中国空间站望远镜吸积中子星双星候选体的完备性
The Completeness of Accreting Neutron Star Binary Candidates from the Chinese Space Station Telescope
AI总结：
本文利用双星演化与机器学习方法研究中国空间站望远镜吸积中子星双星候选体样本的完备性，发现召回率受物质转移率、伴星质量和系统年龄影响，指出需进行完备性修正才能获得完整样本。
摘要：
中子星（NS）具有许多极端物理条件，人们可以通过吸积中子星双星（ANSB）系统获得关于中子星的一些重要信息。即将投入使用的中国空间站望远镜（CSST）为搜寻大量ANSB候选体提供了机会。我们的目标是检验来自CSST数据的潜在ANSB样本的完备性。在本文中，我们通过双星演化和双星星族合成方法，在CSST测光系统下生成了一些ANSB和普通双星，并使用机器学习方法训练了一个分类模型。尽管我们的机器学习模型的精确率（94.56%）与之前的研究一样高，但召回率仅约为63.29%。精确率/召回率主要由中子星与其伴星之间的物质转移率决定。此外，我们还发现，通过机器学习方法从CSST测光数据中获得的ANSB样本的完备性也取决于伴星质量和系统年龄。具有低初始质量伴星（0.1至1倍太阳质量）的ANSB候选体具有相对较高的精确率（94.94%）和较高的召回率（86.32%），而具有较高初始质量伴星（1.1至3倍太阳质量）的ANSB候选体具有相似的精确率（93.88%）和相当低的召回率（42.67%）。我们的结果表明，尽管机器学习方法可能获得相对纯净的ANSB样本，但要获得完整样本，必须进行完备性修正。
英文摘要：
Neutron star (NS) has many extreme physical conditions, and one may obtain some important informations about 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 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 low initial mass companion star ($0.1~ {\rm M}_\odot$ to $1~ {\rm M}_\odot$) have a relatively high Precision ($94.94~ \%$) and high Recall ($86.32~ \%$), whereas ANSB candidates with higher initial mass companion star ($1.1~ {\rm M}_\odot$ to $3~ {\rm M}_\odot$) have similar Precision ($93.88~ \%$) and quite low Recall ($42.67~ \%$). Our results indicate that although the machine learning method may obtain a relative pure sample of ANSBs, a completeness correction is necessary for one to obtain a complete sample.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
