Speaker
Description
The eROSITA all-sky survey mission has the potential to significantly advance our understanding of Galactic X-ray binaries (XRBs). Given its high sensitivity we identified nearly 200 new XRB candidates, including both high-mass and low-mass systems, and even some peculiar case sources. We present key methods used to handle eRASS DR1 dataset and classify sources, such as counterpart identification and multi-wavelength (MWL) machine learning (ML) analysis, tuned for search of XRBs. The results of this work are also used to obtain the first self-consistent estimate of XRB numbers observable by eROSITA, expand the sample of low-luminosity XRBs, give a basis for follow-up observations and accretion physics studies at low states. Final goal of the project is to provide better constraints on the characteristics of XRB population in the Milky Way.
