Identifying microlensing events using neural networks
This addresses the need for automated detection in future space-based microlensing experiments, particularly for binary-lens events which lack dedicated algorithms, though it is incremental as it applies existing neural network methods to a new domain-specific task.
The paper tackled the problem of detecting rare microlensing events in large astronomical surveys by developing neural-network-based classifiers for single and binary-lens events, achieving ~98% accuracy for single-lens and 80-85% for binary-lens events on datasets like OGLE and ZTF.
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.