
The code written and used during this work is called cmeDetect, it was written
using Matlab program ver. 7.8 (R 2009 a). It uses many subroutines and makes
use of few powerful Matlab built-in commands such as cell data type, region
properties function (regionprops) and black-white boundary trace function
(bwtraceboundary), among other functions. See the next paragraph for details
of this code. Also Appendix A shows the flowchart of this code and Appendix
B contains a complete list of the code.
In general, this code uses colored images as its input, provided from the
library of SOHO/LASCO C3 coronagraph with resolution 512 x 512 pixels.
Then the program performs the process of image conversion to grayscale
utilizing an automatic thresholding procedure that is based on the information
obtained from the same image. This conversion with thresholding are performed
using the Matlab function (rgb2gray). This step is made in order to obtain start
(or confirmation) results, and perform preparation tasks. The conformation
results give indication about image quality and file arrangement, and the
٥۰
preparation task provides determining actual solar position and size. After that,
another conversion is made from grayscale to logic (black-white) scale using
(im2bw) Matlab function. These black-white images are the most important
format used by the present code, because the information to be determined are
extracted from them, each one at a given time.
Black-white images are converted first to grayscale images, i.e., this is
made instead of directly converting colored images into logical ones. The
reason is to grip and maintain as much as possible useful information from each
image, because in the present code the main technique was by isolating large
object areas in each image. The threshold value was computed from a global
level in each image. This implies conversion of the intensity of the image to a
binary image. The global level is assumed as a normalized intensity value that
lies within the range (0, 1). This was the only normalization made in this work.
It should be mentioned here that the present work is based on a code that
does not perform image normalization based on accumulation time (which is
calculated from the shutter speed of the camera of C3 coronagraph). This is
mainly because the images were collected from the SOHO/LASCO's website
image library in the form of (jpg) format, rather than from the raw images one.
The raw images from LASCO, which are usually provided in Flexible Image
Formats (FITS) file type, contains a wealth of information about the condition
of that image such as the sensor's temperature and exposure time (shutter
speed). Without such information it would be difficult to perform any
normalization for the selected images. However, the images from
SOHO/LASCO website image library do possess reasonable initial adjustment
as indicated in the main page, so that using these images for CME detection can
be safely considered. Nevertheless it would be better to use image normalization
in a future work. Considering the FITS file format is left for the future work.
Images taken from the SOHO/LASCO website image library were chosen
from LASCO C3 detector only with resolution 512 x 512 pixels. The choice of
٥۱
resolution was made in order to save runtime and memory consumption since
some CME events require a number of images that may reach up to 15-40
images. On the other hand, C3 detector images were chosen over C2 images
since the former covers a wider field of view. However, C2 images still hold
important information about the accurate first levels of CME development.
Therefore including C2 images at the first hours of any CME event will add a
reasonable accuracy to the calculations. This was not made in this research but
is left for future work.
Beside this, a reference image was chosen from the LASCO C3 library
(with the same size as mentioned above). This reference image was chosen such
that it contains no CME and the minimal possible background noise. Such a
reference image is to be used for two reasons in the main calculating code (and
see the full details below):
i. To detect the actual size of the Sun.
ii. To be considered as a reference for background initial elimination in other
images when a CME is detected.
In ideal cases, the CME occupies a definite region in LASCO image as a
bulk of white light with soft boundaries. In the details of the code it was aimed
to consider these definite regions of any image and assume them as a CME.
Earlier automatic CME detection codes such as CACTus [45] and others
used circular Hough transform to locate the CME boundaries and perform
calculations. In those automatic codes, the aim was to scan LASCO images for
any large area with curvature boundaries and start tracing these boundaries to
determine angle and height development. However, and due to its complexity,
the circular Hough transform was not used in the code of the present research.
Yet there is another method listed in the suggestions of future work that is
thought to provide an alternative method of detecting CMEs from C3 images.
Instead, in the present work the CME was determined from its main bulk
mass region as appeared in LASCO images and its properties were measured
٥۲
from the motion of its center of mass, as described below. Although the present
method may bring considerable error in few cases, it is a method that can be
programmed with relative ease. Error generates mainly due to the
inhomogeneous nature of CMEs. The main body of the CME may become more
dense than its boundaries because CMEs usually hover out of the Sun as a
plasma with large mass and turbulent density. Thus, CME main body usually
becomes more dense than its leading and following boundaries. Detecting the
CME based on its main bulk mass may lead to ignoring the leading boundary
edge and this may accumulate the error of calculations.
This problem was approximately corrected in the present code by detecting
the major axis length of the main bulk mass and adding this length to the center
of mass length of each detected CME. Error estimates range in CME heights
ranged between 2 to 30% as compared with the standard CME catalog [45].
More details about this solution are given below in point 2 of Section 4.2.D. that
describes measuring the height of the CME edge from the solar center.
Any detected bulk is assumed as a region of interest for the code (ROI).
Then ROI that represents the possible CME event is traced, and its various
properties are calculated. Calculations include height-time profile for each
CME, speed, acceleration, main position angle and mean area. In order to
perform these measurements from each image, few parameters must be found
first such as the time of each image and the solar size. Solar size is measured
first in pixels and the height-time profile is compared with the solar size which
was previously determined in the same code.
4.2. UComputing Details of the Program The present code was written using Matlab software and it contains many
subfunctions. The following is a brief description of the code's details.
4.2.A. UReading Filenames and DateU The images of a CME event are downloaded and saved into a computer folder
with a certain name. Images from LASCO/SOHO are found at the website:
٥۳
http://sohodata.nascom.nasa.gov/cgi-bin/data_query
Images are chosen from LASCO C3 coronagraph with resolution 512 x 512
only. All images from LASCO are in the rgb format. Furthermore, any image
from this site has a descriptive filename such that the filename of LASCO
image is :
LASCO Image Filenames Format: Date_Hour_Type_Resolution.jpg or
YYYYMMDD_hhmm_type_res.jpg
where Y stands for the year, M for the month, D for the day, h for the hour (UT)
and m for the minute.. etc. This means that the filename contains the year,
month and day. The hour contains the hour and minute. Type and Resolution
describe the detector type (C2 or C3) and the resolution of the image (512 or
1025, square image) respectively.
For example, an image with the name:
20130110_2042_c3_512.jpg
means that this image was taken at 2013-01-10, 20:42 UT, from C3 detector
with a resolution of 512x512.
Once the images of a CME event are downloaded, the present code reads
the filenames from their folder. The reading procedure reads the entire contents
of the folder but it selects the files with the extension (.jpg) or images only.
Then the code sorts these filenames such that the older image is sorted first.
After that the code extracts the date from the filename of each image. The
date of each image has very important rule in the calculations because it
represents the development of the detected CME event. These values are used in
a matrix to store them its date as: YYYY; MM; DD; hh; mm. Time is then
converted into minutes, which helps in finding the time difference between
successive images and compare the result with the dates of the images see
٥٤
directly in them. This helps later in specifying the time evolution of the detected
CME. during calculations this time is converted further into seconds.
4.2.B. UMeasuring the Solar Radius U Each image from LASCO/SOHO has the details shown before in Chapter (1) in
Figures(1.1) where there is a circle at the center of the image. Since used images
were with resolution (512 x 512), then it would be expected that the center of
the Sun lies at the center of the image, i.e. at pixel (256,256). However, this was
not the case. From careful inspection of any image from LASCO of the year
2002 using the Matlab image tool (imtool) it was found that the center of the
Sun lies exactly at x=247, y=260 pixels; with intrinsic (measuring) error of ± 1
pixels for both dimensions. See the colored image (4-1) below. In this figure,
the center of any image from LASCO is magnified by the ratio 1600%. The
white circle in this Figure exists originally in all LASCO images and this circle
indicates the actual position and circumference of the Sun relative to the image.
The green semi-circle is the detected boarder of the white one. This shape was
detected using the main program using the boundary trace function
(bwtraceboundary) as described below. The thin red circle is the perfect circle
plotted from the green semi-circle smoothed points. This time the radius of the
detected (green) circle is calculated from the previous step as well as the center
of the green circle (xc, yc), then data were regenerated using the equation of the
circle: (x+xc)P
2P+(y+yc)P
2P=r P
2P, where r is the radius. The red crossed lines are the
lines representing the center of the Sun, found at xc=247, yc=260 pixels. The
yellow crossed lines are the lines plotted exactly at the center of the image,
x=256, y=256 pixels. Only the white circle exists in LASCO images, while all
other (colored) shapes were plotted using the Matlab code.
The detection of the white circle shape was made by detecting any
continuous shape near the center of the image, i.e., around the pixel point
(256,256). After the program senses this shape it starts to trace the outer
boundaries of it. Here, the main program uses a function found in Matlab called
٥٥
the boundary trace function (bwtraceboundary). This function is able to
determine the geometrical properties of any continuously connected bulk shape
in a black-white image. In the case when this shape is a rough circle, this
function calculates the circle's center and radius. These information were not
directly used in the program, but they were used to perform a simple curve fit to
find the parameters of a smooth circle that is concentric with the original white
circle and with the same radius. The fitted circle is shown in Figure (4-1) as the
thin red circle laying at about the boarders of the original white circle.
The error (err) in determining the position (xc, yc) is obvious from the
difference between the yellow dot at the center and the point of the red-crossed
lines. That is ~ 1 pixel, and it exists due to the fact that points must occupy at
least a single pixel, and during the calculation the center (yellow dot) is at (xc,
yc) while the lines cross at (xc+err, yc+err). Although the error seen in this is
apparently less than 1 pixel, but it is assumed as (1 pixel) since this is the least
possible error value in determining a single point in an image.
The solar radius is measured from the reference image and it can be
assumed that its size does not change in other images-see the next paragraph.
The solar radius is an important parameter used during the calculations
performed by the code, since this code aims to detect CME different spatial
features based on their distances and sizes compared to the solar size. Thus, the
solar size is first detected in the code to find its value in pixels. Then, since the
solar diameter is previously well known, one can calculate the conversion
parameters from pixels to kilometers (or to meters, or even solar radii). After
this any measurement that is made by the code is easily converted into distance
units as required. This is shown in the example of Figure (4-2).
