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How to handle calibration uncertaintiesin high-energy astrophysics
Vinay Kashyap
SPIE 2008 :: Observatory Operations :: 7016-23 :: 16:20 Wednesday 25 June 2008
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How to handle calibration uncertaintiesin high-energy astrophysics
Vinay Kashyap
CHASC Astrostatistical CollaborationChandra X-ray Center
Smithsonian Astrophysical Observatory
Hyunsook Lee, Aneta Siemiginowska,Jonathan McDowell, Arnold Rots,
Jeremy Drake, Pete Ratzlaff,Andreas Zezas, Doug Burke,Rima Izem, Alanna Connors,
David van Dyk, Taeyoung Park
SPIE 2008 :: Observatory Operations :: 7016-23 :: 16:20 Wednesday 25 June 2008
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bottom line
there is now a way
to include calibration uncertainty
in astrophysical data analysis
in a flexible way
for any instrument, mission, or detector.
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Part Icalibration uncertainty
in data analysis
Part IIpractical issues of
storage, retrieval, flexibility
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The three most important effects that affect data analysis
astrophysical model uncertainty
statistical uncertainty (measurement error)
calibration uncertainty (systematic error)
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examples of calibration uncertainty
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power-law residuals with current calibration
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power-law residuals with 20Å contamination overlayer
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(E,p,t) : photon energy, location, arrival time
(E’,p’) : detector channel, chip location
S : astrophysical source model
R : energy redistribution function (RMF)
P : position redistribution function (PSF)
A : effective area (ARF)
M : predicted model counts
general model of HEA data
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effect on model parameter uncertainty
Pro
babi
lity
dens
ity
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but how exactly?
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MCMC
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DATA CALIBRATION
Draw parameters
Update parameters
Compute likelihood
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DATA CALIBRATION
Draw parameters
Compute likelihood
Draw effective areas
Update parameters
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but where do the effective areas come from?
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Principal Components
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new nominal bias
PCs
~N (0,1)
residual
eigenvalue
eigenvector
A = A0 + bias + components + residual
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Part IIpractical issues of
storage, retrieval, flexibility
A = A0 + bias + components + residual
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A = A0 + bias + components + residual
store in sameformat as A0
case specificsecondary FITS extension
e.g.,SPECRESP
e.g.,PCA1DSIMS
POLY1DPCPC
MULTISCALE
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what have we got so far?
• realistic error bars• implemented in BLoCXS• 500x speed up in analysis• Sherpa on the way
• unified file format for use in XSPEC/Sherpa• 100x drop in storage• generalize to any instrument• extendable to model lacunae• roll your own
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summary
there are a number of steps between a calibration
scientist saying “the error on the effective area is X%
at energy Y” to then have it fold into a spectral model
fit and inflate the error bars on the parameters, and
we believe that we have connected the dots.
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uncertainty in energy response
QuickTime™ and aYUV420 codec decompressor
are needed to see this picture.
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what’s next?
unified file format implemented in XSPEC/Sherpa
other schemes of dimensionality reduction
RMFs: 2D PCA, within and between PCA
PSFs: multiscale residuals