Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
Résumé
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 stateof-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 and Gaia DR2 catalogues. 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.