Version 1
: Received: 3 December 2021 / Approved: 6 December 2021 / Online: 6 December 2021 (12:36:42 CET)
How to cite:
Cuéllar, S.; Granados, P.; Fabregas, E.; Curé, M.; Vargas, H.; Dormido-Canto, S.; Farías, G. Deep Learning Exoplanets Detection by Combining Real and Synthetic Data. Preprints2021, 2021120070. https://doi.org/10.20944/preprints202112.0070.v1
Cuéllar, S.; Granados, P.; Fabregas, E.; Curé, M.; Vargas, H.; Dormido-Canto, S.; Farías, G. Deep Learning Exoplanets Detection by Combining Real and Synthetic Data. Preprints 2021, 2021120070. https://doi.org/10.20944/preprints202112.0070.v1
Cuéllar, S.; Granados, P.; Fabregas, E.; Curé, M.; Vargas, H.; Dormido-Canto, S.; Farías, G. Deep Learning Exoplanets Detection by Combining Real and Synthetic Data. Preprints2021, 2021120070. https://doi.org/10.20944/preprints202112.0070.v1
APA Style
Cuéllar, S., Granados, P., Fabregas, E., Curé, M., Vargas, H., Dormido-Canto, S., & Farías, G. (2021). Deep Learning Exoplanets Detection by Combining Real and Synthetic Data. Preprints. https://doi.org/10.20944/preprints202112.0070.v1
Chicago/Turabian Style
Cuéllar, S., Sebastián Dormido-Canto and Gonzalo Farías. 2021 "Deep Learning Exoplanets Detection by Combining Real and Synthetic Data" Preprints. https://doi.org/10.20944/preprints202112.0070.v1
Abstract
Scientists and astronomers have attached 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 lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform 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.
Keywords
Exoplanets Detection; Deep learning; Real and Simulated Data
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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