Naomi McWilliam
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Naomi McWilliam has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Adriano Ghedina
Fundación Galileo Galilei – INAF
1 shared publication - Alessandro Sozzetti
INAF – Osservatorio Astrofisico di Torino
1 shared publication - Andrew Collier Cameron
Centre for Exoplanet Science, SUPA School of Physics and Astronomy, University of St Andrews
1 shared publication - Andrew Vanderburg
Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA 02138, USA
1 shared publication - Annelies Mortier
School of Physics & Astronomy, University of Birmingham , Edgbaston, Birmingham B15 2TT
1 shared publication - Ben S. Lakeland
School of Physics and Astronomy, University of Birmingham , Edgbaston, Birmingham B15 2TT
1 shared publication
Latest Publications
- Open Access
- 18 March 2026
- Published by American Astronomical Society in The Astronomical Journal
- Vol. 171 (4) , 233
Identifying Exoplanets with Deep Learning. VI. Enhancing Neural Network Mitigation of Stellar Activity RV Signals with Additional Metrics
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 6 yr 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 (EWs) 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 α EW, chromatic CCFs, contrast, and FWHM 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 −1 to 93.3 cm s −1 , 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 analogs.
- 18 March 2026
- Published by American Astronomical Society in The Astronomical Journal
- Vol. 171 (4) , 233
Open AccessIdentifying Exoplanets with Deep Learning. VI. Enhancing Neural Network Mitigation of Stellar Activity RV Signals with Additional Metrics
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 6 yr 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 (EWs) 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 α EW, chromatic CCFs, contrast, and FWHM 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 −1 to 93.3 cm s −1 , 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 analogs.