The SuperMACHO Project: Using Gravity to Find Dark Matter Arti Garg November 1, 2007 Harvard...
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Transcript of The SuperMACHO Project: Using Gravity to Find Dark Matter Arti Garg November 1, 2007 Harvard...
The SuperMACHO Project:Using Gravity to Find Dark Matter
Arti GargNovember 1, 2007
Harvard UniversityDepartment of Physics and Harvard-Smithsonian Center for
Astrophysics
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
What is Dark Matter?
• Well, we don’t really know• What we do know:
– Objects in the Universe behave as if they feel stronger gravitational forces than what the matter we see could generate
– Most of the matter in the Universe is “dark”– Places where dark matter might exist:
Abel 2218 (http://spaceimages.northwestern.edu/p29-abel.html)Image Credit: Jason Ware
Permeating the Universe
Galaxy Clusters Galaxy “Halos”
http://zebu.uoregon.edu/1999/ph123/lec08.html
Galactic Halo Dark Matter
• Rotation velocities are too fast
Image Credit: Jason Ware
Andromeda Galaxy
From http://zebu.uoregon.edu/1999/ph123/lec08.html
Radial Profile of Rotation Velocity
Galactic Halo Dark Matter
• Rotation velocities are too fast
• Radial profile of rotation velocities suggests spherical distribution of dark matter – the Halo
NGC 4216 in a simulated halo
From http://chandra.as.utexas.edu/~kormendy/dm-halo-pic.html
Dark MatterHalo
Visible GalaxyDisk
Galactic Halo Dark Matter
• Rotation velocities are too fast• Radial profile of rotation velocities
suggests spherical distribution of dark matter – the Halo
• One proposed candidate for the dark matter is in the form of “MAssive Compact Halo Objects” (MACHOs)– These can be detected through “gravitational
microlensing”
What is Gravitational Lensing?
• Light from a star or galaxy is bent by a massive object between it and the observer
Observer
Lens(e.g. galaxy)
Source
Images
Light Path
Virtual LightPath
From CASTLES Survey: http://cfa-www.harvard.edu/castles/Individual/HE0435.html
HE0435-1223
Infrared Image of a Gravitational Lens System
LensGalaxy
Image
What is microlensing?
• In microlensing, the separation between the source and image is too small to be resolved– The lensed object just looks brighter
• Often the source, the lens, or both are moving so the effect is temporal– For SuperMACHO, the time scale is ~80 days
What is microlensing?
• In microlensing, the separation between the source and image is too small to be resolved– The lensed object just looks brighter
• Often the source, the lens, or both are moving so the effect is temporal– For SuperMACHO, the time scale is ~80 days
Microlensing
Time
Obs
erve
d S
ourc
e B
right
ness
Source
Lens
Lens Trajectory
Microlensing “Light Curve”
Microlensing to Detect Dark Matter
• In 1986, B. Paczynski suggested using microlensing to detect MACHOs by their gravitational effect on stars in nearby dwarf galaxies such as the Magellanic Clouds
Large Magellanic Cloud
Milky Way Halo
Us
MACHOs
Light Path From http://antwrp.gsfc.nasa.gov/apod/ap050104.htmlEarth Image: Apollo 17
SuperMACHO Project• More events:
– CTIO 4m
– Mosaic imager: big FOV
– 150 half nights over 5 years• Completed Jan 2006
– blocks of ~3 months per year• Observe every other night in
dark and gray time
– Single Filter: custom VR-band
• Spatial coverage:– 68 fields, 23 sq deg.
