首页|X-Ray Source Classification Using Machine Learning:A Study with EP-WXT Pathfinder LEIA

X-Ray Source Classification Using Machine Learning:A Study with EP-WXT Pathfinder LEIA

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X-ray observations play a crucial role in time-domain astronomy.The Einstein Probe(EP),a recently launched X-ray astronomical satellite,emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics.With a focus on systematic surveys in the soft X-ray band,EP aims to discover high-energy transients and monitor variable sources in the universe.To achieve these objectives,a quick and reliable classification of observed sources is essential.In this study,we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy(LEIA)and EP-WXT simulations.The proposed Random Forest classifier,built on selected features derived from light curves,energy spectra,and location information,achieves an accuracy of approximately 95％on EP simulation data and 98％on LEIA observational data.The classifier is integrated into the LEIA data processing pipeline,serving as a tool for manual validation and rapid classification during observations.This paper presents an efficient method for the classification of X-ray sources based on single observations,along with implications of most effective features for the task.This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

methods:data analysisX-rays:binariesstars:variables:generalX-rays:bursts

Xiaoxiong Zuo、Yihan Tao、Yuan Liu、Yunfei Xu、Wenda Zhang、Haiwu Pan、Hui Sun、Zhen Zhang、Chenzhou Cui、Weimin Yuan

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National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China

University of Chinese Academy of Sciences,Beijing 101049,China

National Astronomical Data Center,Beijing 100101,China

National Key Research and Development Program of ChinaNational Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)National Natural Science Foundation of China(NSFC)Strategic Priority Research Program of the Chinese Academy of SciencesStrategic Priority Research Program of the Chinese Academy of SciencesStrategic Priority Research Program of the Chinese Academy of SciencesStrategic Priority Research Program of the Chinese Academy of SciencesChina National Astronomical Data Center(NADC)Chinese Virtual Observatory(China-VO)Astronomical Big Data Joint Research CenterNational Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud

2022YFF071150012373110122730771210307012333004XDA15310300XDB0550200XDB0550100XDB0550000

2024

10.1088/1674-4527/ad634f

天文和天体物理学研究
中国科学院国家天文台

天文和天体物理学研究

CSTPCD
影响因子：0.406
ISSN：1674-4527
年,卷(期)：2024.24(8)