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

State of the art exoplanettransit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systemsor in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learningalgorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learningin 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 autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vettingof planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learningtechniques, 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.