Regarding the pervious assumption about fixed measured solar radius,
however, it was found that selecting other reference images resulted in values
that were as large as 9.9707 pixels. Selecting few images for this procedure
showed that no radius values were less than 9.6700 pixels, and no values were
٥٦
higher than 9.9707 pixels. Therefore, the error assumed in this part is between
3.11 % to 3.02 %. The mean value is assumed as 9.8204 pixels, and it is the
value used in the rest of this work. This value comes with a round-off error
1.53% from both maximum and minimum detected solar radius values. This
error value is small enough to be neglected, therefore in practical applications
the previous assumption about fixed measured radius can be considered.
However, in this work, this error will not be neglected, and the mean value of
9.8204 pixels will be used to represent the solar radius with error ±1.53%.
Figure (4-1). The center of any image from LASCO, magnified 1600%. The white circle exists originally in all LASCO images and this represents the actual circumference of the Sun. The green semi-circle is the detected boarder of the white one from the main program. The thin red circle is the perfect circle plotted from the green semi-circle smoothed points. The red crossed lines are the lines representing the center of the Sun, found at x=247, y=260 pixels. The yellow crossed lines are the lines plotted exactly at the center of the image, x=256, y=256 pixels. Only the white circle exists in LASCO images, while all other (colored) shapes were plotted using the Matlab code.
٥۷
Figure (4-2-a). The image 20130114_0706_c3_512.jpg. This is assumed as a
reference image to detect the solar radius.
Figure (4-2-b). The estimated solar radius is 9.670 (in pixels) from the same
image 20130114_0706_c3_512.jpg. This value corresponds to the solar radius 6.958x10P
8P m. However, the value 9.8204 pixels is used-see below.
The solar measured radius from the reference image was 9.6700 pixels.
٥۸
Furthermore, the diameter of this circle was also roughly measured using
the Matlab's (imtool), as seen in Figure (4-2-c). The shown value of the
diameter was 18.91 pixels, indicating that the radius is 9.4550 pixels, which is
less than the minimum detected value from the code.
Since images are treated as matrices in Matlab, then it should be kept in
mind that the entire program uses integer pixel values. In fact any image
processing technique takes this as granted fact. This means that practically there
is no value of 9.8204 pixels in the image, but this value is assumed for length
conversion because it reflects the program ability to define lengths in meters
rather than in pixels. This assumption is considered in order to minimize the
error that associates with the program.
Figure (4-2-c). The visually-measured solar diameter using imtool of Matlab
indicated that it is 18.91 (in pixels) from the same image used in Figures (4-2-a and b).
٥۹
4.2.C. UFiltering the Images A CME is recognized as a distinguished, large and bright object leaving the
Sun. Thus in order to develop a computer program that is able to detect a CME
event it must be able to isolate any large and bright objects leaving the Sun. The
isolated object must be recognized from the background found in the image.
Therefore, careful isolation of the region of interest should be made by
removing any unneeded details in each image. This task is made in four steps in
the present program as described below:
First: Erasing of Descriptive Information Areas Each C3 image has descriptive information such as the location of the Sun and
the date of the image. The solar position is annotated by a circle at the center
and the date at the lower left side of each image. Both of these areas are
completely erased in the grayscale image of C3.
In some examples, after subtracting the selected image from reference
image (see Second step below), a small clusters of pixels remain in the regions
at the center of the image and at the lower left side. This is because dates
printed on each image vary and extraction from reference image may not clean
these regions completely. Therefore, these regions of each of the images are
erased. These regions are specified by the following rectangular regions:
The circle at center of the image by:
(248,230) , (280,230), (280,260) and (240,260).
The region of date at the lower left side of the images by: (1,480), (512,480), (512,512), (1,512).
Second: Images Main Cleaning A cleaning procedure is performed for all the images. In this step, each one of
the images is subtracted from a reference image as mentioned in the
aforementioned paragraph. This reference image is chosen previously for when
there are no significant CME events.
It should be mentioned here that in this work it was found that the current
method of choosing a reference image to subtract the selected images from is
٦۰
better than the method of running difference for selected regions around the
occulting disk, as described elsewhere [52]. The present method performs faster
and more accurately, and it assumes equal weight for the entire image since it is
assumed that the whole image represents an area of interest. The examples
illustrated in Figures (4-3) below show the results of cleaning procedure from
this work in Figure (4-3-a), compared to that from the procedure described in
ref [47] in Figure (4-3-b).
In order to achieve fast performance, the code was written such that it uses
an initial background subtraction from as clean as possible reference image
from LASCO C3. Any extra noise in the targeted image is compared with that
noise in the reference image, if there is a difference and if this difference was
large, the noise is assumed as a useful data and left out in the targeted image.
See more details below. However, if the noise existed and it was not large it is
then deleted from the targeted image before filtering the image. Such a process
guarantees fast removing of fixed points in the background (far stars) and eases
the removal of the occulting disk in the center of the image and date in the
bottom left side.
The example given in Figure (4-4-a) shows how an image is easily and
quickly cleaned up after subtracting it from a reference image. An important
thing to be careful about is that after this process, all pixels are logically tested
such that any pixel with a value not in the (0,1) category is rounded to the zero
level. By this it can be ensured that the resultant image will still has a logical
format consisting of (0,1) values only.
٦۱
Figure (4-3-a). The resultant image from the cleaning procedure used in the present work for the image 20130110_0054_c3_512.jpg. No filtering nor thresholding were performed yet, but the middle circle and date were erased.
Figure (4-3-b). The resultant from the treating the same image using run difference for selected areas around the occulting disk. No filtering nor thresholding were performed yet, but the middle circle and date were erased.
٦۲
Third: Spatial Filter Application
The resultant image is then filtered using spatial filter, then eroded to clean up
small isolated objects.
The filter used in the present code was an average filter. The resultant
image from the previous step is stored in a matrix K. This is first tested, pixel by
pixel, to detect any possible value of (-1). This worked if K(row,col)==1 and
tested as if they are (-1), if so the pixel value is reset to zero. The result is stored
in a vector, e.g., B1(imgNumber, row, col) of the binary image. This is the
vector which holds the pixels of difference for each image, considering the
entire image sequence. This matrix is made using a matrix concatenation. After
this the basic filter procedure is made. This is achieved by detecting where (1) is
present, then test if the following six pixels (up and down) are (0)'s. If so, then
the code replaces the (1) with a (0) in the matrix K, otherwise it aborts because
it may mean that this block of pixels is a large block which might be a CME.
After this the code uses STREL and IMERODE functions with specified
square shape, (2x2) pixels. The STERL function of Matlab creates a
morphological structuring element that is proper for image erosion. The selected
vector was chosen as a square one. This vector is made to put a small square of
zeros centered at the remaining pixels of value (1).
When experimenting few other procedure, it was seen that this method
works smoothly with the best final results. Although Matlab comes with a
verity of filters, the present method showed much better results than the pre-
programmed Matlab filters.
Fourth: Fast Normalization A simple normalization procedure was finally made for each image in the
sequence. The resultant image was ran through a thresholding procedure and
tested for isolated objects. This step is described before with enough details.
٦۳
Figure (4-4-a). The image 20021202_1818_c3_512.jpg after black-white
conversion only. No other treatment was applied to this image.
Figure (4-4-b). The same image 20021202_1818_c3_512.jpg after black-white
conversion and subtraction from the reference image. The middle circle and date were erased.
٦٤
Figure (4-5-a). One of LASCO C3 images of the event of early day 13-7-2012.
This is the 6P
thP image in the sequence.
Figure (4-5-b). The same image after filtering and preparation.
٦٥
4.2.D. UCME DetectionU This is the key aim of the cmeDetect code. Each one of the image files is now
assumed to be sorted in ascending manner, has its time stored, properly filtered
and clean of unnecessary details, and has a proper (normalized black-white)
format. Then each of these images are being input to the main procedure of the
code where the CME is to be detected.
Detecting CME from LASCO images is an operation that requires special
attention. For example, Vršnak et al. [Vršnak2009] discussed that white-light
emissions that appear in coronagraph images might have a blob-like shape with
speed range 10P
2P-10P
3P km/sec; yet they may not be always generated from CME
events. Thus the present code used a selection criterion from which the user can
determine what size of the feature is considered and which one is ignored.
As mentioned before, the detection is made from the isolation and
tracking of bulk mass areas in the image. In Figure (4-5-a) the event started in
the early day of 13-7-2012 is shown, and the image after processing and
preparation is shown in Figure (4-5-b). In this example it is seen how the
program focused on the only bright objects representing a possible CME event.
The detection of the present code is sensitive to few parameters. These
parameters are:
i. the size of the strel vector used in the erosion step.
ii. the horizontal and dimension of the detected object in any image.
iii. thresholding of the image.
Once each of these images is fed to the main procedure of the code, any
possible CME is detected. If any image contains such an object, the program
starts to follow its time development and measures simultaneously the following
parameters. The following parameters are measured from each image for the
entire filenames of a single CME event:
1. Time development of each image. Time is stored in minutes.
Difference time ∆t is calculated as
٦٦
∆t = tRi+1R – tRi
was also calculated and stored, where tRiR is the iP
thP time of each image obtained as
described before. The maximum value of i represents the number of CME event
images.
2. The height of the CME edge from the solar center. This value is
measured in pixels, then converted into kilometers using the calculated
conversion parameter. The same distance is also converted into units of solar
radii. These values are corrected by subtracting one solar radius value in order
to obtain the height from the solar surface rather than the solar center. The
height measured in the present code, H, is assumed as:
H = HRc.m.R + C/2
where HRc.m.R is the center of mass height of the detected object and C is the
length of the semi-major axis of the detected object. This equation represents an
important approximation in the present work. The value of HRc.m. Ralone,
apparently, contains a considerable error in height determination because the
wanted value must be from the outer (leading) edge of the detected CME to the
solar surface (or center). Therefore, the object semi-major axis, C, is also
measured in the main code for the detected object and half its value is added
with HRc.m. RSee the Figure (4-6).
