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Automatic Detection of Microlensing Events in the Galactic Bulge using Machine Learning Techniques

Chu, Selina and Wagstaff, Kiri L. and Bryden, Geoffrey and Shvartzvald, Yossi (2019) Automatic Detection of Microlensing Events in the Galactic Bulge using Machine Learning Techniques. In: Astronomical Data Analysis Software and Systems XXVIII. Astronomical Society of the Pacific Conference Series. No.523. Astronomical Society of the Pacific , San Francisco, CA, pp. 127-130. ISBN 978-1-58381-933-3.

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The Wide Field Infrared Survey Telescope (WFIRST) is a NASA flagship mission scheduled to launch in mid-2020, with more than one year of its lifetime dedicated to microlensing survey. The survey is to discover thousands of exoplanets near or beyond the snowline via their microlensing lightcurve signatures, enabling a Kepler-like statistical analysis of planets at \textasciitilde1-10 AU from their host stars. Our goal is to create an automated system that has the ability to efficiently process and classify large-scale astronomical datasets that missions such as WFIRST will produce. In this paper, we discuss our framework that utilizes feature selection and parameter optimization for classification models to automatically discriminate different types of stellar variability and detect microlensing events.

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Shvartzvald, Yossi0000-0003-1525-5041
Additional Information:© 2019 Astronomical Society of the Pacific. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA.
Group:Infrared Processing and Analysis Center (IPAC)
Funding AgencyGrant Number
Series Name:Astronomical Society of the Pacific Conference Series
Issue or Number:523
Record Number:CaltechAUTHORS:20191202-085436205
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100126
Deposited By: Tony Diaz
Deposited On:02 Dec 2019 17:13
Last Modified:02 Dec 2019 17:13

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