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https://hdl.handle.net/2440/118642
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dc.contributor.author | Ansdell, M. | - |
dc.contributor.author | Ioannou, Y. | - |
dc.contributor.author | Osborn, H.P. | - |
dc.contributor.author | Sasdelli, M. | - |
dc.contributor.author | Smith, J.C. | - |
dc.contributor.author | Caldwell, D. | - |
dc.contributor.author | Jenkins, J.M. | - |
dc.contributor.author | Räissi, C. | - |
dc.contributor.author | Angerhausen, D. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Letters of the Astrophysical Journal, 2018; 869(1):L7-1-L7-7 | - |
dc.identifier.issn | 2041-8205 | - |
dc.identifier.issn | 2041-8213 | - |
dc.identifier.uri | http://hdl.handle.net/2440/118642 | - |
dc.description.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. | - |
dc.description.statementofresponsibility | Megan Ansdell, Yani Ioannou, Hugh P. Osborn, Michele Sasdelli, Jeffrey C. Smith, Douglas Caldwell, Jon M. Jenkins, Chedy Räissi, and Daniel Angerhausen | - |
dc.language.iso | en | - |
dc.publisher | IOP Publishing | - |
dc.rights | © 2018. The American Astronomical Society. All rights reserved. | - |
dc.source.uri | http://dx.doi.org/10.3847/2041-8213/aaf23b | - |
dc.subject | Methods: data analysis; planets and satellites: detection; techniques: photometric | - |
dc.title | Scientific domain knowledge improves exoplanet transit classification with deep learning | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.3847/2041-8213/aaf23b | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Sasdelli, M. [0000-0003-1021-6369] | - |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
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