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Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

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posted on 2018-05-23, 13:07 authored by David. J. Armstrong, Maximilian N. Günther, James McCormac, Alexis M. S. Smith, Daniel Bayliss, François Bouchy, Matthew R. Burleigh, Sarah Casewell, Philipp Eigmüller, Edward Gillen, Michael R. Goad, Simon T. Hodgkin, James S. Jenkins, Tom Louden, Lionel Metrailler, Don Pollacco, Katja Poppenhaeger, Didier Queloz, Liam Raynard, Heike Rauer, Stéphane Udry, Simon. R. Walker, Christopher A. Watson, Richard G. West, Peter J. Wheatley
State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6\% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the \texttt{autovet} code used to implement the algorithm publicly accessible. \texttt{autovet} is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.


This publication is based on data collected under the NGTS project at the ESO La Silla Paranal Observatory. The NGTS instrument and operations are funded by the consortium institutes and by the UK Science and Technology Facilities Council (STFC; project reference ST/M001962/1). DJA, DP, PJW and RGW are supported by an STFC consolidated grant (ST/P000495/1). MNG is supported by the UK Science and Technology Facilities Council (STFC) award reference 1490409 as well as the Isaac Newton Studentship. JSJ acknowledges support by FONDECYT grant 1161218 and partial support by CATA-Basal (PB06, CONICYT)



Monthly Notices of the Royal Astronomical Society, 2018, sty1313

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Physics and Astronomy


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Monthly Notices of the Royal Astronomical Society


Oxford University Press (OUP), Royal Astronomical Society





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