Gaussian process regression of temperature-dependent radial velocities
Federica Rescigno, Khaled Al Moulla

TL;DR
This study uses Gaussian process regression on solar radial velocity data across different spectral formation temperatures to understand activity signals and optimize modeling, revealing temperature-dependent characteristics and correlations with solar convection effects.
Contribution
It introduces a temperature-dependent GP regression approach to analyze solar RVs, highlighting subtle activity-related differences and optimal temperature ranges for minimal RV dispersion.
Findings
Minimal RV dispersion occurs at intermediate temperatures (4000-4750 K).
Hyperparameters show differences between high- and low-activity phases.
Strong correlation between hotter temperature RVs and convection inhibition.
Abstract
Gaussian processes (GPs) described by quasi-periodic covariance functions have in recent years become a widely used tool to model the impact of stellar activity on radial velocity (RV) measurements. We perform a GP regression analysis on solar RV time series measured from spectral segments formed at different temperatures within the photosphere in order to evaluate the relation between the best-fit GP kernel hyperparameters and the observed activity signal as a function of temperature. The posterior distributions of the hyperparameters show subtle differences between high- and low-activity phases and as a function of the spectral formation temperature range, which could have implications on the characteristics of the activity signal and its optimal modelling. For the temperature-dependent RVs, we find that at high and low activity alike, the minimal RV dispersion is obtained at…
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Taxonomy
TopicsGaussian Processes and Bayesian Inference · Winter Sports Injuries and Performance · Gear and Bearing Dynamics Analysis
