Source detection and background estimation with Bayesian inference

A probabilistic technique for the joint estimation of background and sources in high-energy astrophysics is described. Bayesian inference is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. The present analysis is applied on ROSAT PSPC data in Survey Mode. A background map is modelled using a Thin-Plate spline. Source probability maps are obtained for each pixel (45 arcsec) independently and for larger correlation lengths, revealing faint and extended sources. Source probability maps are combined for two ROSAT PSPC energy bands, hard (0.5-2.0 keV) and soft (0.1-0.5 keV), and compared with the corresponding source probability maps at the broad energy band (0.1-2.4 keV) and with the ROSAT All-Sky Survey (RASS) catalogues, bright and faint. The probabilistic method allows for detection improvement of faint extended celestial sources compared to the standard methods applied for the realization of the RASS catalogues.