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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The transit method allows the detection and characterization of planetary systems by analyzing stellar light curves. Convolutional neural networks appear to offer a viable solution for automating these analyses. In this research, two 1D convolutional neural network models, which work with simulated light curves in which transit-like signals were injected, are presented. One model operates on complete light curves and estimates the orbital period, and the other one operates on phase-folded light curves and estimates the semimajor axis of the orbit and the square of the planet-to-star radius ratio. Both models were tested on real data from TESS light curves with confirmed planets to ensure that they are able to work with real data. The results obtained show that 1D CNNs are able to characterize transiting exoplanets from their host star’s detrended light curve and, furthermore, reducing both the required time and computational costs compared with the current detection and characterization algorithms.

Details

Title
Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks
Author
Santiago Iglesias Álvarez 1   VIAFID ORCID Logo  ; Enrique Díez Alonso 2   VIAFID ORCID Logo  ; Sánchez Rodríguez, María Luisa 3   VIAFID ORCID Logo  ; Javier Rodríguez Rodríguez 1   VIAFID ORCID Logo  ; Saúl Pérez Fernández 1   VIAFID ORCID Logo  ; Francisco Javier de Cos Juez 4   VIAFID ORCID Logo 

 Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), C. Independencia 13, 33004 Oviedo, Spain; [email protected] (M.L.S.R.); [email protected] (J.R.R.); [email protected] (S.P.F.); [email protected] (F.J.d.C.J.) 
 Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), C. Independencia 13, 33004 Oviedo, Spain; [email protected] (M.L.S.R.); [email protected] (J.R.R.); [email protected] (S.P.F.); [email protected] (F.J.d.C.J.); Departamento de Matemáticas, Facultad de Ciencias, Universidad de Oviedo, 33007 Oviedo, Spain 
 Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), C. Independencia 13, 33004 Oviedo, Spain; [email protected] (M.L.S.R.); [email protected] (J.R.R.); [email protected] (S.P.F.); [email protected] (F.J.d.C.J.); Departamento de Física, Universidad de Oviedo, 33007 Oviedo, Spain 
 Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), C. Independencia 13, 33004 Oviedo, Spain; [email protected] (M.L.S.R.); [email protected] (J.R.R.); [email protected] (S.P.F.); [email protected] (F.J.d.C.J.); Departamento de Explotación y Prospección Minera, Universidad de Oviedo, 33004 Oviedo, Spain 
First page
83
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20751680
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2930482907
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.