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State of the art exoplanet transit surveys are producing ever increasingquantities of data. To make the best use of this resource, in detectinginteresting planetary systems or in determining accurate planetary populationstatistics, requires new automated methods. Here we describe a machine learningalgorithm that forms an integral part of the pipeline for the NGTS transitsurvey, demonstrating the efficacy of machine learning in selecting planetarycandidates from multi-night ground based survey data. Our method uses acombination of random forests and self-organising-maps to rank planetarycandidates, achieving an AUC score of 97.6\% in ranking 12368 injected planetsagainst 27496 false positives in the NGTS data. We build on past examples byusing injected transit signals to form a training set, a necessary developmentfor 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 planetarycandidates, and can utilise a variety of methods. The apparent robustness ofmachine learning techniques, whether on space-based or the qualitativelydifferent ground-based data, highlights their importance to future surveys suchas TESS and PLATO and the need to better understand their advantages andpitfalls in an exoplanetary context.

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