Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks

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...

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Bibliographic Details
Published in:Axioms 2024-01, Vol.13 (2), p.83
Main Authors: Iglesias Álvarez, Santiago, Díez Alonso, Enrique, Sánchez Rodríguez, María Luisa, Rodríguez Rodríguez, Javier, Pérez Fernández, Saúl, de Cos Juez, Francisco Javier
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
astrophysics
Computer simulation
Computer-generated environments
Convolutional codes
convolutional neural networks
Demographics
Estimates
exoplanets
Extrasolar planets
Light
Light curve
Neural networks
Orbits
Planetary systems
Space telescopes
Transit
transits
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Summary: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.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms13020083