论文标题
一种机器学习方法，用于根据其紧凑的对象性质对低质量X射线二进制物进行分类
A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature
论文作者
论文摘要
低质量X射线二进制文件（LMXB）是二进制系统，其中一个组件是黑洞或中子恒星，而另一个则是一个较小的恒星。明确确定LMXB是托有黑洞还是中子星是一个挑战。在过去的几十年中，多次观察工作以不同的成功来解决这个问题。在本文中，我们探讨了使用机器学习来应对这一观察挑战的使用。我们训练一个随机的森林分类器，使用从Rossi X射线正时探索器档案中获得的能量范围5-25 keV中的能量范围识别紧凑对象的类型。我们报告在分类LMXB源的光谱时的平均准确度为87 +/- 13。我们进一步使用训练有素的模型来预测具有未知或模棱两可分类的LMXB系统的类。随着从现在和即将执行的任务（例如Swift，Xmm-Newton，Xarm，Athena，Nicer）中X射线域中不断增加的天文数据量，此类方法对于更快且可靠的X射线源分类非常有用，并且也可以作为数据减少管道的一部分部署。
Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87+/-13 in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.
