Bayesian Magnetohydrodynamic Seismology of Coronal Loops

TL;DR

This paper introduces a Bayesian method using Markov Chain Monte Carlo simulations to infer physical parameters of coronal loops from observed oscillation data, providing more accurate estimates with confidence levels.

Contribution

It presents a novel Bayesian inversion technique for coronal loop oscillations that constrains multiple parameters simultaneously with quantified uncertainties.

Findings

Successfully constrains Alfven travel time and inhomogeneity length-scale.

Estimates are consistent with previous results but include error bars.

Full parameter constraints achieved with density contrast data.

Abstract

We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most probable values of the relevant parameters, for given observed periods and damping times, and to extract their confidence levels. The posterior probability distribution functions are obtained by means of Markov Chain Monte Carlo simulations, incorporating observed uncertainties in a consistent manner. We find well localized solutions in the posterior probability distribution functions for two of the three parameters of interest, namely the Alfven travel time and the transverse inhomogeneity length-scale. The obtained estimates for the Alfven travel time are consistent with previous inversion results, but the method enables us to additionally constrain the transverse inhomogeneity length-scale and to estimate real error bars for each parameter. When observational estimates for the density contrast are used, the method enables us to fully constrain the three parameters of interest. These results can serve to improve our current estimates of unknown physical parameters in coronal loops and to test the assumed theoretical model.