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The Transiting Exoplanet Survey Satellite (TESS) mission measured light fromstars in ~75% of the sky throughout its two year primary mission, resulting inmillions of TESS 30-minute cadence light curves to analyze in the search fortransiting exoplanets. To search this vast data trove for transit signals, weaim to provide an approach that is both computationally efficient and produceshighly performant predictions. This approach minimizes the required humansearch effort. We present a convolutional neural network, which we train toidentify planetary transit signals and dismiss false positives. To make aprediction for a given light curve, our network requires no prior transitparameters identified using other methods. Our network performs inference on aTESS 30-minute cadence light curve in ~5ms on a single GPU, enabling largescale archival searches. We present 181 new planet candidates identified by ournetwork, which pass subsequent human vetting designed to rule out falsepositives. Our neural network model is additionally provided as open-sourcecode for public use and extension.

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