Highly Variable Quasar Candidates Selected from 4XMM-DR13 with Machine Learning

Highly Variable Quasar Candidates Selected from 4XMM-DR13 with Machine Learning

We present a sample of 12 quasar candidates with highly variable soft X-ray emission, selected from the fourth XMM-Newton Serendipitous Source Catalog (4XMM-DR13), using random forest (RF). Optical to mid-IR photometric data for the 4XMM-DR13 sources were obtained by correlating the sample with the...

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Bibliographic Details
Main Authors:	Heng Wang, Yanli Ai, Yanxia Zhang, Yuming Fu, Wenfeng Wen, Liming Dou, Xue-Bing Wu, Xiangru Li, Zhiying Huo
Format:	Article
Language:	English
Published:	IOP Publishing 2025-01-01
Series:	The Astrophysical Journal
Subjects:	Quasars X-ray quasars
Online Access:	https://doi.org/10.3847/1538-4357/adc7b8
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