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

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

机构地区：[1]National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]National Astronomical Data Center,Beijing 100101,China

出　　处：《Research in Astronomy and Astrophysics》2024年第8期175-195,共21页天文和天体物理学研究（英文版）

基　　金：supported by the National Key Research and Development Program of China(2022YFF0711500);National Natural Science Foundation of China(NSFC,grant Nos.12373110,12273077,12103070,and 12333004);the Strategic Priority Research Program of the Chinese Academy of Sciences(grant Nos.XDA15310300,XDB0550200,XDB0550100,and XDB0550000);supported by China National Astronomical Data Center(NADC);Chinese Virtual Observatory(China-VO);supported by Astronomical Big Data Joint Research Center,cofounded by National Astronomical Observatories,Chinese Academy of Sciences and Alibaba Cloud。

摘　　要：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.

关 键 词：methods data analysis-X-rays binaries-stars VARIABLES general-X-rays BURSTS 

分 类 号：P172.2[天文地球—天文学]