Accounting for calibration uncertainties in X-ray analysis: Effective areas in spectral fitting

Hyunsook Lee, Vinay L. Kashyap, David A. Van Dyk, Alanna Connors, Jeremy J. Drake, Rima Izem, Xiao Li Meng, Shandong Min, Taeyoung Park, Pete Ratzlaff, Aneta Siemiginowska, Andreas Zezas

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.

Original languageEnglish
Article number126
JournalAstrophysical Journal
Volume731
Issue number2
DOIs
Publication statusPublished - 2011 Apr 20

Fingerprint

X-ray spectroscopy
spectrum analysis
calibration
Markov chains
principal components analysis
files
x rays
spectral analysis
simulation
Markov chain
method
energy
principal component analysis
analysis

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

Lee, H., Kashyap, V. L., Van Dyk, D. A., Connors, A., Drake, J. J., Izem, R., ... Zezas, A. (2011). Accounting for calibration uncertainties in X-ray analysis: Effective areas in spectral fitting. Astrophysical Journal, 731(2), [126]. https://doi.org/10.1088/0004-637X/731/2/126
Lee, Hyunsook ; Kashyap, Vinay L. ; Van Dyk, David A. ; Connors, Alanna ; Drake, Jeremy J. ; Izem, Rima ; Meng, Xiao Li ; Min, Shandong ; Park, Taeyoung ; Ratzlaff, Pete ; Siemiginowska, Aneta ; Zezas, Andreas. / Accounting for calibration uncertainties in X-ray analysis : Effective areas in spectral fitting. In: Astrophysical Journal. 2011 ; Vol. 731, No. 2.
@article{8f356b36b1fa4ef8a18067b9cf95b654,
title = "Accounting for calibration uncertainties in X-ray analysis: Effective areas in spectral fitting",
abstract = "While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.",
author = "Hyunsook Lee and Kashyap, {Vinay L.} and {Van Dyk}, {David A.} and Alanna Connors and Drake, {Jeremy J.} and Rima Izem and Meng, {Xiao Li} and Shandong Min and Taeyoung Park and Pete Ratzlaff and Aneta Siemiginowska and Andreas Zezas",
year = "2011",
month = "4",
day = "20",
doi = "10.1088/0004-637X/731/2/126",
language = "English",
volume = "731",
journal = "Astrophysical Journal",
issn = "0004-637X",
publisher = "IOP Publishing Ltd.",
number = "2",

}

Lee, H, Kashyap, VL, Van Dyk, DA, Connors, A, Drake, JJ, Izem, R, Meng, XL, Min, S, Park, T, Ratzlaff, P, Siemiginowska, A & Zezas, A 2011, 'Accounting for calibration uncertainties in X-ray analysis: Effective areas in spectral fitting', Astrophysical Journal, vol. 731, no. 2, 126. https://doi.org/10.1088/0004-637X/731/2/126

Accounting for calibration uncertainties in X-ray analysis : Effective areas in spectral fitting. / Lee, Hyunsook; Kashyap, Vinay L.; Van Dyk, David A.; Connors, Alanna; Drake, Jeremy J.; Izem, Rima; Meng, Xiao Li; Min, Shandong; Park, Taeyoung; Ratzlaff, Pete; Siemiginowska, Aneta; Zezas, Andreas.

In: Astrophysical Journal, Vol. 731, No. 2, 126, 20.04.2011.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Accounting for calibration uncertainties in X-ray analysis

T2 - Effective areas in spectral fitting

AU - Lee, Hyunsook

AU - Kashyap, Vinay L.

AU - Van Dyk, David A.

AU - Connors, Alanna

AU - Drake, Jeremy J.

AU - Izem, Rima

AU - Meng, Xiao Li

AU - Min, Shandong

AU - Park, Taeyoung

AU - Ratzlaff, Pete

AU - Siemiginowska, Aneta

AU - Zezas, Andreas

PY - 2011/4/20

Y1 - 2011/4/20

N2 - While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.

AB - While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can be applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.

UR - http://www.scopus.com/inward/record.url?scp=79955067386&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79955067386&partnerID=8YFLogxK

U2 - 10.1088/0004-637X/731/2/126

DO - 10.1088/0004-637X/731/2/126

M3 - Article

AN - SCOPUS:79955067386

VL - 731

JO - Astrophysical Journal

JF - Astrophysical Journal

SN - 0004-637X

IS - 2

M1 - 126

ER -