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![Page 1: Overview Review of the Goddard method Update on instrument/detector evolution (a purely phenomenological approach) Characterization of the soft proton.](https://reader035.fdocuments.in/reader035/viewer/2022062620/551ac4a15503466b6a8b4e82/html5/thumbnails/1.jpg)
Overview
• Review of the Goddard method
• Update on instrument/detector evolution(a purely phenomenological approach)
• Characterization of the soft proton flares(spectral shape and spatial distributions)
• Testing the stability of this method(by looking at pointings with multiple obsids)
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Method Review: Philosophy
• Emission from the Galactic ISM fills the FOV– Sufficiently faint that it must be summed over
large regions in order to produce a good spectrum
• The same is true of extended extragalactic emission (e.g. galaxies & clusters)
• Background subtraction method must provide good statistics over large regions of the FOV
• but still be sensitive to the small-scale variations in the detector
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Method Review
• The corner pixels are a measure of the particle background
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Method Review
• The corner pixels are a measure of the particle background
• There are not enough counts in the corner pixels of a single observation for a robust characterization of the background…
• …need to find some way of using corner data from other observations…
• ...need to characterize the temporal variations of the corner spectra.
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Method Review: Corner Data
• The Entire Public Archive, Revs 25-1128– 3500 observations, 8100 observation segments– 76 Ms/MOS camera
• Remove CalClosed and badly flared data– 2500 observation segments/MOS camera– 44 Ms/MOS camera
• 34% of MOS data affected by flares
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Method Review: Corner Data
For each obsid we formed a histogram of the rate, set the “quiescent level” to mean rate, and removed periods >3σ above quiescent level.
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The mean corner spectrum
3 Parameters for the Spectral Shape
Rate: 0.3-10.0 keV Hardness: 2.5-5.0/0.4-0.8 keVPL Index 5-12 keV
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For A Typical Chip
Rate
Hardness
Index
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• Variation in the Hardness is greater than statistical: Hardness is temporally variable
• Variation in Power Law Index is statistical
Method Review
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Method Review
Step 1: Augmentation
For a given observation, measure Rate and Hardness. From the database of corner data, extract more data with similar rates and hardness.
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Step 1: Augmentation
Since the mean spectrum varies from chip-to-chip, augmentation must be done on chip-by-chip basis.
Method Review
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Method Review
• The background spectrum measured by the corner data need not be the same spectrum measured within the FOV.
• The instrumental lines are known to vary particularly strongly with location.
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The continuum also varies strongly
Method Review
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Method Review
Step 2: Correct spectrum to the FOV
Assume that temporal variations affect the corner pixels and the FOV in a similar manner.
The background spectrum in the observation FOV is then given by
Obs. Corner spectrum*FWC FOV spectrum
FWC Corner spectrum
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Method Review
Step 2: Correct spectrum to the FOV
• Chip 1 is a special case – corner data must be constructed from a subset of corner data for the other chips– MOS1 use 2, 3, 6, & 7– MOS2 use 3, 4, & 7
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Method Review: FWC Data
• The Entire Public Archive
• For normal chip states– 60-85 observation segments– 0.7-1.1 Ms of exposure– 1.5-2.3×105 counts
• For anomalous chip states– ~10 observation segments– 60-200 ks of exposure– 1.6-6.2×104 counts
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Method ReviewStep 3: Ignore the Al and Si lines
Slight shifts in gain or location can produce strong P-Cygni profiles after subtraction.
Therefore interpolate over 1.2-1.9 keV in forming the background spectrum and
Fit the Al and Si lines when fitting the object spectrum
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Method ReviewImplementation:
• Originally: Perl scripts calling SAS tasks,IDL routines for light curve cleaning and
background spectrum construction (mine)
• Now: Perl scripts calling SAS tasks, FORTRAN routines for light curve cleaning and
background spectrum construction (Snowden)
• Soon: Perl scripts calling SAS tasks (Perry)
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Detector Phenomenology
• Not all chips are well behaved…
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Detector Phenomenology
• Not all chips are well behaved…
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Detector Phenomenology
• Not all chips are well behaved…
• MOS1-4, MOS1-5, MOS2-2, MOS2-5– anomalous states occur at different times– always characterized by high rates and low
hardness
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Detector Phenomenology
• MOS1-4, MOS1-5, MOS2-2, MOS2-5– always characterized by high rates and low
hardness due to low energy “plateau”
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Detector Phenomenology
• MOS1-4, MOS1-5, MOS2-2, MOS2-5– always characterized by high rates and low
hardness due to low energy “plateau”– “extra” component somewhat localized for some
chips
2-5S 2-5H
0.3-
0.8
keV
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Detector Phenomenology
• MOS1-4, MOS1-5, MOS2-2, MOS2-5– always characterized by high rates and low
hardness due to low energy “plateau”– “extra” component somewhat localized for some
chips, but not so clear in other cases
1-5S 1-5H
0.3-
0.8
keV
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Detector Phenomenology
• MOS1-4, MOS1-5, MOS2-2, MOS2-5– “extra” component somewhat localized for some
chips, but not so clear in other cases– MOS1-4 anomalous state does not generally
appear in the corner pixels
1-4S 1-4H
0.3-
0.8
keV
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Detector Phenomenology
• Summary of States– S: “standard state”– V: “verification” : 1-4, 2-2, 2-5 for Rev<41.5– H: “high”:1-4, 1-5, 2-5, detectable in corner pixels– A: “anonymous”: 1-4, not detectable in corner pix.
