  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.
