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Over 30% of the ~4000 known exoplanets to date have been discovered using'validation', where the statistical likelihood of a transit arising from afalse positive (FP), non-planetary scenario is calculated. For the largemajority of these validated planets calculations were performed using the vespaalgorithm (Morton et al. 2016). Regardless of the strengths and weaknesses ofvespa, it is highly desirable for the catalogue of known planets not to bedependent on a single method. We demonstrate the use of machine learningalgorithms, specifically a gaussian process classifier (GPC) reinforced byother models, to perform probabilistic planet validation incorporating priorprobabilities for possible FP scenarios. The GPC can attain a mean log-loss persample of 0.54 when separating confirmed planets from FPs in the Keplerthreshold crossing event (TCE) catalogue. Our models can validate thousands ofunseen candidates in seconds once applicable vetting metrics are calculated,and can be adapted to work with the active TESS mission, where the large numberof observed targets necessitates the use of automated algorithms. We discussthe limitations and caveats of this methodology, and after accounting forpossible failure modes newly validate 50 Kepler candidates as planets, sanitychecking the validations by confirming them with vespa using up to date stellarinformation. Concerning discrepancies with vespa arise for many othercandidates, which typically resolve in favour of our models. Given such issues,we caution against using single-method planet validation with either methoduntil the discrepancies are fully understood.

Exoplanet Validation with Machine Learning: 50 new validated Kepler planets