pyBLoCXS: Bayesian Low-Counts X-ray Spectral Analysis in Sherpa

Siemiginowska, Aneta

Typical X-ray spectra obtained with current missions have low counts and should be modeled with the Poisson distribution. However, chi2 statistic is often used as an alternative and the data are assumed to follow the Gaussian distribution. In order to be able to apply chi2 statistic a variety of weights to the statistic or a binning of the data have been usually performed. Such modifications introduce biases in data modeling or/and a loss of information. Standard modeling packages such as XSPEC and Sherpa provide the Poisson likelihood and allow computation of rudimentary MCMC chains], but so far do not allow for setting a full Bayesian model.

We have implemented a sophisticated Bayesian MCMC-based algorithm to carry out spectral fitting of low counts sources in the Sherpa environment. The code is a Python extension to Sherpa and allows to fit any predefined Sherpa model to high-energy X-ray spectral data. We present the algorithm and discuss several issues related to the implementation, including flexible definition of priors and allowing for variations in the calibration information.

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