Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
2018
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
Keplertransit signals as either
exoplanetsor 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 knowledgeinto 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
Keplerdata plus key stellar parameters taken from the
KeplerDR25 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
Keplerto TESS data. This work illustrates the importance of including expert
domain knowledgein 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.
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