• Difference Imaging
SuperMACHO fields
Primary field setPrimary field set
Secondary field setSecondary field set
SuperMACHO Team
Harvard/CfA – Arti Garg, Christopher W. Stubbs (PI), W. Michael Wood-Vasey, Peter Challis, Gautham Narayan
CTIO/NOAO – Armin Rest1, R. Chris Smith, Knut Olsen2, Claudio Aguilera
LLNL – Kem Cook, Mark E. Huber3, Sergei NikolaevUniversity of Washington – Andrew Becker, Antonino Miceli4
FNAL – Gajus MiknaitisP. Universidad Catolica – Alejandro Clocchiatti, Dante Minniti,
Lorenzo Morelli5
McMaster University – Douglas L. WelchOhio State University – Jose Luis PrietoTexas A&M University – Nicholas B. Suntzeff
1. Now Harvard University, Department of Physics2. Now NOAO North, Tucson3. Now Johns Hopkins University
4. Now Argonne National Laboratory5. Now University of Padova
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Image Reduction Pipeline
• Implemented in Perl, Python, and C• Images processed morning after observing• Stages of image processing:
– Standard calibration (bias, flat field)– Illumination correction– Deprojection/Remapping (SWARP)– Regular Photometry (DoPhot)– Difference Imaging– Photometry on Difference Images (Fixed PSF)
Image Reduction Pipeline
• Implemented in Perl, Python, and C• Images processed morning after observing• Stages of image processing:
– Standard calibration (bias, flat field)– Illumination correction– Deprojection/Remapping (SWARP)– Regular Photometry (DoPhot)– Difference Imaging– Photometry on Difference Images (Fixed PSF)
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Microlensing Event Selection
• Detecting microlensing– We monitor tens of millions of stars in the Large
Magellanic Cloud– Tens of thousands of those appear to change
brightness– Need to determine whether those changes are:
• Real, and not an artifact or cosmic ray• Due to microlensing, or some other phenomenon
Microlensing Event Selection
• Detecting microlensing– We monitor tens of millions of stars in the Large
Magellanic Cloud– Tens of thousands of those appear to change
brightness– Need to determine whether those changes are:
• Real, and not an artifact or cosmic ray
• Due to microlensing, or some other phenomenon
Time
Brig
htne
ss
Microlensing Event Selection
• Microlensing causes the brightness of a star to change in a predictable way
Time
Brig
htne
ss
Microlensing Event Selection
• But many other things also change in brightness such as supernovae – these turn out to be much more common
Microlensing Event Selection
• And if your nights off from the telescope and the weather conspire in the wrong way, it’s hard to tell what’s microlensing
Microlensing Event Selection
• So what do you do? – You get a graduate student!
1. “Follow-up” Observations
Magellan I&II 6.5m Telescopes
Microlensing Event Selection
• So what do you do? – You get a graduate student!
2. Light Curve analysis tools
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My Work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Follow-up Program
• Developed computational tools and protocols for analyzing many GBs of nightly CTIO observations in almost real time to pick out interesting events and prioritize them for follow-up observation– Follow-up is time critical because events are only
active for a few weeks
• Applied for many nights of Magellan telescope time to follow interesting events as we discovered them at CTIO
Classifying events using follow-up
• Spectroscopic Observations
Spectrum of a supernova Spectrum of the Sun, a typical star(How microlensing might look)
Source: http://homepages.wmich.edu/~korista/sun-images/solar_spec.jpg
Wavelength Wavelength
Inte
nsity
Inte
nsity
SM-2004-LMC-821
VR~21
Spectral classification: Broad Absorption Line AGN
Classifying events using follow-up
• Spectroscopy is an excellent way to classify an event, but...– It is time-consuming and can’t be done for
faint events
• Obtaining a spectrum for every interesting event is not feasible
Classifying events using follow-up
• Multi-band observations - “poor man’s spectroscopy”
Classifying events using follow-up
• Multi-band observations - “poor man’s spectroscopy”
• The ratio of brightness in different “filters” gives a crude measure of the event’s wavelength spectrum– The ratios for “vanilla” stars (i.e. microlensing)
differ from supernovae
• This method is less precise but can be used for faint events
Stars have characteristic ratios of filter intensities
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
A light curve describes an object’s brightness as a function of time
Time
Brig
htne
ss
Light Curve Analysis
• Why do we need it?– Only have follow-up for 2 out of 5 years– Follow-up is incomplete and sometimes inconclusive
• What is it?– Software analysis tools that calculate ~50 “statistics”
describing the light curve• Unique?• Significant and Well-sampled?• Microlensing-like?• Unlike other things?
Unique?
