Bayesian evidence for a nonlinear damping model for coronal loop oscillations

TL;DR

This study uses Bayesian analysis to compare linear and nonlinear damping models for solar coronal loop oscillations, finding strong evidence favoring the nonlinear model in most observed cases.

Contribution

It introduces a Bayesian framework to distinguish between damping mechanisms in coronal loops and demonstrates its effectiveness with observational data.

Findings

Bayesian evidence clearly separates the two damping models in observable space.

Most observed cases favor the nonlinear damping model over the linear one.

Quantitative analysis shows the nonlinear model has higher marginal likelihood in the majority of cases.

Abstract

Recent observational and theoretical studies indicate that the damping of solar coronal loop oscillations depends on the oscillation amplitude. We consider two mechanisms, linear resonant absorption and a nonlinear damping model. We confront theoretical predictions from these models with observed data in the plane of observables defined by the damping ratio and the oscillation amplitude. The structure of the Bayesian evidence in this plane displays a clear separation between the regions where each model is more plausible relative to the other. There is qualitative agreement between the regions of high marginal likelihood and Bayes factor for the nonlinear damping model and the arrangement of observed data. A quantitative application to 101 loop oscillation cases observed with SDO/AIA results in the marginal likelihood for the nonlinear model being larger in the majority of them. The cases with conclusive evidence for the nonlinear damping model outnumber considerably those in favor of linear resonant absorption.