Multiwavelength Analysis and Machine Learning Classification of X-ray Sources in Several Open Clusters

Open clusters are excellent laboratories to explore various aspects of stellar physics. Younger clusters offer opportunities to search for remnants of high mass stars, including pulsars, and neutron star and black hole systems in binaries, while older clusters allow us to explore the aging of populations of binaries with compact objects, and study the evolution of the X-ray activity-rotation-age relation for lower mass stars. In addition, one can find more exotic systems, such as gamma-Cas, symbiotic, or gamma-ray (colliding-wind) binaries. In this work, we perform multi-wavelength analysis and classification of X-ray sources detected by Chandra X-ray Observatory (CXO) in several open clusters with ages spanning the range from few Myrs to few hundred Myrs. We use a supervised machine-learning approach to classify X-ray sources in these clusters using multiwavelength information. As expected, most of the confidently classified sources are pre-main sequence stars in younger clusters, and low mass stars in older clusters. However, we also found and analyzed several confidently classified candidate compact objects, and other interesting sources.