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Identifying microlensing events using neural networks

Mróz, Przemek (2020) Identifying microlensing events using neural networks. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper
Mróz, Przemek0000-0001-7016-1692
Additional Information:This work has made use of data from the OGLE survey. We would like to thank OGLE observers for their contribution to the collection of the photometric data used in this paper. We would like to thank Radek Poleski for sharing his classifications of OGLE microlensing events and Dmitry Duev for discussions on neural networks. We thank Dmitry Duev, Radek Poleski, and Andrzej Udalski for their comments on the manuscript.
Subject Keywords:Gravitational microlensing, Classification, Neural networks
Record Number:CaltechAUTHORS:20200916-112840367
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105409
Deposited By: George Porter
Deposited On:21 Sep 2020 20:30
Last Modified:21 Sep 2020 20:30

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