Accounting for Calibration Uncertainties in X-ray Analysis: Effective Areas in Spectral Fitting

TL;DR

This paper introduces statistical methods to incorporate calibration uncertainties into X-ray spectral analysis, improving accuracy and reliability of astrophysical parameter estimation.

Contribution

It presents both an approximate multiple imputation method and an exact Bayesian approach for accounting calibration uncertainties in spectral fitting.

Findings

The methods are applicable to high-energy astrophysics data.

Implementation with Chandra data demonstrates effectiveness.

Procedures can be generalized to other calibration uncertainties.

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.