Bayesian Estimation of Hardness Ratios: Modeling and Computations

Abstract

A commonly used measure to summarize the nature of a photon spectrum is the so-called Hardness Ratio, which compares the number of counts observed in different passbands. The hardness ratio is especially useful to distinguish between and categorize weak sources as a proxy for detailed spectral fitting. However, in this regime classical methods of error propagation fail, and the estimates of spectral hardness become unreliable. Here we develop a rigorous statistical treatment of hardness ratios that properly deals with detected photons as independent Poisson random variables and correctly deals with the non-Gaussian nature of the error propagation. The method is Bayesian in nature, and thus can be generalized to carry out a multitude of source-population--based analyses. We verify our method with simulation studies, and compare it with the classical method. We apply this method to real world examples, such as the identification of candidate quiescent Low-mass X-ray binaries in globular clusters, and tracking the time evolution of a flare on a low-mass star.