标题： X射线源分类使用机器学习：EP-WXT路径探测器LEIA研究 显示英文标题

标题： X-ray Sources Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

摘要： X射线观测在时域天文学中起着至关重要的作用。 爱因斯坦探测器（EP），一颗最近发射的X射线天文卫星，成为时域天文学和高能天体物理学领域的领先者。 专注于软X射线波段的系统调查，EP旨在发现高能瞬变源并在宇宙中监测可变源。 为了实现这些目标，对观测源进行快速可靠的分类是必不可少的。 在本研究中，我们开发了一个机器学习分类器，用于使用EP-WXT天文用鱼眼成像仪（LEIA）和EP-WXT模拟数据进行自主源分类。 所提出的随机森林分类器，基于从光变曲线、能量谱和位置信息中提取的选定特征，在EP模拟数据上达到了约95%的准确率，在LEIA观测数据上达到了98%。 该分类器被集成到LEIA数据处理流程中，作为观测期间手动验证和快速分类的工具。 本文提出了一种基于单次观测的X射线源分类方法，并讨论了对该任务最有效特征的含义。 这项工作有助于EP任务的快速源分类，同时也为特征选择和分类技术提供了有价值的见解，以提高X射线源分类的效率和准确性，并可适用于其他X射线望远镜数据。 显示英文摘要

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.