٦۷
Figure (4-6). Any detected object in the LASCO C3 images is assumed to have semi-major and semi-minor axes, C and B respectively. The distance of the leading CME edge is approximated by adding C/2 to the center of mass height.
3. The direction of the line between center of mass of the detected
CME and solar center point is calculated. This angle is then transformed such
that it is being measured from the solar north and with direction of counter
clockwise. This transformation is made in order to obtain comparable results
with the CDAW catalog [51].
4. The area of each CME is measured. The value of this parameter is
measured with (pixelsP
2P) and then converted into (kilometersP
2P) using the same
conversion parameter mentioned before. The CME area will be used to find an
approximate relation with CME mass as found in CDAW.
After these steps, the main code directs the results into subfunctions to
calculate the following parameters:
٦۸
5. Speed. The speed is calculated from dividing the matrix containing
CME heights by the matrix containing time difference. The resultant speed
would also be stored in a matrix form. CME speed is calculated as km/sec.
6. Acceleration. The speed matrix goes through a difference procedure
by the time difference matrix and the result is stored into the acceleration
matrix. CME acceleration is calculated as km/secP
2P.
Next, the numerical results of the code are presented and discussed. Each
time there is a simple comparison with CDWA catalog. At the end of these
figures, a summery is presented in the form of a collective table.
4.3. UNumerical ResultsU Any CME event should be recognized from LASCO C3 images as a
distinguished, large and bright object leaving the Sun. The code written in this
work aims to detect such features and define then as CMEs then measures their
properties by direct comparison with solar size and coordinates. Measured
properties include the CME angle from the solar north, CME average area, time
of evolution and height. From differentiation of height with respect to time, the
speed of each detected CME is calculated, and from the speed and time
differences, the acceleration is calculated.
The first results listed below give examples about how the code
measurements are performed for of three event examples, each of which is
assumed to be with a single CME. These measurements include distance of the
detected CME, its area, and its angular direction. The rest of the parameters are
program-based.
The CME height (or distance from the surface of the Sun) in the following
calculation examples, as well as in all calculations performed in this work, are
the most important parameters of detection. It is as the distance found from the
outer detected boundaries of the object to the surface of the Sun. The initial
calculation of the cmeDetect code actually finds the distance to the center of the
Sun, but in another step the solar radius is subtracted from the measured value.
٦۹
This is made in order to be able to compare with CME libraries where most of
them use the distance from the outer boundaries of the detected CME to the
solar surface.
Each time a CME is detected, its area is also found from the main program.
The final area listed in the results represent the mean average of the
accumulated values of the detected areas during the run of the program. These
parameters can be used in an approximate calculation of the CMEs mass.
In Figures (4-7-a and b) the initial test is presented for CME detection for
images of an example CME. This event started at 02/12/2002 at time 17:16. The
image in Figure (4-7-a) is the third one of the sequence at the time 19:42:00,
and the same image is given in Figure (4-7-b) after it went through the entire
code and analyzed. The yellow lines in this part of the figure intersect at the
middle of the detected center of the sun. The values shown above are the initial
values of direction (mean position angle), CME distance from the solar surface,
and the detected area of the bulk mass of this image. Other examples have been
taken in Figures (4-8-a and b) and (4-9-a and b) for different events. In each set
of figures, the treated image is shown in part (a) and its original LASCO image
is given in part (b).
۷۰
Figure (4-7-a). Initial test for CME detection for images of the CME event starting at 02/12/2002 at time 17:16. The image time's 19:42:00. The yellow lines intersect at the middle of the detected center of the Sun. Shown above the initial values of direction (mean position angle), CME distance from the solar surface, and the detected area of the bulk mass of this image.
Figure (4-7-b).The original LASCO/SOHO image of the CME event
analyzed in Figure (4-7-a). This image time was 09:42:00.
۷۱
In Figure (4-8-a) the detection threshold of the present code is illustrated to
some extent. In the original LASCO image, Figure (4-8-b), the CME that is
needed to be detected and recognized by the code almost has faint density at the
outer leading edge, yet the code could reasonably detect it; although the
detection efficiency is not perfect. This is because the outer edge which
represents the most important part of the CME is about 3 to 7% less than the
actual one. As the results will reveal later, this shortage of efficiency will cause
most CME height values to be, in general, with similar behavior to the reference
values of CDAW, yet with higher values than the correspondent CDAW catalog
taken from ref [51]. Some of the present values, however, are in perfect
agreement.
The height values are used by the code in order to calculate CME speed
and acceleration thus these error values in the original CME height will cast an
error on the related calculated values. This error becomes less as the LASCO
images contain more distinct, separated CME eruptions as in the example of
Figure (4-9). The measurements of the mean position angle, on the other hand,
were consistent with acceptable error values in almost all results as seen later in
this chapter.
۷۲
Figure (4-8-a). Initial test for CME detection for images taken at
10/01/2013 at time 03:30.
Figure (4-8-b). The original LASCO/SOHO image of the CME event of
Figure (4-8-a).
۷۳
Figure (4-9-a). Initial test for CME detection for images starting at 01/03/2012
at time 00:06. This is the 9P
thP image at time 3:54.
Figure (4-9-b). The original LASCO/SOHO image of the Figure (4-9-a).
۷٤
In the rest of the selected CME events examples listed below, the results
are presented without referring to the original CME images. The Figures above
show the results for selected CME events in the years 2002, 2012 and 2013. The
present results are shown in parts (a, b and c). In each set of figures the
following parts is shown:
Part (a) shows the results of CME height of the event,
Part (b) shows the speed, and,
Part (c) shows the acceleration.
Reference results of the same CME events are given in Part (d), which
shows both CME height and its first derivative obtained from CDAW catalog
[45]. Furthermore, in few examples there is another plot shown in Part (e)
where the curve fit of CME height is plotted in order to perform a detailed
comparison with standard CDAW data. This case is made after performing
curve fitting of the present data-see below. Not all the currently studied CME
events' heights were treated with the curve fitting procedure, therefore part (e)
of few figures is presented and separated from part (a) which shows current
heights results without any further illustrations.
The CME events listed in Table (4.1) were selected as examples of input
for the code used in this work.
۷٥
Table (4.1). The CME event examples used with the present code.
# Date TimeP
(1) Figures P
(2) 1. 01/01/2000 0TU14:30:05U0T 2. 02/01/2000
0TU05:30:05U0T 3. 18/01/2000
0TU11:54:05U0T 4. 01/12/2002 0TU10:34:05U0T 5. 02/12/2002 0TU17:54:05U0T 4-10 6. 04/12/2002 0TU03:26:05U0T 4-11 7. 06/12/2002 0TU13:31:47U0T 8. 13/12/2002
0TU02:54:07U0T 9. 19/12/2002
0TU22:06:05U0T 10. 28/12/2002
0TU13:54:05U0T 4-12 11. 28/12/2002 0TU16:30:06U0T 4-13 12. 01/01/2003 0TU19:54:05U0T 4-14 13. 01/03/2003 0TU13:54:24U0T 4-15 14. 18/03/2003 0TU07:31:43U0T 4-16 15. 01/08/2003
0TU12:54:05U0T 16. 28/11/2003
0TU10:06:05U0T 17. 24/01/2007 0TU14:23:51U0T 4-17 18. 01/05/2013 0TU03:12:08U0T 19. 02/05/2013 0TU05:48:06U0T 4-18 20. 17/05/2013 0TU09:12:10U0T 4-19
(1) This time appears with the data file from CDAW. However, the same CDAW figures contain the start time of any CME according to their first appearance in C2 images. In this work the used files were C3, yet the date that appeared in the images were taken in below. (2) Blank items are CME event examples that were analyzed but not plotted. Their numerical results are given in Table (4.2) at the end of the present Chapter.
Selection of CME events was not based on any certain criterion, rather,
events with definite and considerable size with as possible distinction were
searched for from CDAW catalog and the corresponding image data were
retrieved from SOHO/LASCO website then examined with cmeDetect code.
Table (4.1) also shows the related figures of the numerical results. Numeric
values of the present results and those of CDAW are all listed in Table (4-2) at
the end of this Chapter.
There are 10 more events that were analyzed in the code but their results
were not plotted. This is a two-fold choice, first because most of these extra
۷٦
events came with high error values for speed and acceleration, and specially for
height. This researcher saw to point them out anyway for future developments.
The other reason is to save space of the present research.
4.3.1. UThe CME event of 02/12/2002 at time 17:16:00 The results are shown in the Figures (4-10-a to d). In Figure (4-10-a), the CME
height results showed that this quantity develops almost linearly with time. The
same behavior is shown in the rest of all of the examples used in this work.
Such behavior is explained to be due to the nature of the CME itself, where it is
identified as a mass of plasma leaving the Sun. However, the height
development is not the same for every CME event. After about 5 hours of the
initial CME detection a sudden increase took place in the height values where
the CME jumped from 13RRsunR to 20RRsunR in about an hour. Associated is a
sudden jump in the results of the Figure (4-10-b) for speed measures. This
implies that there was a sudden acceleration at these areas which is indeed
shown in Figure (4-10-c). However, the speed and acceleration results in this
case, and most cases studied in this work, were fluctuating around a mean value.
In order to calculate the CME speed, there are two suggested methods:
(1) by taking the mean average of the results of dividing CME height by
time difference, which is shown in Figure (4-10-b) and in all (b) parts of the
results, and
(2) to perform a linear least-square curve fitting to the CME height data
obtained from the present code, and deduce the CME speed as the slope of the
resultant fit equation.
The curve fitting procedure was added in order to determine the speed of
the CME more reasonably than just taking the mean average of the CME height
development with time which is shown in Figure (4-10-b).
