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Exoplanet validation with machine learning : 50 new validated Kepler planets
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Armstrong, David J., Gamper, Jevgenij and Damoulas, Theodoros (2020) Exoplanet validation with machine learning : 50 new validated Kepler planets. Monthly Notices of the Royal Astronomical Society . staa2498. doi:10.1093/mnras/staa2498 (In Press)
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Official URL: https://doi.org/10.1093/mnras/staa2498
Abstract
Over 30% of the ∼4000 known exoplanets to date have been discovered using ‘validation’, where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated. For the large majority of these validated planets calculations were performed using the vespa algorithm (Morton et al. 2016). Regardless of the strengths and weaknesses of vespa, it is highly desirable for the catalogue of known planets not to be dependent on a single method. We demonstrate the use of machine learning algorithms, specifically a gaussian process classifier (GPC) reinforced by other models, to perform probabilistic planet validation incorporating prior probabilities for possible FP scenarios. The GPC can attain a mean log-loss per sample of 0.54 when separating confirmed planets from FPs in the Kepler threshold crossing event (TCE) catalogue. Our models can validate thousands of unseen candidates in seconds once applicable vetting metrics are calculated, and can be adapted to work with the active TESS mission, where the large number of observed targets necessitates the use of automated algorithms. We discuss the limitations and caveats of this methodology, and after accounting for possible failure modes newly validate 50 Kepler candidates as planets, sanity checking the validations by confirming them with vespa using up to date stellar information. Concerning discrepancies with vespa arise for many other candidates, which typically resolve in favour of our models. Given such issues, we caution against using single-method planet validation with either method until the discrepancies are fully understood.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QB Astronomy |
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| Divisions: | Faculty of Science > Physics | |||||||||
| Library of Congress Subject Headings (LCSH): | Extrasolar planets , Extrasolar planets -- Detection -- Data processing, Gaussian processes | |||||||||
| Journal or Publication Title: | Monthly Notices of the Royal Astronomical Society | |||||||||
| Publisher: | Oxford University Press | |||||||||
| ISSN: | 1745-3933 | |||||||||
| Official Date: | 20 August 2020 | |||||||||
| Dates: |
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| Date of first compliant deposit: | 14 August 2020 | |||||||||
| Article Number: | staa2498 | |||||||||
| DOI: | 10.1093/mnras/staa2498 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Publisher Statement: | This is a pre-copyedited, author-produced version of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record: Exoplanet Validation with Machine Learning: 50 new validated Kepler planets David J Armstrong, Jevgenij Gamper, Theodoros Damoulas, Monthly Notices of the Royal Astronomical Society, staa2498 is available online at: https://academic.oup.com/mnras/advance-article-abstract/doi/10.1093/mnras/staa2498/5894933 | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| RIOXX Funder/Project Grant: |
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