arXiv 2008.119302020-08-27astro-ph.IMastro-ph.EPastro-ph.SRcs.LG

使用神经网络识别微透镜事件

Identifying microlensing events using neural networks

California Institute of Technology（加州理工学院）

Przemek Mroz

备注：: submitted
发表信息：: Acta Astron., 70, 169 (2020)
DOI：: 10.32023/0001-5237/70.3.1

摘要：

目前的引力微透镜调查正在观察银河系隆起中的数亿颗恒星-这使得寻找罕见的微透镜事件成为一项具有挑战性的任务。在几乎所有以前的作品中，微透镜事件都是通过应用非常严格的选择切割或手动检查数万条光变曲线来检测的。然而，在未来的空间微透镜实验中预期的微透镜事件的数量迫使我们考虑全自动的方法。它们对于选择通常表现出复杂的光变曲线形态并且难以找到的二元透镜事件特别重要。文献中没有二元镜片事件的专用选择算法，这妨碍了其统计研究。在这里，我们提出了两个简单的基于神经网络的分类器，用于检测单个和二进制微透镜事件。我们使用OGLE-III和OGLE-IV数据集证明了它们的鲁棒性，并显示它们在Zwicky瞬态设施（ZTF）的数据中检测到的微透镜事件上表现良好。分类器能够正确识别约98%的单镜头事件和80-85%的双镜头事件。

英文摘要：

Current gravitational microlensing surveys are observing hundreds of millions of stars in the Galactic bulge - which makes finding rare microlensing events a challenging tasks. In almost all previous works, microlensing events have been detected either by applying very strict selection cuts or manually inspecting tens of thousands of light curves. However, the number of microlensing events expected in the future space-based microlensing experiments forces us to consider fully-automated approaches. They are especially important for selecting binary-lens events that often exhibit complex light curve morphologies and are otherwise difficult to find. There are no dedicated selection algorithms for binary-lens events in the literature, which hampers their statistical studies. Here, we present two simple neural-network-based classifiers for detecting single and binary microlensing events. We demonstrate their robustness using OGLE-III and OGLE-IV data sets and show they perform well on microlensing events detected in data from the Zwicky Transient Facility (ZTF). Classifiers are able to correctly recognize ~98% of single-lens events and 80-85% of binary-lens events.

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