The values of the calculated CME height and their linear fit were also
plotted in the same figure with standard height data obtained from CDAW
catalog in Figure (4-10-e). The goodness of the fit was acceptable compared
۷۷
with the present data, however, these data showed only behavior agreement
with CDAW data. The error values, although reach considerable values up to
60% of height data, was expected because the present code uses few major
approximations as discussed above, therefore adding more corrections is
necessary for any future development of the present attempt. Nevertheless, there
is a general agreement between the present results and those of CDAW catalog.
Speed measurement from the mean average values of Figure (4-10-b) was
274 km/sec and from CDAW was 867 km/sec; therefore this speed comes with
a very high percentage error value of about 68 %. The CME speed from the
slope of the fit was a*RRsunR/60000 =~ 338 km/sec. The percentage of error this
time was better to reach about 61%, yet it still a value with considerable error.
Furthermore, the maximum value of speed from present calculations was 980
km/sec shown in Figure (4-10-b), and this particular value has an error of ~
13%.
Figure (4-10-a).The present results of CME height of the event 02/12/2002 at
time 17:16:00.
۷۸
Figure (4-10-b).The present results of CME speed of the event 02/12/2002 at
time 17:16:00.
Figure (4-10-c).The present results of CME acceleration of the event
02/12/2002 at time 17:16:00.
۷۹
Figure (4-10-d). Height and its derivative results of the event 02/12/2002 at time
17:16:00 from CDAW [51].
Figure (4-10-e). Height and its derivative results of the event with onset 02/12/2002 at time 17:16:00 from present and CDAW data. Blue circles
illustrate present data, red diamonds is their linear fit, and black squares are the CDAW data [45].
۸۰
4.3.2. UThe CME event of 04/12/2002 at time 01:33:00
The results are shown in the Figures (4-11-a to d). The figures are arranged as
before. In Figure (4-11-a), it can be seen that after about 3 hours the CME
height raised suddenly from 7RRsunR to about 9 RRsunR. CME height linearity this
time is less dependent on time specially at high altitudes where from the present
results it is shown that speed decreased at about this point of time as shown in
Figure (4-11-b).
Another remark is that the height of CME resulted in decreased values at
about the end of the calculations, which obviously declares that there is an
inconsistent results. This behavior is due to the nature of the present code
which, as described before, depends on determining the bulk mass of the CME
as a detection method.
In this case it can be noticed that CME height developped almost linearly
with time till time ~ 150 minutes then almost constant value was reached at time
between 150 to 250 minutes, followed by a sudden acceleration that took place
till time of about 300 minutes. This remark, combined with the results of the
date 02-12-2002 given above, indicates that the region that lays at about 5 to 15
RRsunR acts as an acceleration region for CME. This effect was suggested earlier
[20] where the reason was suspected to be either due to solar flare associated
with CME eruptions or with interplanetary medium. AlSawad [20] mentioned
that "if the bulk of acceleration takes place in interplanetary medium, then the
interplanetary shock driven ahead of the CME is the major accelerator and
hence the flare part in acceleration will be a minor or even not at all an
accelerator, according to some studies."
The present height values were, in general, much higher than
correspondent CDAW values till about time ~ 300 minutes, then dropped below
that value as seen in Figure (4-11-e). From this figure the speed value was
calculated as 262 km/sec while CDAW value was 301 km/sec, giving an error
value to present result of about 13%. This was by no means better than the mean
۸۱
average value of CME speed taken from Figure (4-11-b) which was 140 km/sec
and error 53%.
Figure (4-11-a).The present results of CME height of the event 04/12/2002 at
time 1:33:00.
Figure (4-11-b).The present results of CME speed of the event 04/12/2002 at
time 1:33:00.
۸۲
Figure (4-11-c).The present results of CME acceleration of the event
04/12/2002 at time 1:33:00.
Figure (4-11-d). Height and its derivative results of the event 04/12/2002 at time
1:33:00 from CDAW [51].
۸۳
Acceleration results also indicated this generally, as shown in Figure (4-
11-c). The mean average value of calculated acceleration was about 2.1 m/secP
2P,
and that from CDAW was 2.65 m/secP
2P; thus the present calculation was in
general agreement with the standard catalog value within an error about 20%.
The mean position angle measured in this example was 317 degrees and
that of the CDAW catalog was 339 degrees, which means that the error in the
present calculations was about 6% only.
Figure (4-11-e). Height and its derivative results of the event 04/12/2002 at time
1:33:00 compared with CDAW results.
4.3.3. UThe CME event of 28/12/2002 at time 12:31:00 The third CME example is the event of mid-day 28-12-2002. This is a good
example that can be used to demonstrate the difference between cmeDetect code
and CDAW catalog since the results were in remarkable similar behavior yet
with different numeric values. Figure (4-12-a) shows the CME height with time,
and in similar behavior yet with different numeric values. the effect seen above
in the region 10-15RRsunR was not seen. The linearity of this figure is clear for
۸٤
almost all values. The angular direction was at 315 degrees with error of less
than 1%.
Before the consistency of present results is discussed in this case, it
should be mentioned that the fluctuating behavior of speed and acceleration
curve takes place in particular CME cases when the same images contain more
than one detectable event. This reveals the high instability of the procedure used
in this code, that is to estimate speed by determining the height derivative with
time directly. Thus it is left as a future work to develop a built-in function that
directly computes the appropriate values of speed and acceleration.
Speed and acceleration shown in Figures (4-12-b and c) also did not show
any significant acceleration behavior at the time of these heights. Speed results
in special showed a general increasing behavior yet interrupted with sudden
drops, and this might be due to the effect of coronal part of the solar magnetic
field which holds in a resistive way the development of the CME plasma.
Figure (4-12-a).The present results of CME height of the event 28/12/2002 at
time 1۲:۳۱:00.
۸٥
Figure (4-12-b).The present results of CME speed of the event 28/12/2002 at
time 12:31:00.
Figure (4-12-c).The present results of CME acceleration of the event
28/12/2002 at time 1۲:۳۱:00.
۸٦
Figure (4-12-d). Height and its derivative results of the event 28/12/2002 at time
12:31:00 from CDAW [51].
Figure (4-12-d) shows that CME speed was decreasing. This phenomenon
will be discussed briefly in the results of CME event at 01/03/2003 at time
12:21:00 (paragraph 4.3.6). The mean average of the calculated acceleration in
this work was ( -2.5) m/secP
2P, with about 12% error from acceleration found in
CDAW which has the value of (-2.84) m/sec P
2P. The results of height are also
compared with a figure plot at Figure (4-12-e) in this example. The figure
clearly shows about the exact behavior with almost fixed difference of about
1.5RRsunR. Speed calculation in this example was in a good agreement with
CDAW value, it was 354 km/sec from the average of Figure (4-12-b), and 432
km/sec from the curve fitting; with associated percentage of error 11% and 8%
from the CDAW value 399 km/sec.
۸۷
Figure (4-12-e). Height and its derivative results of the event 28/12/2002 at time
12:31:00 compared with CDAW results.
4.3.4. UThe CME event of 28/12/2002 at time 16:26:00 This event followed the previous one in about the same angular direction within
a time interval of about 4 hours. The angular direction of this event was 321
from CDAW catalog and 315 degrees from the present calculations, with an
error ~ 2%.
Although these successive events were appearing in few images, the
present code could successfully distinguish between them and the results were
acceptable in general. Figure (4-13-a) shows how the CME height behaved
similarly to that of the previous results, Figure (4-12-a). This second event
lasted longer than the first one and was much larger, where CDAW results of
mass calculations of the first event was 1.7x10 P
15P gm while the second was with
mass about 9.9 x10P
15P gm.
۸۸
The mean velocity of the present calculation came with about 66%,
angle with 2% and acceleration with about 29% percentage error. CME height
is seen to increase linearly with time for the period t=180 to t=425 minutes.
Comparison between Figures (4-13-a) and (4-13-d) shows that the present
height results were less than those of CDAW. From Figure (4-13-d) the velocity
is seen to behave with non-linear relation with time which reveals that
acceleration must have some inconsistency values from the CDAW data. The
acceleration recorded by this catalog of this event was very high comparing to
most other CME events.
Figure (4-13-a). The present results of CME height of the event 28/12/2002 at time
16:26:00.
۸۹
Figure (4-13-b).The present results of CME speed of the event 28/12/2002 at
time 16:26:00.
Figure (4-13-c).The present results of CME acceleration of the event
28/12/2002 at time 16:26:00.
۹۰
Figure (4-13-d). Height and its derivative results of the event 28/12/2002 at time
16:26:00 from CDAW [51].
4.3.5. UThe CME event of 01/01/2003 at time 17:13:00 This is the event that took place on the 2003 new year eve. Resutls were in
general agreement with CDAW results. However, few remarks must be
mentioned. The last two points of Figure (4-14-a) represent an error since they
indicate that CME height was decreasing, i.e., the CME is approaching the Sun.
This is considered as a fault in the code because if the CME returned to the Sun
it will not represent an ejection, but a flare. The speed and acceleration results
of Figure (4-14-b and c) were also fluctuating about respectively the vaules 190
km/sec and 2.6 m/secP
2P. Correspondent CDAW results were 281 km/sec and 0.8
m/sec P
2P, respectively.
۹۱
Figure (4-14-a).The present results of CME height of the event 01/01/2003 at
time 17:13:00.
Figure (4-14-b).The present results of CME speed of the event 01/01/2003 at
time 17:13:00.
۹۲
Figure (4-14-c).The present results of CME acceleration of the event 01/01/2003 at time 17:13:00.
Figure (4-14-d). Height and its derivative results of the event 01/01/2003 at time
17:13:00 from CDAW [51].
۹۳
4.3.6. UThe CME event of 01/03/2003 at time 12:21:00 This event gave better results of CME heights compared with the previous one.
Figures (4-15-a to c) give the CME height, speed and acceleration. Calculated
speed was with average 159 km/sec and that of CDAW was 357 km/sec. A
similar error is found in the acceleration. Angular direction was 73 degrees,
with error 6% from CDAW value.
Figure (4-15-a).The results of CME height of the event 01/03/2003 at time
12:21:00.
Figure (4-15-b).The results of CME speed of the event 01/03/2003 at time
12:21:00.
