Population of X-Ray Sources in the Intermediate-age Cluster NGC 3532: a Test Bed for Machine-learning Classification

Author:

Chen StevenORCID,Kargaltsev OlegORCID,Yang HuiORCID,Hare JeremyORCID,Volkov Igor,Rangelov BlagoyORCID,Tomsick JohnORCID

Abstract

Abstract Open clusters are thought to be the birthplace 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 multiwavelength data from several surveys. We apply a random-forest machine-learning pipeline (MUWCLASS) to classify all confidently detected X-ray sources (signal-to-noise ratio, hereafter S/N, > 5) 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 stars are relatively bright in X-rays, most of which are likely binaries. We do not find any compact objects among X-ray sources reliably associated with NGC 3532, down to the limiting X-ray flux of ∼2 × 10−15 erg s−1cm−2, corresponding to L X ∼ 6 × 1028 erg s−1 at the cluster’s distance. We also identify several Galactic sources beyond NGC 3532 that differ from typical coronally active stars, and were classified by MUWCLASS as potential compact objects. Detailed investigation reveals that these sources may indeed belong to rarer classes, and deserve follow-up observations.

Funder

SI ∣ Smithsonian Astrophysical Observatory

National Aeronautics and Space Administration

Publisher

American Astronomical Society

Subject

Space and Planetary Science,Astronomy and Astrophysics

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Unsupervised machine learning for the classification of astrophysical X-ray sources;Monthly Notices of the Royal Astronomical Society;2024-01-24

2. Chandra X-Ray Observatory Observations of 13 Fermi LAT Sources;The Astrophysical Journal;2024-01-01

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