  Differentiating between real transit events and false positive signals in
photometric time series data is a bottleneck in the identification of
transiting exoplanets, particularly long-period planets. This differentiation
typically requires visual inspection of a large number of transit-like signals
to rule out instrumental and astrophysical false positives that mimic planetary
transit signals. We build a one-dimensional convolutional neural network (CNN)
to separate eclipsing binaries and other false positives from potential planet
candidates, reducing the number of light curves that require human vetting. Our
CNN is trained using the TESS light curves that were identified by Planet
Hunters citizen scientists as likely containing a transit. We also include the
background flux and centroid information. The light curves are visually
inspected and labeled by project scientists and are minimally pre-processed,
with only normalization and data augmentation taking place before training. The
median percentage of contaminants flagged across the test sectors is 18% with a
maximum of 37% and a minimum of 10%. Our model keeps 100% of the planets for 16
of the 18 test sectors, while incorrectly flagging one planet candidate (0.3%)
for one sector and two (0.6%) for the remaining sector. Our method shows
potential to reduce the number of light curves requiring manual vetting by up
to a third with minimal misclassification of planet candidates.
