  Space-based missions such as Kepler, and soon TESS, provide large datasets
that must be analyzed efficiently and systematically. Recent work by Shallue &
Vanderburg (2018) successfully used state-of-the-art deep learning models to
automatically classify Kepler transit signals as either exoplanets or false
positives; our application of their model yielded 95.8% accuracy and 95.5%
average precision. Here we expand upon that work by including additional
scientific domain knowledge into the network architecture and input
representations to significantly increase overall model performance to 97.5%
accuracy and 98.0% average precision. Notably, we achieve 15-20% gains in
recall for the lowest signal-to-noise transits that can correspond to rocky
planets in the habitable zone. We input into the network centroid time-series
information derived from Kepler data plus key stellar parameters taken from the
Kepler DR25 catalogue. We also implement data augmentation techniques to
alleviate model over-fitting. These improvements allow us to drastically reduce
the size of the model, while still maintaining improved performance; smaller
models are better for generalization, for example from Kepler to TESS data.
This work illustrates the importance of including expert domain knowledge in
even state-of-the-art deep learning models when applying them to scientific
research problems that seek to identify weak signals in noisy data. This
classification tool will be especially useful for upcoming space-based
photometry missions focused on finding small planets, such as TESS and PLATO.