-Frequent and periodic variability -Year-to-Year change in baseline
Variable Star Active Galactic Nucleus (AGN)
-Need more data after peak
Significant and well-sampled?
Microlensing-Like?
-This is a Supernova
-Fit well by microlensing and supernova models
Unlike other phenomena?
Passes all Criteria
Outline
• What is Dark Matter?• How can we detect DM with a telescope?
– Gravitational Microlensing• The SuperMACHO survey• My Work
– Image-Processing Software Verification– Microlensing Event Selection:
• “Follow-up” Observations• “Light curve” Analysis
– Simulations• Detection Efficiency• Contamination Rate
Simulations
1. Allows optimal “tuning” of selection criteria– Allow the most microlensing events while rejecting the most
contaminants– Provides estimate of contaminant fraction
2. Provides quantitative estimate of detection efficiency– Fraction of simulated events that are recovered– Differences between simulated population and recovered
population
3. Estimate how many events we should expect from various models
– Multiply by distribution of event parameters consistent with various microlensing models to get expected number of microlensing events (Rest et al. 2005)
Simulations
• Simulate a large number of events– Microlensing: all combinations of source star
brightness, event duration, and amplification• Determine which events survive selection criteria
Detection Efficiency
– Supernovae: all combinations of redshift, extinction by dust, intrinsic shape
• Determine which events survive selection criteria
Contamination Rate
Simulations
• Obtain light curves for a grid of positions across our field-of-view
• Add simulated event to each position– Can add multiple events to the same light curve– We simulated ~57 million ML events and ~4 million SNe
Simulations
Simulations of
Microlensing
events
Simulations of Supernovae
Detection Efficiency Depends on Source Brightness
Source Brightness
(-2.5*log(Intensity))
Nu
mb
er o
f ev
ents
Simulated
Recovered
Next Steps
• We are finalizing our selection criteria– Final set of Candidates– Final Detection Efficiencies– Final Contamination Rate
• We will distinguish between microlensing models by comparing the predicted rate of ML events with our observed rate
• Most of the matter in our Galaxy is “dark”
• We can detect Dark Matter with gravitational lensing
Summary
Summary
• SuperMACHO searches for Dark Matter in the form of MACHOs in the Milky Way
• Gravitational microlensing is easily confused with other things
• Additional observations and light curve analysis improve event classification
• Simulations allow for estimation of detection efficiency and contamination rate
Summary
Lens Equation
Source: Blandford & Narayan 1986
(Mollerach & Roulet 2002)
Microlensing
Source: Michael Richmond (RIT)
Lens Trajectories Magnification Due to Lensing Event
Source: Paczynski 1991
rE = projection of E at lens distance
source
u =impact parameter
Microlensing Light Curve
Time
Flu
x
to = time of maximum brightness
t = characteristic time ( ) fo = baseline
source flux
fo x Amax umin = closest approach
Observables for Event Ensemble
= Optical depth toward source population– likelihood that a source is within
rE of a lens at any time
Γ = Distribution of
(Mollerach & Roulet 2002, Alcock et al. 2000)
(Mollerach & Roulet 2002)
Ensemble of events has a uniform distribution of umin
The MACHO project (1995-2000)
• Found of 1.2 x 10-7 (Alcock et al 2000)
– Consistent with Milky Way Halo composed of ~8-50% MACHOs
– Event time scales ~80 days• Recent results from
EROS-2 indicate some events were not microlensing (Miltsztajn & Tisserand 2005)
– Revised MACHO fraction estimate ~16% (Bennett 2005)
– EROS-2 find a MACHO fraction of <7% (Tisserand et al. 2006)
- 0.3+0.4
(Alcock et al. ApJ 542, 281 2000)
Contamination
SuperMACHO Project• More events:
– CTIO 4m
– Mosaic imager: big FOV
– 150 half nights over 5 years• Completed Jan 2006
– blocks of ~3 months per year• Observe every other night in
dark and gray time
– Single Filter: custom VR-band
• Spatial coverage:– 68 fields, 23 sq deg.
• Difference Imaging
RR Lyrae from MACHO (black) and SuperMACHO (red)