۹٤
Figure (4-15-c).The results of CME acceleration of the event 01/03/2003 at time
12:21:00.
From comparing Figure (4-15-b) with the CDAW result of speed
distribution given in the lower part of Figure (4-15-d) one can see a similarity of
behavior if the first three points of the present calculations were ignored. This
decreasing behavior of speed with time indicates that there is an acting force
that constrain the CME development toward the interplanetary space. This
equilibrium-like development was mentioned [54] and attributed to the nature of
the solar magnetic field in the region behind the initial plasma ejections. If the
magnetic field was reconnected between the progressive plasma, a
disconnection of the magnetic field may occur leading to a CME. The resultant
CME may sustain sufficient kinetic energy and its speed will be increasing with
time raising a positive acceleration. If the ejection was separated from the solar
corona yet it did not sustain enough kinetic energy, negative acceleration will be
resulted and the CME speed will decrease as the CME gains height. Usually the
first case is associated with halo CMEs and comes with a twisted magnetic
field. A similar behavior was seen in other examples in this work, as in the
۹٥
event of 28/12/2002 at time 12:31:00 seen in Figure (4-12-d) and in the
forthcoming Figure (4-17-d) of the event of 24/01/2007 at time 13:41:00.
Figure (4-15-d). Height and its derivative results of the event 01/03/2003 at time
12:21:00 from CDAW [51].
۹٦
4.3.7. UThe CME event of 18/03/2003 at time 06:52:00 In Figures (4-16-a to c) the results of this event are shown. The error of speed
measurements was about 48% and of acceleration was as high as 69%.
Figure (4-16-a).The results of CME height of the event 18/03/2003 at time
06:52:00.
Figure (4-16-b).The results of CME speed of the event 18/03/2003 at time
06:52:00.
۹۷
Figure (4-16-c).The results of CME acceleration of the event 18/03/2003 at time
06:52:00.
Figure (4-16-d). Height and its derivative results of the event 18/03/2003 at time
06:52:00 from CDAW [51].
۹۸
4.3.8. UThe CME event of 24/01/2007 at time 13:41:00 This example is chosen in the present work in order to perform a comparison
with an example mentioned by Webb et al. [55]. In Figures (4-17-a to d) the
results of this event are shown, and in Figure (4-17-e) the comparison of heights
with their linear least square fit is given. The height results were in a good
agreement with CDAW results with almost negligible error.
Webb et al. mentioned that speed minimum value was 580 km/sec and
maximum given by CDAW was 785 km/sec, thus it showed a considerable
variance. Although present calculations of CME height was consistent with
standard value, the present value of speed came up with an error ~ 40%, and
about similar value of error for acceleration, about 36%. Angle error from
present calculation was as low as 2% only.
Figure (4-17-a).The results of CME height of the event 24/01/2007 at time
13:41:00.
۹۹
Figure (4-17-b).The results of CME speed of the event 24/01/2007 at time
13:41:00.
Figure (4-17-c).The results of CME acceleration of the event 24/01/2007 at time
13:41:00.
۱۰۰
Figure (4-17-d). Height and its derivative results of the event 24/01/2007 at time
13:41:00 from CDAW [51].
Figure (4-17-e). Height and its derivative results of the event with onset
24/01/2007 at time 14:41:00 from present and CDAW data.
۱۰۱
4.3.9. UThe CME event of 02/05/2013 at time 03:47:00 This event and the next one were chosen from May 2013. A similar behavior of
the results is seen in both cases when comparing the present results and
correspondent result of CDAW.
Figure (4-18-a).The present results of CME height of the event 02/5/2013 at
time 03:47:00.
Figure (4-18-b).The present results of CME speed of the event 02/05/2013 at
time 03:47:00.
۱۰۲
Figure (4-18-c).The present results of CME acceleration of the event 02/5/2013
at time 03:47:00.
Figure (4-18-d). Height and its derivative results of the event 02/05/2013 at time
03:47:00 from CDAW [51].
۱۰۳
4.3.10. UThe CME event of 17/05/2013 at time 08:37:00
Figure (4-19-a).The present results of CME height of the event 17/05/2013 at
time 08:37:00.
Figure (4-19-b).The present results of CME speed of the event 17/05/2013 at time
08:37:00.
۱۰٤
Figure (4-19-c).The results of CME acceleration of the event 17/05/2013 at time
08:37:00.
Figure (4-19-d). Height and its derivative results of the event 17/05/2013 at time
08:37:00 from CDAW [51].
۱۰٥
۱۰٦
4.4. UCME MassU
4.4.1. UMass-Area Calibration
In Chapter Two the standard method of CME mass measurement was briefly
reviewed. The present paragraph discusses the results of the attempt made in
this work in order to determine the CME mass when they erupt with halo
morphology. In CDAW catalog [51], it was mentioned that the mass
determination was approximate and comes with high uncertainties; specially
when the CME was halo. The technique described was based on the work of
Zhuravlev et al. [56] and Howard et al. [49].
In this work the attempt was to try connecting the detected CME area
with its mass to find a simple form of calibration relation. This is based on two
key assumptions:
i. The CME mass depend on their volume, thus on their surface area.
ii. The mass density (or surface density) of any CME is constant with speed.
Therefore, if one accepts the above crucial assumptions, then the average
CME area the first stages of development suffices of to give a rough estimation
about the total CME mass.
In order to perform this task, the area was calculated for each of the
CMEs of the present results, then the first few values were averaged. The limit
of averaging CME area was roughly chosen to be less than 15RRsunR. Then, the
mass data for the correspondent CME events included in CDAW catalog were
collected. Combined together, mass and area values were ran through a least
square fitting procedure using the Matlab cftool. In this work, two types of
fitting for this procedure were considered:
i. The linear least square fit. That is to assume that: M = pR1R A + pR2R, with pR1R and
pR2 R being constants and A is the average CME area. The fitted relation is M =
403.2 A + 1.129x10P
15P. Fitting goodness R-square = 0.8274. This relation gives
the mass in grams if the area was in kmP
2P. This relation holds when the mass
linearly depend on the surface area, which is an approximation for plasma cases
۱۰۷
as mentioned above. The value of the constant pR2R should, based on the above
assumption, be equal to zero. Instead, its value was close to the value of pR1R.
ii. The non-linear least square fit. In this case the fitting relation was assumed to
be as: M = pR1R L + pR2R and L = AP
0.5P. In this case the assumption was that the
CME linear density is constant. On performance of the curve fitting routine,
then the relation M = 2.286x10P
9P L – 1.135x10P
15P. Fitting goodness R-square =
0.933. Although the statistics of this fit are better than the first one, but the
value of the parameter pR2R in this case is relatively high with negative sign.
In Figures (4-20-a) the first (linear) relation is shown, and the second
(non-linear) in Figure (4-20-b).
Figure (4-20-a). The resultant linear curve fitting of CME mass against average area. The fitted relation is M = 403.2 A + 1.129x10 P
15P. Fitting goodness R-square
= 0.8274.
۱۰۸
Figure (4-20-b). The resultant non-linear curve fitting of CME mass against average area. M = 2.286x10P
9P L – 1.135x10P
15P. Fitting goodness R-square =
0.933. 4.4.2. UHalo CME MassU After the mass-area calibration relation was found, the goal is to find the mass
of halo CMEs that were not reported by CDAW. In this work five halo CMEs
were considered – See Table (4.2) above. In Table (4.3) these values are listed
and their masses calculated from the above calibration attempt are also given.
For each mass estimation, the correspondent kinetic energy (KE) is also
calculated from speed values of the present code.
۱۰۹
Table (4.3). The calculated masses of five halo CMEs from the mass-area calibration relations.
Date Time Area (kmP
2P)
Mass from linear fit
(g)
KE (erg)
Mass from non-linear
fit (g)
KE (erg)
02/12/2002 17:54 5,07x10 P
12 3.17x10P
15 9.37x10P
17 4.012 x10P
15 1.18x10P
18 19/12/2002 22:06 7,90x10 P
12 4.31x10P
15 1.28x10P
18 5.273x10P
15 1.56x10P
18 24/01/2007 14:20 3,90x10 P
13 1.68x10P
16 7.93x10P
17 1.31x10P
16 6.18x10P
18 01/05/2013 03:12 4,42 x10 P
11 1.30x10P
15 8.39x10P
18 3.84x10P
14 2.45x10P
17 17/05/2013 09:12 3,86 x10 P
12 2.686x10P
15 1.881x10P
18 3.356x10P
15 2.34x10P
18
The kinetic energy values in the above table were found from the average
speed values of Table (4.2), using the relation: KE=1/2 m v P
2P . In general it is
noticed that the kinetic energy from the non-linear fit were higher than those
from linear fit. It should be mentioned that the values found in Table (4.3) are
approximate values based on the two key assumptions mentioned above. An
accurate estimation of the mass-area calibration should consider the properties
of the solar plasma, and this task requires utilizing the magnetic field inclusion
in these calculations. Since LASCO images can provide only few information
about these specifications, one should consider other detectors such as EIT or
even other sun observation missions. Thus it is suggested that in order to follow
the present method of development of the kinetic energy, an accurate estimation
of the CME plasma properties must be included. These properties are: particle
density, magnetic field strength and direction, thermal energy and pressure;
beside velocity rather than speed.
۱۱۰
Chapter Five
Conclusions and Future Work
5.1. UConclusions
The present work attempted to detect and analyze CMEs from automatic
analysis of SOHO's LASCO/C3 images with resolution 512x512. The used
code, cmeDetect, was written in Matlab and it is based on the technique of bulk
detection of the CME after few pre-processing procedures. Beside its relative
ease of programming, the present code also demanded a realistic short runtime
which makes it an ideal choice for fast initial analysis of LASCO images.
However, this gain came up with a shorthand in results' accuracy.
