Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks
This provides a computationally efficient method for astronomers to search vast TESS data for exoplanets, reducing human effort, though it is incremental as it applies existing neural network techniques to this domain.
The researchers tackled the problem of efficiently identifying planetary transit candidates from millions of TESS light curves by developing a convolutional neural network that performs inference in ~5ms per light curve, resulting in 181 new planet candidates.
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.