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Type: Journal article
Title: Scientific domain knowledge improves exoplanet transit classification with deep learning
Author: Ansdell, M.
Ioannou, Y.
Osborn, H.P.
Sasdelli, M.
Smith, J.C.
Caldwell, D.
Jenkins, J.M.
Räissi, C.
Angerhausen, D.
Citation: Letters of the Astrophysical Journal, 2018; 869(1):L7-1-L7-7
Publisher: IOP Publishing
Issue Date: 2018
ISSN: 2041-8205
Statement of
Megan Ansdell, Yani Ioannou, Hugh P. Osborn, Michele Sasdelli, Jeffrey C. Smith, Douglas Caldwell, Jon M. Jenkins, Chedy Räissi, and Daniel Angerhausen
Abstract: Space-based missions such as Kepler, and soon the Transiting Exoplanet Survey Satellite (TESS), provide large data sets that must be analyzed efficiently and systematically. Recent work by Shallue & Vanderburg 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 data release 25 catalog. 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 Letter 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.
Keywords: Methods: data analysis; planets and satellites: detection; techniques: photometric
Rights: © 2018. The American Astronomical Society. All rights reserved.
DOI: 10.3847/2041-8213/aaf23b
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