Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks

• URL: http://arxiv.org/abs/2101.10919v1
• Date: Tue, 26 Jan 2021 16:40:51 GMT
• Title: Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks
• Authors: Greg Olmschenk, Stela Ishitani Silva, Gioia Rau, Richard K. Barry, Ethan Kruse, Luca Cacciapuoti, Veselin Kostov, Brian P. Powell, Edward Wyrwas, Jeremy D. Schnittman, Thomas Barclay
• Abstract summary: Transiting Exoplanet Survey Satellite measured light from stars in 75% of the sky throughout its two year primary mission. Millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives.
• Score: 1.2583362454189522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.

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