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

TL;DR

This paper presents a machine learning pipeline using random forests and self-organising maps to efficiently vet exoplanet candidates from ground-based survey data, significantly improving candidate selection accuracy.

Contribution

It introduces a novel machine learning method with injected transit signals for training, and provides an open-source tool for automated planetary candidate vetting.

Findings

Achieved 97.6% AUC score in ranking planets versus false positives.

Demonstrated effectiveness of machine learning on ground-based survey data.

Provided publicly accessible code for the vetting algorithm.

Abstract

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.