The Completeness of Accreting Neutron Star Binary Candidates from the Chinese Space Station Telescope  

作　　者：Hao Shen Shun-Yi Lan Xiang-Cun Meng 

机构地区：[1]Yunnan Observatories,Chinese Academy of Sciences,Kunming 650216,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]International Centre of Supernovae,Yunnan Key Laboratory,Kunming 650216,China

出　　处：《Research in Astronomy and Astrophysics》2024年第9期28-36,共9页天文和天体物理学研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.12288102 and 12333008);the National Key R&D Program of China(No.2021YFA1600403);support from the International Centre of Supernovae,Yunnan Key Laboratory(No.202302AN360001);the Yunnan Revitalization Talent Support Program-Science&Technology Champion Project(No.202305AB350003);the Yunnan Fundamental Research Projects(Nos.202401BC070007 and 202201B C070003);the science research grants from the China Manned Space Project。

摘　　要：A neutron star(NS)has many extreme physical conditions,and one may obtain some important information about an NS via accreting neutron star binary(ANSB)systems.The upcoming Chinese Space Station Telescope(CSST)provides an opportunity to search for a large sample of ANSB candidates.Our goal is to check the completeness of the potential ANSB samples from CSST data.In this paper,we generate some ANSBs and normal binaries under the CSST photometric system by binary evolution and binary population synthesis method and use a machine learning method to train a classification model.Although the Precision(94.56%)of our machine learning model is as high as before study,the Recall is only about 63.29%.The Precision/Recall is mainly determined by the mass transfer rate between the NSs and their companions.In addition,we also find that the completeness of ANSB samples from CSST photometric data by the machine learning method also depends on the companion mass and the age of the system.ANSB candidates with a low initial mass companion star(0.1 M_(⊙)to 1 M_(⊙))have a relatively high Precision(94.94%)and high Recall(86.32%),whereas ANSB candidates with a higher initial mass companion star(1.1 M_(⊙)to 3 M_(⊙))have similar Precision(93.88%)and quite low Recall(42.67%).Our results indicate that although the machine learning method may obtain a relatively pure sample of ANSBs,a completeness correction is necessary for one to obtain a complete sample.

关 键 词：stars:neutron X-rays:binaries methods:analytical 

分 类 号：P145.6[天文地球—天体物理]