The automatic classification of X-ray detections is a necessary step in
extracting astrophysical information from compiled catalogs of astrophysical
sources. Classification is useful for the study of individual objects,
statistics for population studies, as well as for anomaly detection, i.e., the
identification of new unexplored phenomena, including transients and spectrally
extreme sources. Despite the importance of this task, classification remains
challenging in X-ray astronomy due to the lack of optical counterparts and
representative training sets. We develop an alternative methodology that
employs an unsupervised machine learning approach to provide probabilistic
classes to Chandra Source Catalog sources with a limited number of labeled
sources, and without ancillary information from optical and infrared catalogs.
We provide a catalog of probabilistic classes for 8,756 sources, comprising a
total of 14,507 detections, and demonstrate the success of the method at
identifying emission from young stellar objects, as well as distinguishing
between small-scale and large-scale compact accretors with a significant level
of confidence. We investigate the consistency between the distribution of
features among classified objects and well-established astrophysical hypotheses
such as the unified AGN model. This provides interpretability to the
probabilistic classifier. Code and tables are available publicly through
GitHub. We provide a web playground for readers to explore our final
classification at this https URL

通过无监督机器学习方法，我们为 Chandra Source Catalog 的源提供了概率分类，并展示了该方法在识别年轻恒星物体的发射以及区分小尺度和大尺度紧凑吸积源方面的成功，为这种概率分类器提供了可解释性。