Classifying Exoplanet Candidates with Convolutional Neural Networks: Application to the Next Generation Transit Survey

Based on data collected under the NGTS project at the ESO La Silla Paranal Observatory. The NGTS facility is operated by the consortium institutes with support from the UK Science and Technology Facilities Council (STFC) through projects ST/M001962/1 and ST/S002642/1. LR is supported by an STFC studentship (1795021). The contributions at the University of Leicester by MRG and MRB have been supported by STFC through consolidated grant ST/N000757/1. PE and ACh acknowledge the support of the DFG priority program SPP 1992 ”Exploring the Diversity of Extrasolar Planets” (RA 714/13-1). The contributions at the University of Warwick by PJW and RGW have been supported by STFC through consolidated grants ST/L000733/1 and ST/P000495/1. DJA gratefully acknowledges support from the STFC via an Ernest Rutherford Fellowship (ST/R00384X/1). JSJ acknowledges support by Fondecyt grant 1161218 and partial support by CATA-Basal (PB06, CONICYT). This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This research used the ALICE High Performance Computing Facility at the University of Leicester.