BAYESX: a Bayesian inference tool for the analysis of Xray observations of galaxy clusters
Abstract
We present the first public release of our Bayesian inference tool, BAYESX, for the analysis of Xray observations of galaxy clusters. We illustrate the use of BAYESX by analysing a set of four simulated clusters at z = 0.20.9 as they would be observed by a Chandralike Xray observatory. In both the simulations and the analysis pipeline we assume that the dark matter density follows a spherically symmetric Navarro, Frenk and White (NFW) profile and that the gas pressure is described by a generalized NFW (GNFW) profile. We then perform four sets of analyses. These include prioronly analyses and analyses in which we adopt wide uniform prior probability distributions on f_{g}(r_{200}) and on the model parameters describing the shape and slopes of the GNFW pressure profile, namely (c_{500}, a, b, c). By numerically exploring the joint probability distribution of the cluster parameters given simulated Chandralike data, we show that the model and analysis technique can robustly return the simulated cluster input quantities, constrain the cluster physical parameters and reveal the degeneracies among the model parameters and cluster physical parameters. We then use BAYESX to analyse Chandra data on the nearby cluster, A262, and derive the cluster physical and thermodynamic profiles. The results are in good agreement with other results given in literature for the cluster. To illustrate the performance of the Bayesian model selection, we also carried out analyses assuming an Einasto profile for the matter density and calculated the Bayes factor. The results of the model selection analyses for the simulated data favour the NFW model as expected. However, we find that the Einasto profile is preferred in the analysis of A262. The BAYESX software, which is implemented in Fortran 90, is available at http://www.mrao.cam.ac.uk/facilities/software/bayesx/.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 January 2015
 DOI:
 10.1093/mnras/stu2146
 arXiv:
 arXiv:1310.1885
 Bibcode:
 2015MNRAS.446.1799O
 Keywords:

 methods: data analysis;
 cosmology: observations;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 22 pages, 11 figures