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American Astronomical Society, Astronomical Journal, 6(163), p. 272, 2022

DOI: 10.3847/1538-3881/ac67e6

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Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

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 10 10 https://tamarervin.github.io/SolAster/ 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 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−1 in a quadrature sum), yielding an rms scatter of ∼60 cm s−1 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−1) RV variations in the SDO data by directly measuring the Rossiter–McLaughlin signals.