Modelling solar radial velocities and photometric variability with SOAPv4

TL;DR

This study uses SOAPv4 to model solar radial velocity and photometric variability caused by active regions, validating the models against observations and demonstrating the importance of accurate active region identification.

Contribution

The paper introduces an improved modeling approach with SOAPv4 and validates it using morphological transforms for active region detection, enhancing the accuracy of solar variability simulations.

Findings

SOAPv4 reproduces solar RV variability with ~0.91 m/s residuals.

Morphological transforms effectively identify active regions.

Photometric simulations match overall variability trends.

Abstract

Stellar activity remains one of the main limitations in the detection of Earth-like planets using radial velocity (RV) measurements. The Sun, as the only star for which surface features can be spatially resolved, offers a unique testbed for studying the impact of active regions on RV and photometric variability. Using SOAPv4, we modelled solar RV and photometric variability induced by spots and faculae over long timescales. Our goal is to verify whether present-day, state-of-the-art models of the cross-correlation function correctly reproduce the observed variability. Moreover, we aim to assess how the choice of input data and identification technique influences the agreement between simulated and observed signals. To simulate solar RV and photometric time series, we first identified active regions in SDO images. This was done using mathematical morphological (MM) transforms applied to SDO/HMI and AIA images. MM identification was validated against other state-of-the-art identification methods. Using these inputs, we ran SOAPv4 to simulate solar RVs and photometry, and we validated the results with HARPS-N RV observations, as well as with VIRGO/SPM photometric measurements. The simulations that use MM identification achieved the best match with the observed RV time series, yielding residuals with a measured standard deviation of ~0.91 m/s. Other state-of-the-art methods produced higher filling factors and, consequently, larger discrepancies. The photometric simulations reproduced the overall variability trends. We demonstrate that MM transforms accurately identify solar active regions. Using these inputs, SOAPv4 reproduces the observed solar RV variability with a measured standard deviation of the residuals of ~0.91 m/s. Photometric simulations capture the overall variability trends, confirming that SOAP can reliably model the impact of both spots and faculae on solar RVs and photometry.