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.

中文翻译：

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基于其紧凑对象性质对低质量 X 射线二进制文件进行分类的机器学习方法

低质量 X 射线双星 (LMXB) 是双星系统，其中一个组件是黑洞或中子星，另一个是质量较小的恒星。明确确定 LMXB 是黑洞还是中子星具有挑战性。在过去的几十年里，多项观察工作试图解决这个问题，但取得了不同程度的成功。在本文中，我们探索使用机器学习来应对这一观察挑战。我们训练一个随机森林分类器，使用从 Rossi X-ray Timing Explorer 档案中获得的 5-25 keV 能量范围内的能谱来识别紧凑物体的类型。我们报告对 LMXB 源光谱进行分类的平均准确度为 87+/-13。我们进一步使用经过训练的模型来预测具有未知或模糊分类的 LMXB 系统的类别。随着来自当前和即将到来的任务（例如，SWIFT、XMM-Newton、XARM、ATHENA、NICER）的 X 射线域中的天文数据量不断增加，此类方法对于更快、更稳健地分类 X-射线非常有用。射线源，也可以作为数据缩减管道的一部分进行部署。