Leveraging space-based data from the nearest Solar-type star to better understand stellar activity signatures in radial velocity data
Abstract
Stellar variability is a key obstacle in reaching the sensitivity required to recover Earth-like exoplanetary signals using the radial velocity (RV) detection method. To explore activity signatures in Sun-like stars, we present SolAster, a publicly-distributed analysis pipeline that allows for comparison of space-based measurements with ground-based disk-integrated RVs. Using high spatial resolution Dopplergrams, magnetograms, and continuum filtergrams from the Helioseismic and Magnetic Imager (HMI) aboard the Solar Dynamics Observatory (SDO), we estimate 'Sun-as-a-star' disk-integrated RVs due to rotationally modulated flux imbalances and convective blueshift suppression, as well as other observables such as unsigned magnetic flux. Comparing these measurements with ground-based RVs from the NEID instrument, which observes the Sun daily using an automated solar telescope, we find a strong relationship between magnetic activity indicators and RV variation, supporting efforts to examine unsigned magnetic flux as a proxy for stellar activity in slowly rotating stars. Detrending against measured unsigned magnetic flux allows us to improve the NEID RV measurements by ~20\% (~50 cm/s in a quadrature sum), yielding an RMS scatter of ~60 cm/s over five months. We also explore correlations between individual and averaged spectral line shapes in the NEID spectra and SDO-derived magnetic activity indicators, motivating future studies of these observables. Finally, applying SolAster to archival planetary transits of Venus and Mercury, we demonstrate the ability to recover small amplitude (< 50 cm/s) RV variations in the SDO data by directly measuring the Rossiter-McLaughlin (RM) signals.
Keywords
Cite
@article{arxiv.2204.09014,
title = {Leveraging space-based data from the nearest Solar-type star to better understand stellar activity signatures in radial velocity data},
author = {Tamar Ervin and Samuel Halverson and Abigail Burrows and Neil Murphy and Arpita Roy and Raphaelle D. Haywood and Federica Rescigno and Chad F. Bender and Andrea S. J. Lin and Jennifer Burt and Suvrath Mahadevan},
journal= {arXiv preprint arXiv:2204.09014},
year = {2022}
}
Comments
20 pages, 12 figures