Misc,
Systematic errors in the maximum likelihood regression of Poisson count data: introducing the overdispersed chi-square distribution
(2023)cite arxiv:2302.04011Comment: MNRAS accepted, MN-22-4879-MJ.R2.
Abstract
This paper presents a new method to estimate systematic errors in the
maximum-likelihood regression of count data. The method is applicable in
particular to X-ray spectra in situations where the Poisson log-likelihood, or
the Cash goodness-of-fit statistic, indicate a poor fit that is attributable to
overdispersion of the data. Overdispersion in Poisson data is treated as an
intrinsic model variance that can be estimated from the best-fit model, using
the maximum-likelihood Cmin statistic. The paper also studies the effects of
such systematic errors on the Delta C likelihood-ratio statistic, which can be
used to test for the presence of a nested model component in the regression of
Poisson count data. The paper introduces an overdispersed chi-square
distribution that results from the convolution of a chi-square distribution
that models the usual Delta C statistic, and a zero-mean Gaussian that models
the overdispersion in the data. This is proposed as the distribution of choice
for the Delta C statistic in the presence of systematic errors. The methods
presented in this paper are applied to XMM-Newton data of the quasar 1ES
1553+113 that were used to detect absorption lines from an intervening warm-hot
intergalactic medium (WHIM). This case study illustrates how systematic errors
can be estimated from the data, and their effect on the detection of a nested
component, such as an absorption line, with the Delta C statistic.
