Probabilistic classification of X-ray sources applied to Swift-XRT and XMM-Newton catalogs
This work proposes a revisited naive Bayes classification of the X-ray sources in the Swift-XRT and XMM-Newton catalogs into four classes — AGN, stars, X-ray binaries (XRBs) and cataclysmic variables (CVs) — based on their spatial, spectral and timing properties and their multiwavelength counterparts. An outlier measure is used to identify objects of other natures. The classifier is optimized to maximize the classification performance of a chosen class (here XRBs) and it is adapted to data mining purposes.
More info: https://ui.adsabs.harvard.edu/abs/2021arXiv211101489T/abstract
