论文标题
使用神经网络识别微透镜事件
Identifying microlensing events using neural networks
论文作者
论文摘要
当前的重力微透镜调查正在观察银河系凸起中数亿颗恒星 - 这使得在稀有的微透镜事件中成为一个具有挑战性的任务。在几乎所有以前的作品中，通过应用非常严格的选择切割或手动检查成千上万的光曲线来检测到微卷事件。但是，在未来的空间基微透镜实验中预期的微卷事件数量迫使我们考虑完全自动化的方法。它们对于选择经常表现出复杂的光曲线形态并且难以找到的二进制镜头事件尤其重要。文献中没有针对二元镜头事件的专用选择算法，这阻碍了他们的统计研究。在这里，我们介绍了两个简单的基于神经网络的分类器，用于检测单次微化事件。我们使用Ogle-III和Ogle-IV数据集证明了它们的鲁棒性，并证明它们在从Zwicky瞬态设施（ZTF）的数据中检测到的微透镜事件表现良好。分类器能够正确识别〜98％的单透镜事件和80-85％的二元镜头事件。
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.
