Identifying microlensing events using neural networks
- Creators
- Mróz, P.
Abstract
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.
Additional Information
© 2020 Copernicus Foundation for Polish Astronomy. Received August 27, 2020. 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.Attached Files
Published - pap_70_3_1.pdf
Submitted - 2008.11930.pdf
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Additional details
- Eprint ID
- 105409
- Resolver ID
- CaltechAUTHORS:20200916-112840367
- Created
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2020-09-21Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field