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https://hdl.handle.net/2440/118642
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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 2041-8213 |
Statement of Responsibility: | 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 |
Published version: | http://dx.doi.org/10.3847/2041-8213/aaf23b |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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