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arXiv 2201.12209astro-ph.SRastro-ph.GAastro-ph.IM
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利用机器学习方法从档案测光数据早期识别微引力透镜事件

Early recognition of Microlensing Events from Archival Photometry with Machine Learning Methods

I. Gezer, Ł. Wyrzykowski, P. Zieliński, G. Marton, K. Kruszyńska, K. A. Rybicki, N. Ihanec, M. Jabłońska, O. Ziółkowska

AI总结:

本文利用监督机器学习方法,基于Gaia、2MASS和AllWISE档案多波段数据构建分类器,在90%概率阈值下正确识别了120个Gaia警报微引力透镜事件中的三分之一,并分类了368个候选体中的38个,为快速评估银河系瞬变源分类提供了通用工具。

摘要:

引力微引力透镜方法是探测银河系中孤立黑洞的有力方法。在微引力透镜事件期间,源星的亮度会增加,这一特征被许多测光巡天用于发出潜在事件的警报。典型的微引力透镜事件表现出特征性的光变曲线,然而,一些爆发现象的变星可能表现出与微引力透镜事件相似的光变曲线,尤其是在观测采样频率不够密集的情况下。我们的目标是设计一种方法,仅利用档案多波段测光数据,将微引力透镜事件的候选体与其他类型的警报区分开来。在微引力透镜事件搜索中最常见的污染源是经典Be型星、年轻恒星天体以及渐近巨分支星(如米拉变星)。我们利用来自Gaia、2MASS和AllWISE星表的数千个主要警报恒星类别的示例构建了训练集,结合了光学到中红外的星等。我们使用监督机器学习技术构建模型对警报进行分类。我们在Gaia科学警报报告的120个微引力透镜事件上验证了我们的方法,这些事件已经过光谱和测光研究。仅使用档案信息,在90%概率阈值下,我们正确识别了三分之一的微引力透镜事件。我们还对368个被标记为微引力透镜事件潜在候选体的Gaia警报位置运行了分类器。在90%概率阈值下,我们分类出38个微引力透镜事件和29个其他类型的变星。我们开发的机器学习支持方法可普遍用于当前和未来的警报巡天,以快速评估银河系瞬变源的分类,并帮助决定是否进行后续观测。

英文摘要:

Gravitational microlensing method is a powerful method to detect isolated black holes in the Milky Way. During a microlensing event brightness of the source increases and this feature is used by many photometric surveys to alert on potential events. A typical microlensing event shows a characteristic light curve, however, some outbursting variable stars may show similar light curves to microlensing events especially when the cadence of observations is not dense enough. Our aim is to device a method for distinguishing candidates for microlensing events from any other types of alerts using solely their archival photometric multi-wavelength data. The most common contaminants in the microlensing event search are Classical Be-type stars, Young Stellar Objects and Asymptotic Giant Branch stars such as Miras. We build a training set using thousands of examples for the main classes of alerting stars combining optical to mid-infrared magnitudes from Gaia, 2MASS and AllWISE catalogues. We used supervised machine learning techniques to build models for classification of alerts. We verified our method on 120 microlensing events reported by Gaia Science Alerts which were studied spectroscopically and photometrically. With the use of only archival information at 90% probability threshold we correctly identified one-third of the microlensing events. We also run our classifier on positions of 368 Gaia alerts which were flagged as potential candidates for microlensing events. At the 90% probability threshold we classified 38 microlensing events and 29 other types of variables. The machine learning supported method we developed can be universally used for current and future alerting surveys in order to quickly assess the classification of galactic transients and help decide on further follow-up observations.

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。

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