• No other chip has yet been detected in A state
• But since A state can only be detected in FWC data…
• Need to understand what causes these states– Are there detector parameters that can be used to
detect these states independently?– Particularly important for the A states!
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• Handling multiple states– V state does not have enough effected data to
justify significant effort– H state FWC data does exist, poor for 1-4 & 1-5– A state FWC data OK for 1-4 (not great)
• Modifications to ESAS– Detect chip state first– Extract appropriate state FWC data– Augmentation careful to exclude data from the
wrong state– For now 1-4 is caveat emptor
Detector Phenomenology
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• Since there may be residual s.p. contamination,• Must characterize the spectrum• Must characterize the spatial distribution
• Easiest to detect effects in the spectrum
Soft Proton Flare Characterization
• Total count rate in the FOV:
object+cosmic background+QPB+s.p.flares
• Light curve cleaning minimizes sum but– Does not guarantee that s.p.flares=0
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Soft Proton Flares
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Soft Proton Flares
• Use the entire public archive
• For each obsid, run light-curve cleaning
• If light-curve cleaning successful– Extract spectrum from full FOV for flare-free int.– Extract spectrum from full FOV from flare int.
– Spectrum=flare−non-flare*tflare/tnon-flare
• since flare spectral shape depends upon the strength of the flare, select only weak flares for this analysis
– Assumes that s.p. flare is additive.
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Soft Proton Flares
• Characterize flare spectrum by (8.0-10.0 keV)/(0.8-1.35 keV) hardness ratio
• Wide range of hardness ratios • Not clear whether statistically significant• Moot point
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Soft Proton Flares
• Assume that the S.P. spectral shape is variable– Variation of hardness ratio– Dependence of hardness ratio on flare strength
• Can’t be subtracted from object spectrum → must be fitted with object spectrum
• Find functional form with fewest parameters to describe the entire family of spectra
• RMF and ARF not appropriate for soft protons
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Soft Proton Flares
• Best fit
A0exp(−B0E)+A1exp(−B1E)
where
A1=a0+a1A0+a2A02
B1=b0+b1B0+b2B02
which provides a very good fit to the data,
the ai and bi need be determined only once,
but has the problem that it can not be implemented is most spectral fitting packages
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Soft Proton Flares
• Broken power law fit using RMF is OK
A0E−B0 for E<Eb
A0EbB1−B0
E−B0 for E>Eb
• Broken power law fit using RMF with
Eb=constant
Is almost as good
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Soft Proton Flares
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Soft Proton Flares
• Spatial distribution determined in same way as the spectrum but with bright point sources explicitly removed from the images
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Soft Proton Flares
• Spatial distribution determined in same way as the spectrum but with bright point sources explicitely removed from the images
• Only small scale feature is low energy “hot” edge of MOS1-2, none seen in MOS2
• Distribution flat at low energies, peaked at higher energies
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Soft Proton Flares
• Spatial distribution determined in same way as the spectrum but with bright point sources explicitly removed from the images
• Only small scale feature is low energy “hot” edge of MOS1-2, none seen in MOS2
• Distribution flat at low energies, peaked at higher energies, always flatter than the X-ray vignetting
• No vignetting differences due to filters
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Soft Proton Flares
• Spectro-spatial variation– Spectrum becomes steeper with radius– Little difference between chips at the same radius
• (with the exception of the “hot” spot on MOS1-2)
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Soft Proton Flares
• If fitting multiple regions (i.e., a cluster)– The normalization of the S.P. contamination must
be allowed to vary with radius.– The spectral shape of the S.P. contamination must
be allowed to vary with radius.
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Application
• How good is the method?– Small number statistics for corner data– Marginal statistics for FWC corrections– Introducing systematics at low energies?
• Test– Find directions with multiple obsids
• Divide MOS1 and MOS2 spectra by responses
• Sum and smooth
• Compare
• Chose 8 high b fields with a total of 40 obsids
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Application
• Lowest spectrum set as fiducial– Dotted = uncertainty in difference
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Application
• Lowest spectrum set as fiducial
• Statistics: of 40 obsids– 5-8 show signs of SWCX– 1 shows strong SPC– Several show minor SPC
• Differences between spectra (other than those due to SWCX) consistent within uncertainties
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Application
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Application