Mallorca, Spain, 2009 March 31 – April 1

EPIC Calibration Meeting, MallorcaEPIC Calibration Meeting, Mallorca Update of EPIC pn noise suppressionUpdate of EPIC pn noise suppression K. Dennerl, 2009 March K. Dennerl, 2009 March 3131

Mallorca, Spain, 2009 March 31 – April 1Mallorca, Spain, 2009 March 31 – April 1

EPIC Calibration & Operations Meeting

Update of EPIC pn noise suppressionUpdate of EPIC pn noise suppression


“Noise events”: events which are not caused by photons,

mainly evident at low Pulse Height Amplitudes (PHAs)

“Noise events”: events which are not caused by photons,

mainly evident at low Pulse Height Amplitudes (PHAs)

50.7 ks closed FF quadrant 0, rev 059

50.7 ks closed FF quadrant 0, rev 059

PHA [adu]

nu

mb

er

of

ev

ents


raw_y

20-65 adu

nois

e

Previous attempts to suppress the noise events were based on their spatial/spectral characteristics ( epreject )


Previous attempts to suppress the noise events were based on their spatial/spectral characteristics ( epreject )

without noise suppression (epreject)

Vela SNR, FF, rev 534, 38.8 ks

with noise suppression (epreject)


However, the spatial/spectral characteristics of noise events turned out to be not sufficiently stable in time to achieve in all cases a satisfactory noise suppression with the concept of a temporally constant noise model.

20 50adu


An improved approach would be to scale the temporally constant noise model individually to each observation, by using information derived from the particular observation.

This approach is under investigation, but it is fairly sophisticated, and it is not clear yet whether it will be reliable enough for general usage.


An improved approach would be to scale the temporally constant noise model individually to each observation, by using information derived from the particular observation.

This approach is under investigation, but it is fairly sophisticated, and it is not clear yet whether it will be reliable enough for general usage.

In the process of investigating the temporal properties of noise events, it was found that individual frames are considerably noisier than the average. If these frames are rejected, the signal to noise ratio can be improved at low energies.

This method is so straightforward that it can be easily implemented.


FF closedclosed, rev 0462, 23 ks, CCD 1

Number of frames containing NEVT_FRM events within 20..30 adu

NEVT_FRM > 7


FF closedclosed, rev 0462, 23 ks, CCD 1

NEVT_FRM > 5



FF closed

closed, rev 0462, 23 ks, CCD 1 – 6


CCD 1

CCD 2

CCD 3

CCD 4

CCD 5

CCD 6



FF closed

closed, rev 0462, 23 ks, CCD 7 - 12

CCD 7

CCD 8

CCD 9

CCD 10

CCD 11

CCD 12


EPIC-pn noise (LW), rev 0790, 61 ks closedclosed, after offset corrections, all events

20 adu 21 adu 22 adu 23 adu 24 adu

25 adu 26 adu 27 adu 28 adu 29 adu

30 adu 31-35 adu 36-40 adu 41-45 adu 46-50 adu


rev 462

23 ks closedclosed

FF

no offset corrections

all events

30-50 adu

raw dataraw data

At 30-50 adu, “noise” creates mainly undefined patterns:


rev 462

23 ks closedclosed

FF


non-singles

processed dataprocessed data


30-50 adu


rev 462

23 ks closedclosed

FF


singles

processed dataprocessed data


30-50 adu


singles, 20 – 30 adu, all frames

LW thick, SNR 1E0102, rev 0803, 30 ks


total number of frames

(reconstructed) number of frames with no event below 31 adu per CCD

number of frames with 2 events below 31 adu per CCD

SNR 1E 0102, rev 803, LW, thick, 30 ks

number of frames with more than 9 events below 31 adu per CCD

number of frames with 1 event below 31 adu per CCD



CCD 1

CCD 2

CCD 3

CCD 4

CCD 5

CCD 6

LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 1 – 6



CCD 7

CCD 8

CCD 9

CCD 10

CCD 11

CCD 12

LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 7 – 12



image image mask image mask applied


original image cleaned image removed noise

= +


20 – 30 adu, all events

source practically invisible in removed events


LW thick, SNR 1E0102, rev 0803, 30 ks, CCD 4

before / after

removal of noisy frames

20 – 30 adu

all events

same intensity cuts


singles, 0.3-3.0 keV

mask for source regions to be avoided for noise

determination

source region mask

In order to determine the noise properties, some areas have to be ignored:



final mask, excluding source regions and noisy pixelsnoisy pixel mask


In order to determine the noise properties, some areas have to be ignored:


unfiltered data singles, 24 adu after noisy frame removal



= +

singles, 20 – 30 adu, frames with more

than 2 events below 31 adu per frame and

CCD

singles, 20 – 30 adu, frames with 1 or 2

events below 31 adu per frame and CCD

singles, 20 – 30 adu, all frames



unfiltered data after noisy frame removal

singles, 120-130 eV



epreject


At which stage should “noisy frames” be identified At which stage should “noisy frames” be identified

and removed ?and removed ?

raw data recombined event list

raw data:

events not yet recombined better discrimination of noisy frames

standard processing does not allow removal of frames before epevents

major changes of SAS may be required

time-consuming tasks (epevents..) need to be repeated in case of iterations

screening improves signal/noise ratio only at low energies and would cause

unnecessary loss of exposure at higher energies

recombined events list:

quick and easy application: optional task at the end of the pipeline processing

number of events within PHA range not available anymore (patterns)


Solution:Solution:

separate the determination of noisy frames from their removal !

only one change required for pipeline processing: keep the “rawevents.dat” files;

they contain

• information about the raw events (no recombination yet performed)

• time tags: unique identification of frames possible, also in the final event list !

noise suppression is based on the removal of complete frames

no need for repeating the pattern recognition and energy correction,

only exposure needs to be updated if frames are removed

one additional task (e.g. “epnoise”) would be sufficient:

- filter a copy of the final event list according to criteria derived from the “rawevents.dat” files and other input: masks, adu range and cutoff criteria (e.g. discrepancy between observed distribution and poissonian fit)

- update the exposure extensions

quick iterations possible

output from source detection could be used for creating masks


optional image mask

upper ADU threshold

“epnoise”

raweventsxx.datraweventsxx.dat

“Final” cleaned event file

possible implementation:


Mallorca, Spain, 2009 March 31 – April 1Mallorca, Spain, 2009 March 31 – April 1

EPIC Calibration & Operations Meeting

Update of EPIC pn noise suppressionUpdate of EPIC pn noise suppression

Mallorca, Spain, 2009 March 31 – April 1

Documents

Transcript of Mallorca, Spain, 2009 March 31 – April 1