Identifying Exoplanets with Deep Learning VI. Enhancing neural network mitigation of stellar activity RV signals with additional metrics

TL;DR

This paper enhances neural network models for mitigating stellar activity signals in radial velocity exoplanet measurements by incorporating additional stellar activity metrics, significantly reducing RV scatter to approach supergranulation noise levels.

Contribution

It introduces a neural network trained with multiple stellar activity indicators, improving RV prediction accuracy beyond previous models that used only white light CCFs.

Findings

Neural network reduces RV scatter from 147.1 cm/s to 93.3 cm/s.

Additional metrics like TSI, magnetic flux, and chromatic CCFs improve model performance.

Effective tracers for (super)granulation are key for further RV jitter mitigation.

Abstract

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.