The most important conclusions made from the results of the present work
are summarized as follows:
1- The present cmeDetect code was able to CME detect and recognize
well-defined CMEs. If they were with faint density, CMEs at the outer leading
edge could not be reasonably detected; or the detection efficiency was not
perfect. This was explained because the outer edge which represents the most
important part of the CME is about 3 to 7% less than the actual one. The results
revealed that this shortage of efficiency will cause most CME height values to
behave with similar behavior to the reference values of CDAW, yet with
generally higher values. Some of the present values were in perfect agreement.
2- The region that lays outside the corona in about 5 to 15 RRsunR is seen to
has an acceleration (positive) attitude on some of the studied CME events. This
remark was repeated for example at the events that took place in: 04/12/2002 at
1:33:00.
3- The present code could in general be used in cases where two successive
events were appearing in few images of CME events that took place at the same
day. The results of the two event of 28/12/2002 at 12:31 and 16:29 showed that
the present code could successfully distinguish between them and the results
۱۱۱
were acceptable in general. This leads to the conclusion that the present
technique is approximately suitable for fast CME automatic detection and
analysis.
4- Despite the above point, it should be mentioned that there are few
precautions such as the small size of the CME, or its fast movement; that may
add a considerable error in the final results. Therefore one can conclude that not
all CME events are correctly detectable utilizing the present technique. The
present code could successfully detect large CME events with speed less than
about 700 kilometers per second. Error values in most cases were high.
5- Few of the present CME examples were processed further by a least
square fitting procedure for the height values against time. The aim was to
define a better speed value by elimination of the high fluctuations of speed
values as calculated directly from differentiation of CME height with time. The
most important concluded remark was that the latter method is more preferable
than the former one. In some examples, the error was reduced considerably.
6- In all of the studied cases, the error on calculation of the mean position
angle of the CMEs was in a good agreement with CDAW data. Thus, a
conclusion is made that the present technique suffices for CME angular
determination.
7- The present work provided an attempt to relate the detected CME
masses with their surface areas. Two suggestions were given in this course: the
linear and non-linear dependence of CME mass on area. Finding the
approximate relation was aided by CME masses from CDAW and areas
calculated from this work. Masses were calculated for both cases and in general
it was noticed that the kinetic energy from the non-linear fit were higher than
those from linear fit. Thus one may conclude that area and mass of CME could
be related, at least approximately.
۱۱۲
5.2. USuggestions for Future Development U
The present attempt could and must be developed further with many
improvements. The suggestions for this task are:
1. Considering the FITS file format is left for future developments of the
cmeDetect code. FITS files include much more useful information than jpg
images, however, FITS requires an accurate reading procedure.
2. The LASCO/C2 images hold detailed and accurate initial information
about the first stages of CME events. Therefore it would be useful in any future
development of cmeDetect to including C2 images at the first hours of any CME
event.
3. From a general insight of the results and code details, and comparison
with other codes, it is reasonable to suggest to use a combination of running-
difference and bulk-detection methods in a single process that aims to detect
CMEs from C3 images.
4. The region just outside the solar corona to about 15 RRsunR is seen of
importance in CME analysis since it appears that CME usually suffer from high
gain of speed at this region. Therefore, this region should be subjected to further
and careful analyses. An important task in this suggestion is to include the solar
dynamo effects on its interplanetary magnetic fields.
5- It is necessary to develop a built-in function within the present code that
directly computes the appropriate values of speed and acceleration from fitting
procedure.
6- In order to follow the present method of relating kinetic energy with
CME area, it is suggested that an accurate estimation of the CME plasma
properties must be included. These properties are: particle density, magnetic
field strength and direction, thermal energy and pressure; beside velocity rather
than speed.
۱۱۳
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Appendices Appendix A:
cmeDetect Flowchart
Loop on No.
Images
Start
Read LASCO Files
Read Time of
Images
Find Solar Radius
from Ref. Image
Clean Images
Find Time
Differences
Find theta, length, distances, area, end
points
Analyze Results
End
Plot Results
Normalize Image
Convert to BW
Apply Filter, Strel
Find Basic Parms.
Apply regionprops
Note: The chart symbol
refers to a subfunction.
Appendix B:
The Complete List of the Code cmeDetect.m
clc clear close all pause(0.1) crat=15;% Crat is the criterion of image imextendedmax. This value is fine craA=10;% Criteria for detecting obj's craB=10; % As crat increase, cratA and B should decrease cx1=247; cy1=260; % the center of the Sun instead of 256, 256 solarR=6.958e+08; % in meter solarArea=2*pi*solarR^2/1e6;% in km2 ploproc=0; % to plot subplots set it to 1 if not needed set to zero % Open files from a whole folder % addpath M:\soho_LASCO\Results_CME1_files\1 % This work with joining procedure cme13 % fileFolder = fullfile('M:\soho_LASCO\Results_CME1_files\1'); %North-North-West % addpath M:\soho_LASCO\Results_files\2 % this is 97 images!! % fileFolder = fullfile('M:\soho_LASCO\Results_files\2'); % %North-West-West %addpath M:\soho_LASCO\Results_files\2\11 % This is the same as above with selected images % fileFolder=fullfile('M:\soho_LASCO\Results_files\2\11'); % addpath M:\soho_LASCO\Results_files\2\22 % This is the same as above with selected images % fileFolder=fullfile('M:\soho_LASCO\Results_files\2\22'); % addpath M:\soho_LASCO\Results_files\3\event2 % checked for cmeDetect13 OK. # 14 ok % fileFolder = fullfile('M:\soho_LASCO\Results_files\3\event2');%North-North-East %addpath M:\soho_LASCO\Results_files\3\event1 % checked till cmeDetect13 ok % fileFolder = fullfile('M:\soho_LASCO\Results_files\3\event1');%South-East addpath M:\soho_LASCO\Results_files\6\1 fileFolder=fullfile('M:\soho_LASCO\Results_files\6\1'); dirOutput = dir(fullfile(fileFolder,'*.jpg')); fileNames = {dirOutput.name}' ; lfn=length(fileNames); I=zeros(512,512,lfn); tOut1Flaq=zeros(1,lfn)'; % Display images only imgW=2*247; imgH=2*260;% size(D); if the center was @256 we use size alone [IT1 erMsg]=ImagesTime(fileNames); % this tests for ascending sort and if erMsg~=0; disp('ERROR while sorting.'); return; end S12=imread('M:\soho_LASCO\Results_files\20130114_0706_c3_512.jpg'); S22=rgb2gray(S12); D=S22; [SolR1]=SuCirDet(S22,imgW+18,imgH-8);% This calculates the solar radius disp({['Detected solar radius is: ' num2str(SolR1) ' pixels.']}); disp({['The value: ' num2str(9.8204) ' pixels is used.']}); SolR=9.8204; pause (1); close all
