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Deep learning exoplanets detection by combining real and synthetic data
Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the us...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132280/ https://www.ncbi.nlm.nih.gov/pubmed/35613093 http://dx.doi.org/10.1371/journal.pone.0268199 |
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author | Cuéllar, Sara Granados, Paulo Fabregas, Ernesto Curé, Michel Vargas, Héctor Dormido-Canto, Sebastián Farias, Gonzalo |
author_facet | Cuéllar, Sara Granados, Paulo Fabregas, Ernesto Curé, Michel Vargas, Héctor Dormido-Canto, Sebastián Farias, Gonzalo |
author_sort | Cuéllar, Sara |
collection | PubMed |
description | Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope light curves. The approach is based on related work from the literature and enhanced to validation with real light curves. A CNN classification model is trained from a mixture of real and synthetic data. The model is then validated only with unknown real data. The best ratio of synthetic data is determined by the performance of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves. |
format | Online Article Text |
id | pubmed-9132280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91322802022-05-26 Deep learning exoplanets detection by combining real and synthetic data Cuéllar, Sara Granados, Paulo Fabregas, Ernesto Curé, Michel Vargas, Héctor Dormido-Canto, Sebastián Farias, Gonzalo PLoS One Research Article Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope light curves. The approach is based on related work from the literature and enhanced to validation with real light curves. A CNN classification model is trained from a mixture of real and synthetic data. The model is then validated only with unknown real data. The best ratio of synthetic data is determined by the performance of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves. Public Library of Science 2022-05-25 /pmc/articles/PMC9132280/ /pubmed/35613093 http://dx.doi.org/10.1371/journal.pone.0268199 Text en © 2022 Cuéllar et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cuéllar, Sara Granados, Paulo Fabregas, Ernesto Curé, Michel Vargas, Héctor Dormido-Canto, Sebastián Farias, Gonzalo Deep learning exoplanets detection by combining real and synthetic data |
title | Deep learning exoplanets detection by combining real and synthetic data |
title_full | Deep learning exoplanets detection by combining real and synthetic data |
title_fullStr | Deep learning exoplanets detection by combining real and synthetic data |
title_full_unstemmed | Deep learning exoplanets detection by combining real and synthetic data |
title_short | Deep learning exoplanets detection by combining real and synthetic data |
title_sort | deep learning exoplanets detection by combining real and synthetic data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132280/ https://www.ncbi.nlm.nih.gov/pubmed/35613093 http://dx.doi.org/10.1371/journal.pone.0268199 |
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