Population of X-ray Sources in the Intermediate-Age Cluster NGC 3532: a Test Bed for Machine-Learning Classification
Steven Chen, Oleg Kargaltsev, Hui Yang, Jeremy Hare, Igor Volkov,, Blagoy Rangelov, John Tomsick

TL;DR
This study uses Chandra X-ray data and machine learning to classify X-ray sources in the 300-Myr-old open cluster NGC 3532, identifying stellar types and searching for compact objects.
Contribution
It applies a novel machine-learning pipeline to classify X-ray sources in an open cluster, enhancing understanding of stellar populations and compact object absence.
Findings
Most X-ray sources are coronally-active low-mass stars.
Several early-type stars are likely binaries with X-ray emission.
No compact objects were detected within the cluster down to the flux limit.
Abstract
Open clusters are thought to be the birth place of most stars in the Galaxy. Thus, they are excellent laboratories for investigating stellar evolution, and X-ray properties of various types of stars (including binary stars, evolved stars, and compact objects). In this work, we investigate the population of X-ray sources in the nearby 300-Myr-old open cluster NGC 3532 using Chandra X-ray Observatory and multi-wavelength data from several surveys. We apply a random-forest machine-learning pipeline (MUWCLASS) to classify all confidently detected X-ray sources (S/N) in the field of NGC 3532. We also perform a more detailed investigation of brighter sources, including their X-ray spectra and lightcurves. Most X-ray sources are confirmed as coronally-active low-mass stars, many of which are confidently identified by MUWCLASS. Several late B or early A-type \textbf{stars} are relatively…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