% Conversion parameter in m/pixels; convP=solarR/SolR; % Conversion parameter in m/pixels disp({['Conversion parameter is: ' num2str(convP) ' m/pixels.']}); [L_erased,D]=CMEcleanimg(D,0); D=im2bw(D); % This makes #1 diff from cmedetect13 % this is to make B-W mapping of fixed points (convert to binary image) SE=[]; CG=[]; % To display more figures without using the mouse scrsz = get(0,'ScreenSize'); figure('NumberTitle','on','Name','Detected CME Position', 'Position',... [1 scrsz(4)/2-100 scrsz(3)/2 scrsz(4)/2]); figure('NumberTitle','on','Name','Largest Isolated CME ','Position',... [scrsz(3)/2 scrsz(4)/2-100 scrsz(3)/3+500 scrsz(4)/2]); % Next step is to analyze what was read in. [tOut1,tOut2,ImDateNum,NameOne,ImDate]=datesF(fileNames); % This finds the dates from filenames for i=1:lfn I2 = imread(fileNames{i}); I=rgb2gray(I2); [L_erased,I]=CMEcleanimg(I,0);% erases date and middle circle thresh=100; % theshold for finding the maxima [J]=normCME(I,thresh); % for normalization and vast crude filter %K=J; % figure, imshow(J); pause % SO FAR NO CHANGE IN THE REST %I=J; % This makes #2 diff from cmedetect13 I=im2bw(I); K=I-D; % D is the reference image, I is the present image l=0; if ploproc==1; subplot(2,2,1), imshow(I2); title ('Original Image no BW and basic filter.');end for i1=6:256-6; l=l+1; Co1=[0 0 0]; aI1=i1; for i2=6:512-6; % ASSUMING imgW == imgH == 512 ONLY if K(i1,i2)==-1; K(i1,i2)=0;end % to eleminate any possible (-1) value in K if (K(i1,i2)==1) && (K(i1,i2+1)==0) && (K(i1,i2+2)==0) && (K(i1,i2+3)==0) &&(K(i1,i2+4)==0) && (K(i1,i2+5)==0) && (K(i1,i2+6)==0)... &&(K(i1+1,i2)==1) && (K(i1+2,i2)==0) && (K(i1+3,i2)==0); K(i1,i2)=0; end if (K(i1,i2)==1) && (K(i1+1,i2)==0) && (K(i1+2,i2)==0) && (K(i1+3,i2)==0)&& (K(i1+4,i2)==0) &&(K(i1+5,i2)==0)&& (K(i1+6,i2)==0) K(i1,i2)=0; end end end if ploproc==1; subplot(2,2,2), imshow(K);title('Image after smooth filter.');end Str1=strel('square',8); % was disk , 4 for creating a square vector of 2 X 2 KS1 = imdilate(K,Str1); % dialation makes results worse
KS2 = imerode(K,Str1); % erosion cleans it up and works fine if ploproc==1; subplot(2,2,4), imshow(KS2);title('Image after erosion.'); end RE1=graythresh(KS2);% KS3=im2bw(KS2,RE1); %figure(3), imshow(KS3) if ploproc==1; subplot(2,2,3), imshow(KS3);title ('BW Image after thresholding.');end I5=KS3; props=regionprops(I5,{'FilledImage','Centroid','MajorAxisLength',... 'MinorAxisLength','Orientation','Area','Eccentricity','ConvexHull'}); [k1 k2]=size({props.FilledImage});% k2 is the number of detected objects regardless their size for t=1:k2 % Here we put ALL Areas in a matrix called a33 DE2=cat(1,props(t,:).Area); a33(t)=DE2; end indMaxArea=find(a33==max(a33)); % This is the index of the max. detected area. Gotcha! ik2=0; for i22=1:k2 % the var i is used in the MAIN LOOP. Careful DE=cat(1,props(i22,:).FilledImage); a(i22).dat=DE; % Here is the isolated objects DE2=cat(1,props(i22,:).Area); a44(i22).dat=DE2; % Here is the net area of isolated objects [a1 a2]=size(DE); if (a1>craA && a2>craB) % This is the criterion of how big the detected objects % should be. The bigger detewcts only Large objects. ik2=ik2+1; % independent index for output if (i22>=indMaxArea) figure(2);imshow(a(indMaxArea).dat); WE=(props(indMaxArea).ConvexHull); end [theta,justLength,L1,L2,V1,V2,s1,s2]=facntCME(props(i22).Centroid,imgW,imgH); % L1 and L2 are the horizental and vertical dist. from the center % of the image theta; % This is the Angle OK justLength;% This is the Length of c.m. of CME from center of the sun in pixels OK % CMEdist is the distance after conversion Lobj=props(i22).MajorAxisLength;% This is assumed as the length of the detected object OBJln=Lobj+justLength; % This is the Area1=props(i22).Area; % This is the total Area of each isolated CME in pixels OK g1=(props(i22).Area).^0.5/(pi);% This is the Radius of the detected % object assuming it is a Circle and this is apprximation ok % justLength is the center of the detected obj. from the center of % the image in pixels and here is the distance of detected CME from % the sun in kilometers : CMEdist=OBJln/2*convP/1e3-solarR/1e3;% in km % CMEdist=justLength*convP/1e3-solarR/1e3;% in km. Corrected such
% that the distance is NOT from the CME to the center of the Sun; but % from the CME to the SURFACE of the Sun. % and the area before was in pixels not we need it in km2 CMEarea=Area1*convP*convP/1e6; % in km2 figure(1);imshow(KS3,'InitialMagnification',100); hold on % Scalled showed image. xlabel({['Image name: ' fileNames{i}]}); [coefX coefY]=funcplotCME(g1,Lobj,V1,V2,imgH,imgW,justLength,theta,... CMEdist,CMEarea);% This is the main plotting function title(i) pause(0.5); % speed of plot + theta disp close(2) % Below we put length and area and angle in matrices with dim, (i,ik2): matAngle(i,ik2)=theta; % ik1 is an index independent of i22 matDist(i,ik2)=CMEdist; % I used ik2 instead of i22 because there may be more than one area detected matArea(i,ik2)=CMEarea; % This is the area in km2. Above is the distance in km. indexMat(i,ik2)=i22; % This matrix only stores the index i22 where there was a process matCoefX(i,ik2)= coefX; % This is the X-point of each distance of detected areas matCoefY(i,ik2)= coefY; % This is the Y-point of each distance of detected areas matLength(i,ik2)=justLength;% This is the length of detected areas from the sun in Pixels pause (.1) tOut1Flaq(i,ik2)=1;%means time record is needed end % of if statement end % of i22 loop end % of i loop. This marks reading the parameters from the images. % First a correction procedure is needed to join similar regions but separated % due to filtering and other reasons. Use AngleNew DistNew and AreaNew % for any forthcoming process. The var. crat is the angle criteria upon which the % code decides that this is a single event with two areas or not. Larger % criteria joins farther areas, so use crat less than 25 degrees. pause(1); close all disp('P L O T T I N G ...'); [AngleNew AreaNew DistNew]=CMEjoinAreas2(matAngle, matArea, matDist,crat); [CMEAng1,CMEArea1,CMEDist1]=simpleAnalyzeCME(AngleNew, AreaNew, DistNew); [CMEAng,CMEArea,CMEDist]=secAnalyzeCME(CMEAng1,CMEArea1,CMEDist1); [CMEspeed,CMEacc,CMEacc2,tO1,tO2]=CMEprpts(CMEAng,CMEArea,CMEDist,tOut1,tOut2,fileNames);% [out]=FinalCMEPlot(CMEDist,CMEAng,CMEspeed,CMEacc2,NameOne,ImDate,solarR); % added since # 15 disp (' E N D of main loop'); return pause(0.5); D = rgb2gray(imread(fileNames{69}));% this is the reference image figure; for i=1:lfn
I2 = imread(fileNames{i}); I = rgb2gray(I2); K=I-D; imshow(K);% title('Image with gray scale and basic process (subtraction from image 69).') title(fileNames{i}) pause (0.3) end
Few of the subfunctions used in the main code: function [J,K]=CMEcleanimg2(D,F) % The input D must by grayscale (use rgb2gray) % If F ==1 the output is plotted if F<>1 there is no plot % D = rgb2gray(imread(fileNames{3}));% this is the reference image --- 69 was good % imshow(D); J=D;J(480:512,1:512)=J(1,1); K=J;K(230:260,230:280)=K(1,1); if F==1; imshow(D); title('Original'); figure, imshow(J); title('Lower part erased'); figure, imshow(K); title('Occulter part erased'); end end ----------------------------------------------------------------------------------- function [coefX coefY]=funcplotCME(g1,Lobj,V1,V2,imgH,imgW,justLength, theta,CMEdist,CMEarea) if theta<=90; theta2=90-theta; elseif theta>90 && theta<=180 theta2=450-theta; elseif theta>180 && theta<=270 theta2=450-theta; else theta2=360-theta+90; end VE1=Lobj/2+V1(2); VE2=Lobj+V2(2); r=512-VE2; r1=imgH/2;r2=imgW/2; coefX=r1-(g1+justLength)*(cosd(theta));coefY=r2-(g1+justLength)*(sind(theta)); plot(V1,V2,'b-','LineWidth',2); plot([V1(2) coefX],[V2(2) coefY],'r','LineWidth',3); % the line plot(imgH/2,imgW/2,'bo','LineWidth',5); plot(coefX,coefY,'rD','LineWidth',2); plot(V1(2),V2(2),'bS','LineWidth',2); line([1 512],[247 247],'Color','y','linewidth',2);% this is x-line line([260 260],[1 512],'Color','y','linewidth',2);% this is y-line legend({['Angle is ',num2str(theta2),' Degrees'];['Distance is ',num2str(CMEdist,'%e'),' km'];... ['Total Area of the CME is ',num2str(CMEarea,'%e'),' km^2']},'Location','NorthOutside') hold off; %plot(V1(2),V2(2),'rD');
