Skip to Main content Skip to Navigation
New interface
Journal articles

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.
Complete list of metadata
Contributor : Chedy Raïssi Connect in order to contact the contributor
Submitted on : Monday, December 17, 2018 - 4:02:42 PM
Last modification on : Friday, November 18, 2022 - 10:13:54 AM

Links full text



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, 2018, 869 (1), pp.L7. ⟨10.3847/2041-8213/aaf23b⟩. ⟨hal-01957950⟩



Record views