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Searching for Unusual Sources in the Chandra Source Catalog

Presentation #108.07 in the session “Missions and Instruments (Poster)”.

Published onApr 01, 2022
Searching for Unusual Sources in the Chandra Source Catalog

Machine learning techniques are useful tools for sifting through large astronomical datasets. We apply a principal component analysis (PCA) and an unsupervised Random Forest (uRF) to the second release of the Chandra Source Catalog (CSC 2) to search for unusual X-ray sources. The CSC2 contains ~317,000 X-ray sources observed by NASA’s Chandra X-ray Observatory (CXO) through 2014. We limit our analysis to high-significance (detection significance ≥ 7.5) sources to ensure that existing observations can offer a “first look” follow-up. We found 119 sources that were consistently identified as outlier sources by the uRF across 100 applications of the algorithm, many of which are well-observed but have not been individually investigated.

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