out=coefX; ----------------------------------------------------------------------------------- function [out]=FinalCMEPlot(CMEDist,CMEAng,CMEspeed,CMEacc2,NameOne,ImDate,solarR) % added since # 15 [AP1 AP2]=size(CMEspeed); [AD1 AD2]=size(CMEacc2); [AG1 AG2]=size(CMEDist); tiS=['Date of first point is:' NameOne ' and number of points is:' num2str(AP1)]; tiA=['Date of first point is:' NameOne ' and number of points is:' num2str(AD1)]; tiB=['Date of first point is:' NameOne ' and number of points is:' num2str(AG1)]; [aT bT]=size(CMEspeed); for k=1:bT figure; [a1 a2]=size(CMEspeed(:,k)); [b1 b2]=size(ImDate); c1=abs(a1-b2); plot(ImDate(1:end-c1),CMEspeed(:,k),'-D','LineWidth',2); xlabel('CME Evolution Time, minutes','FontWeight','Bold'); %%text(ImDate(1:end-c1)+5,CMEspeed(:,k),num2str(CMEAng(1:end-1,k))) ylabel('CME Speed, km/sec','FontWeight','Bold');TT1=strcat('CME No. :', num2str(k),'. ',tiS); title(TT1,'FontWeight','Bold'); figure; [a3 a4]=size(CMEacc2(:,k)); [b3 b4]=size(ImDate); c2=abs(a3-b4); plot(ImDate(1:end-c2),CMEacc2(:,k),'-D','LineWidth',2); TT2=strcat('CME No. :', num2str(k),'. ',tiA); title(TT2,'FontWeight','Bold'); xlabel('CME Evolution Time, minutes','FontWeight','Bold'); ylabel('CME Acceleration, km/sec^2','FontWeight','Bold'); out=1; figure; [z1 z2]=size(CMEDist(:,k)); [re1 re2]=size(ImDate); if z1>re2;CMW(:,k)=CMEDist(1:re2,k);op1=0;end if z1<re2;op1=abs(z1-re2);CMW=CMEDist;end if z1==re2;op1=0;CMW=CMEDist;end plot(ImDate(1:end-op1),CMW(:,k)*1000/solarR,'-D','LineWidth',2); xlabel('CME Evolution Time, minutes','FontWeight','Bold'); ylabel('CME Height, in R_S_U_N Units','FontWeight','Bold'); TT3=strcat('CME No. :', num2str(k),'. ',tiB); title(TT3,'FontWeight','Bold'); end end ----------------------------------------------------------------------------------- function [CMEspeed,CMEacc,CMEacc2,tO1,tO2]=CMEprpts(CMEAng,CMEArea,CMEDist,tOut1,tOut2,fileNames) [a b]=size(CMEDist); for k=1:b DistDiff1=diff(CMEDist(:,k));% The difference of X(n+1)-X(n); X(n+2)-X(n+1)... DistDiff2=diff(CMEDist(:,k),2);% The second difference, similar to [t11 t12]=size(tOut1'); [t21 t22]=size(tOut2'); [an1 an2]=size(CMEAng);
[ar1 ar2]=size(CMEArea); [ad1 ad2]=size(CMEDist); [df1 df2]=size(DistDiff1); [dt1 dt2]=size(DistDiff2); [tO1]=prepTimeVec(t11,df1,tOut1); [tO2]=prepTimeVec(t21,dt1,tOut2); size(DistDiff1); size(tO1); size(DistDiff2) ; size(tO2) ; CMEspeed(:,k)=abs((DistDiff1)./(60*tO1'));%(2:end)'); % This is CMEacc(:,k)=(DistDiff2)./(60*60*tO2');% (2:end)'); Here /60*60 because we size(diff(CMEspeed(:,k))); size(tOut1); dV=diff(CMEspeed(:,k)); [t11 t12]=size(tO1'); [v1 v2]=size(dV); % [tO3]=prepTimeVec(t11,dV,tO1); CMEacc2(:,k)=((dV./(60*tO1(2:end)')));% Only /60 is added because size(tO1(2:end)); end end ----------------------------------------------------------------------------------- function [CMEAng,CMEArea,CMEDist]=secAnalyzeCME(CMEAng1,CMEArea1,CMEDist1) [a b]=size(CMEAng1); CMEDist=CMEDist1; CMEArea=CMEArea1; % The loop below to correct angles from solar north counter clockwise as in % the )http://cdaw.gsfc.nasa.gov/CME_list) for PA measurements. for j=1:b for i=1:a if CMEAng1(i,j)<=90; CMEAng(i,j)=90-CMEAng1(i,j); elseif CMEAng1(i,j)>90 && CMEAng1(i,j)<=180 CMEAng(i,j)=450-CMEAng1(i,j); elseif CMEAng1(i,j)>180 && CMEAng1(i,j)<=270 CMEAng(i,j)=450-CMEAng1(i,j); else CMEAng(i,j)=360-CMEAng1(i,j)+90; end end end [a1 b1]=size(CMEAng); if a1~=a || b1~=b disp ('ERROR in angles at analyze function'); end ------------------------------------------------------------------------------------ function [tOut1,tOut2,ImDateNum,NameOne,ImDate]=datesF(fileNames) % Ahmed A. Selman 31-10-2014. if iscell(fileNames); % if fileNames was a cell DT5=cell2mat(fileNames); lfn=numel(fileNames); for t=1:lfn A=regexprep(DT5(t,:),'_c3_512.jpg', ''); A=regexprep(A, '_', ''); % ERRT=fileNames{i}; ERRT=A; ImY=ERRT(1:4); ImM=ERRT(5:6); ImD=ERRT(7:8);
Imh=ERRT(9:10); Imm=ERRT(11:12); ImDateSt(t,:)=[ImY ', ' ImM ', ' ImD ' ' Imh ':' Imm ':00']; ImDateNum(t)=datenum(ImDateSt(t,:)); end %for t=2:lfn Str12=24*60*diff((ImDateNum)); % This is the first difference, minutes Str22=24*60*24*60*diff((ImDateNum),2);% and the second one, else % in case one image is input DT5=fileNames; A=regexprep(DT5,'_c3_512.jpg', ''); A=regexprep(A, '_', ''); ERRT=A; ImY=ERRT(1:4); ImM=ERRT(5:6); ImD=ERRT(7:8); Imh=ERRT(9:10); Imm=ERRT(11:12); ImDateSt=[ImY ', ' ImM ', ' ImD ' ' Imh ':' Imm ':00']; ImDateNum=datenum(ImDateSt); Str12=0; % there is no time difference Str22=0; % there is no 2nd time difference end NameOne=ImDateSt(1,:); ImDate(1)=Str12(1); % The first of the series is ref. for t=2:lfn-1; ImDate(t)=Str12(t)+ImDate(t-1);% Time end [fA fB]=size(Str12); tOut1=zeros(fA,fB); tOut1(1,1)=Str12(1,1); % tOut1 is for speed, only 1 is different for i=1:fA for j=1:fB tOut1(i,j+1)=Str12(i,j); end end [gA gB]=size(Str22); tOut2=zeros(gA,gB); tOut2(1,1)=Str22(1,1); % tOut2 is for acc, 2 are different tOut2(1,2)=Str22(1,2); for i=1:gA for j=1:gB tOut2(i,j+2)=Str22(i,j); end end end
Uالخالصة
تعمل على كشف االنبعاثات وهنالك حاجة مستمرة لبناء برامج آلية تعتمد على الحاسوب
تمثل كتال براقة من البالزما الساخنة، ولكونها .من المراصد المختلفة )CME(اإلكليلية كتليةال
حصول تنطلق هذه االنبعاثات من مناطق نشطة في اإلكليل الشمسي باتجاهات متفاوتة وبمعدالت
تنبعث إلى الفضاء اإلكليلية كتليةاالنبعاثات اليعتقد أن و. تشير إلى أن اإلكليل الشمسي دائم التغير
غير االنبعاثات ومع أن اآللية الدقيقة لتكون وانبعاث هذه . بسبب حدوث عدم استقرارية مفاجئة
يوجب تطوير مؤكدة لليوم كانت هناك دراسات عديدة حول هذا الموضوع وهو األمر الذي
. اإلمكانيات المالئمة للكشف عنها
قد كتب برنامج حاسوبي يهدف في البحث الحالي حيث هدفا هذه المهمة اتخاذ لقد جرى
) LASCOالسكو (باستخدام صور الكاشف اإلكليلية كتليةاالنبعاثات الإلى الكشف عن وتحليل
ودرست كتلية نبعاثات الأخذت بعض األمثلة لال ).SOHOسوهو (الذي على متن المرصد
كانت األحداث التي اعتمدت . بصور األرشيف من السكوباستخدام البرنامج الحالي مع االستعانة
، ۲۰۰۲، ۲۰۰۰خالل السنوات ية اإلكليليةكتلاالنبعاثات الحدثا من أحداث ۲۰مجموعة من
. ۲۰۱۳و ۲۰۰۷، ۲۰۰۳
باسم يسم بجرت باستخدام برنامج ماتال اإلكليلية كتليةاالنبعاثات العملية الكشف عن
)cmeDetect( . هذا البرنامج احتوى على عدة دوال فرعية كتبت خالل هذا البحث لغرض
طريقة الكشف األساسية التي اتبعت . وتحليل مواصفاتها اإلكليلية كتليةاالنبعاثات الالكشف عن
التحليل اعتمد على . تتبع حركتها البحث عن المناطق الكبيرة في الصور ومن ثمكانت تعتمد على
المحسوبة قسم االرتفاع النتائج النهائية تضمنت . بكسل) ٥۱۲في ٥۱۲(صور السكو بحجم
.الطاقة، الكتلة والمساحة ،الزاوية، التعجيل، )االنطالق(، سرعتها اإلكليلية كتليةالنبعاثات الل
.)CDAW(جرت مقارنة معظم النتائج مع مكتبة
ة اإلكليليةاالنبعاثات الكتليوالكشف عن أحداث كان قادرا على التمييز البرنامج الحالي
تمتاز بقلة كثافة الحواف الخارجية الواضحة في صور السكو ولكن عندما كانت هذه األحداث
االنبعاثات بسبب كون حافة هذا التصرف فسر . كانت قدرة الكشف أما معدومة أو بكفاءة قليلة
ولكنها كانت بقيمة أقل هي الجزء األكثر أهمية لعملية الكشف من هذا النوع ة اإلكليليةكتليال
الكشف ا النقص في كفاءة بينت نتائج البحث الحالي أن هذ. من القيم الحقيقية% ۷إلى ۳بحوالي
المحسوبة من قيمها اإلكليلية كتليةنبعاثات السيكون سببا لحساب ارتفاعات أعلى على العموم لال
.بين الحسابين بقي متوافقا بصورة عامةولكن التصرف CDAWدليل المقاسة في
النتائج بينت تطابقا جيدا في قيم الزوايا التي حسبت من العمل الحالي مع الزوايا الموجودة
لم يكن هناك توافق مرض توافق أقل وجد في ما يخص االرتفاعات وتقريبا . CDAWفي دليل
متطابقة تماما مع قيم الدليل مع هذا كانت قلة من نتائج البحث الحالي . نتائج التعجيل والسرعةمع
. المذكور
إلى ٥بين في االرتفاعات ية اإلكليليةكتلاالنبعاثات الوجد أن هناك تصرف مميز لبعض
ة ومفاجئة في دبعض االنبعاثات شهدت زيادة مطرمرة ضعف نصف قطر الشمس حيث أن ۱٥
. ة اإلكليليةكتلياالنبعاثات اللبعض آخر من هذه المالحظة لم تتكرر . السرعة
باستخدام طريقة جرت موائمتها اإلكليلية كتليةاالنبعاثات البعض قيم االرتفاعات الخاصة ب
بناءا على هذا ومن مناقشة أسباب . فتحسنت نتائج السرعة إلى درجة مهمةالمربع األصغر
أن هناك عدد مهم من التحسينات التي النتائج غير المتوافقة في البحث الحالي جرى التوصل إلى
.يجب إدخالها
االنبعاثات إليجاد العالقة بين مساحة عالوة على ما سبق ففي البحث الحالي جرت محاولة
.المساحة-خاص بالكتلةمعايرة إيجاد منحني لغرض المحسوبة وبين كتلتها اإلكليليةكتلية ال
ومن .المعالجة الخطية والثانية على غير الخطيةاقترحت طريقتين إلجراء هذا األولى تعتمد على
المجوفة بصورة تقريبية ووجد أن المعالجة ة اإلكليليةكتلياالنبعاثات الالطريقتين حسبت كتلة
من هذه الحسابات جرى حساب الطاقة . غير الخطية تنتج قيما أعلى للكتلة من المعالجة الخطية
. لهذه االنبعاثات المجوفةالحركية
جمهورية العراق وزارة التعليم العالي والبحث العلمي
كلية العلوم -جامعه بغداد قسم علوم الفلك والفضاء
حساب وتحليل االنبعاثات الكتلية اإلكليلية
سوهو) \من بيانات (السكو
رسالة الفلك والفضاءعلوم الى قسم ةمقدم
جامعة بغداد -كلية العلوم كجزء من متطلبات نيل
الفلك والفضاءعلوم درجة الماجستير في
من قبل
الحكيم زينب فاضل حسين )۲۰۰۲علوم في الفلك والفضاء (بكلوريوس
بأشراف سلمان د. أحمد عبدالرزاق
م ۲۰۱٥هـ ۱٤۳٦
