Identifying Exoplanets with Deep Learning VI. Enhancing neural network mitigation of stellar activity RV signals with additional metrics
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915808671694848 |
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| author | McWilliam, Naomi de Beurs, Zoë L. Vanderburg, Andrew Viaña, Javier Mortier, Annelies Buchhave, Lars A. Cameron, Andrew Collier Cosentino, Rosario Dumusque, Xavier Ghedina, Adriano Lakeland, Ben Lodi, Marcello López-Morales, Mercedes Sasselov, Dimitar Sozzetti, Alessandro |
| author_facet | McWilliam, Naomi de Beurs, Zoë L. Vanderburg, Andrew Viaña, Javier Mortier, Annelies Buchhave, Lars A. Cameron, Andrew Collier Cosentino, Rosario Dumusque, Xavier Ghedina, Adriano Lakeland, Ben Lodi, Marcello López-Morales, Mercedes Sasselov, Dimitar Sozzetti, Alessandro |
| contents | The measurement of exoplanet masses using the radial velocity (RV) technique is currently limited by stellar activity, which introduces quasiperiodic variability signals that must be modeled and removed to enhance the sensitivity of the RV measurements to exoplanet signals. Neural networks have previously been demonstrated effective in modeling stellar activity signals in HARPS-N solar data using white light cross correlation functions (CCFs). Building on this work, we train a neural network on six years of HARPS-N solar data with additional parameters commonly associated to stellar activity, including chromatic CCFs, line shape metrics, spectral activity indicators, total solar irradiance (TSI) light curves from SORCE and TSIS-1, and TSI time derivatives. Our results show that parameters such as the bisector inverse slope and Na D equivalent widths do not significantly improve the neural network's ability to predict activity-induced RV variations compared to using the white light CCFs alone. However, parameters such as unsigned magnetic flux, the TSI and its time derivative, S-index, H-alpha equivalent width, chromatic CCFs, contrast, and full width at half maximum do improve the neural network's ability to predict RV scatter. Our new model reduces the RV scatter in a held-out test set from 147.1 cm/s to 93.3 cm/s, consistent with supergranulation noise levels reported in previous studies. These results suggest that finding effective tracers for (super)granulation will be critical to train models capable of further mitigating RV jitter, and necessary for characterizing Earth analogues. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_17760 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Identifying Exoplanets with Deep Learning VI. Enhancing neural network mitigation of stellar activity RV signals with additional metrics McWilliam, Naomi de Beurs, Zoë L. Vanderburg, Andrew Viaña, Javier Mortier, Annelies Buchhave, Lars A. Cameron, Andrew Collier Cosentino, Rosario Dumusque, Xavier Ghedina, Adriano Lakeland, Ben Lodi, Marcello López-Morales, Mercedes Sasselov, Dimitar Sozzetti, Alessandro Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics The measurement of exoplanet masses using the radial velocity (RV) technique is currently limited by stellar activity, which introduces quasiperiodic variability signals that must be modeled and removed to enhance the sensitivity of the RV measurements to exoplanet signals. Neural networks have previously been demonstrated effective in modeling stellar activity signals in HARPS-N solar data using white light cross correlation functions (CCFs). Building on this work, we train a neural network on six years of HARPS-N solar data with additional parameters commonly associated to stellar activity, including chromatic CCFs, line shape metrics, spectral activity indicators, total solar irradiance (TSI) light curves from SORCE and TSIS-1, and TSI time derivatives. Our results show that parameters such as the bisector inverse slope and Na D equivalent widths do not significantly improve the neural network's ability to predict activity-induced RV variations compared to using the white light CCFs alone. However, parameters such as unsigned magnetic flux, the TSI and its time derivative, S-index, H-alpha equivalent width, chromatic CCFs, contrast, and full width at half maximum do improve the neural network's ability to predict RV scatter. Our new model reduces the RV scatter in a held-out test set from 147.1 cm/s to 93.3 cm/s, consistent with supergranulation noise levels reported in previous studies. These results suggest that finding effective tracers for (super)granulation will be critical to train models capable of further mitigating RV jitter, and necessary for characterizing Earth analogues. |
| title | Identifying Exoplanets with Deep Learning VI. Enhancing neural network mitigation of stellar activity RV signals with additional metrics |
| topic | Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2602.17760 |