Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909831427784704 |
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| author | Gavankar, Anoop Mittal, Tanish Ninan, Joe Hanasoge, Shravan |
| author_facet | Gavankar, Anoop Mittal, Tanish Ninan, Joe Hanasoge, Shravan |
| contents | Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stellar jitter. In this work, we develop a machine learning (ML) framework based on a Transformer architecture that aims to detect the presence and likely period of planetary signals in time-series spectra, even in the presence of stellar activity. The model is trained to classify whether a planetary signal exists and assign it to one of several discrete period and amplitude bins. Injection-recovery tests on randomly selected 100 epoch observation subsets from NEID solar data (2020-2022 period) show that for low-amplitude systems ($<$1 ms$^{-1}$), our model improves planetary candidate identification by a factor of two compared to the traditional Lomb-Scargle periodogram.
Our ML model is built on a Vision Transformer (ViT) architecture that processes reduced representations of solar spectrum observations to predict the period and semi-amplitude of planetary signal candidates. By analyzing multi-epoch spectra, the model reliably detects planetary signals with semi-amplitudes as low as 65 cms$^{-1}$. Even under real solar noise and irregular sampling, it identifies signals down to 35 cms$^{-1}$. Comparisons with the Lomb-Scargle periodogram demonstrate a significant improvement in detecting low-amplitude planetary candidates, particularly for longer orbital periods. These results underscore the potential of machine learning to identify planetary candidates early in EPRV surveys, even from limited observational counts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07034 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates Gavankar, Anoop Mittal, Tanish Ninan, Joe Hanasoge, Shravan Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stellar jitter. In this work, we develop a machine learning (ML) framework based on a Transformer architecture that aims to detect the presence and likely period of planetary signals in time-series spectra, even in the presence of stellar activity. The model is trained to classify whether a planetary signal exists and assign it to one of several discrete period and amplitude bins. Injection-recovery tests on randomly selected 100 epoch observation subsets from NEID solar data (2020-2022 period) show that for low-amplitude systems ($<$1 ms$^{-1}$), our model improves planetary candidate identification by a factor of two compared to the traditional Lomb-Scargle periodogram. Our ML model is built on a Vision Transformer (ViT) architecture that processes reduced representations of solar spectrum observations to predict the period and semi-amplitude of planetary signal candidates. By analyzing multi-epoch spectra, the model reliably detects planetary signals with semi-amplitudes as low as 65 cms$^{-1}$. Even under real solar noise and irregular sampling, it identifies signals down to 35 cms$^{-1}$. Comparisons with the Lomb-Scargle periodogram demonstrate a significant improvement in detecting low-amplitude planetary candidates, particularly for longer orbital periods. These results underscore the potential of machine learning to identify planetary candidates early in EPRV surveys, even from limited observational counts. |
| title | Machine Learning for Radial Velocity Analysis I: Vision Transformers as a Robust Alternative for Detecting Planetary Candidates |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2510.07034 |