Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning

Abstract : 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.
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https://hal.inria.fr/hal-01957950
Contributor : Chedy Raïssi <>
Submitted on : Monday, December 17, 2018 - 4:02:42 PM
Last modification on : Thursday, July 25, 2019 - 4:34:14 PM

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Megan Ansdell, Yani Ioannou, Hugh Osborn, Michele Sasdelli, Jeffrey Smith, et al.. Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning. The Astrophysical journal letters, Bristol : IOP Publishing, 2018, 869 (1), pp.L7. ⟨10.3847/2041-8213/aaf23b⟩. ⟨hal-01957950⟩

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