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Machine-learning approaches to exoplanet transit detection and candidate validation in wide-field ground-based surveys

A. Collier Cameron, E. Hťbrard, A. H. M. J. Triaud, J.-M. Almenara, D. R. Anderson, D. J. Armstrong, S. C. C. Barros, F. Bouchy, P. Boumis, D. Brown, F. Faedi, K. L. Hay, L. Hebb, F. Kiefer, L. Mancini, P. F. L. Maxted, E. Palle, D. Pollacco, D. Queloz, B. Smalley, S. Udry, R. G. West, P. J. Wheatley

Abstract

Since the start of the Wide-angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of this paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test data sets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine-learning methods including Random Forest Classifiers (RFCs) and convolutional neural networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data similar to 90 percent of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.

Keywords
methods: data analysis; methods: statistical; planets and satellites: detection; Astrophysics - Earth and Planetary Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysic

Monthly Notices of the Royal Astronomical Society
Volume 483, Issue 4, Page 5534
2019 March

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Instituto de Astrof√≠sica e Ci√™ncias do Espa√ßo Universidade do Porto Faculdade de Ciências da Universidade de Lisboa
Fundação para a Ciência e a Tecnologia COMPETE 2020 PORTUGAL 2020 União Europeia