  NASA's Transiting Exoplanet Survey Satellite (TESS) presents us with an
unprecedented volume of space-based photometric observations that must be
analyzed in an efficient and unbiased manner. With at least $\sim1,000,000$ new
light curves generated every month from full frame images alone, automated
planet candidate identification has become an attractive alternative to human
vetting. Here we present a deep learning model capable of performing triage and
vetting on TESS candidates. Our model is modified from an existing neural
network designed to automatically classify Kepler candidates, and is the first
neural network to be trained and tested on real TESS data. In triage mode, our
model can distinguish transit-like signals (planet candidates and eclipsing
binaries) from stellar variability and instrumental noise with an average
precision (the weighted mean of precisions over all classification thresholds)
of 97.0% and an accuracy of 97.4%. In vetting mode, the model is trained to
identify only planet candidates with the help of newly added scientific domain
knowledge, and achieves an average precision of 69.3% and an accuracy of 97.8%.
We apply our model on new data from Sector 6, and present 288 new signals that
received the highest scores in triage and vetting and were also identified as
planet candidates by human vetters. We also provide a homogeneously classified
set of TESS candidates suitable for future training.
