Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913238496575488 |
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| author | Álvarez, Santiago Iglesias Alonso, Enrique Díez Rodríguez, María Luisa Sánchez Rodríguez, Javier Rodríguez Fernández, Saúl Pérez Juez, Francisco Javier de Cos |
| author_facet | Álvarez, Santiago Iglesias Alonso, Enrique Díez Rodríguez, María Luisa Sánchez Rodríguez, Javier Rodríguez Fernández, Saúl Pérez Juez, Francisco Javier de Cos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks Álvarez, Santiago Iglesias Alonso, Enrique Díez Rodríguez, María Luisa Sánchez Rodríguez, Javier Rodríguez Fernández, Saúl Pérez Juez, Francisco Javier de Cos Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning 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. |
| title | Computing Transiting Exoplanet Parameters with 1D Convolutional Neural Networks |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2402.13673 |