Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources

• URL: http://arxiv.org/abs/2401.12203v1
• Date: Mon, 22 Jan 2024 18:42:31 GMT
• Title: Unsupervised Machine Learning for the Classification of Astrophysical X-ray Sources
• Authors: V\'ictor Samuel P\'erez-D\'iaz, Juan Rafael Mart\'inez-Galarza, Alexander Caicedo, Raffaele D'Abrusco
• Abstract summary: We develop an unsupervised machine learning approach to provide probabilistic classes to Chandra Source Catalog sources. We provide a catalog of probabilistic classes for 8,756 sources, comprising a total of 14,507 detections. We investigate the consistency between the distribution of features among classified objects and well-established astrophysical hypotheses.
• Score: 44.99833362998488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 https://umlcaxs-playground.streamlit.app.

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