Model comparison for the density structure across solar coronal waveguides

TL;DR

This study uses Bayesian methods to compare different density models in solar waveguides based on observed oscillation data, providing insights into the density structure and physical parameters of the solar atmosphere.

Contribution

It introduces a Bayesian framework for model comparison and averaging to infer cross-field density structuring in solar magnetic waveguides from oscillation data.

Findings

Minimal evidence favoring any model unless oscillations are strongly damped

Model averaging improves parameter inference in damping cases

Quantified differences between density models

Abstract

The spatial variation of physical quantities, such as the mass density, across solar atmospheric waveguides governs the timescales and spatial scales for wave damping and energy dissipation. The direct measurement of the spatial distribution of density, however, is difficult and indirect seismology inversion methods have been suggested as an alternative. We applied Bayesian inference, model comparison, and model-averaging techniques to the inference of the cross-field density structuring in solar magnetic waveguides using information on periods and damping times for resonantly damped magnetohydrodynamic (MHD) transverse kink oscillations. Three commonly employed alternative profiles were used to model the variation of the mass density across the waveguide boundary. Parameter inference enabled us to obtain information on physical quantities such as the Alfv\'en travel time, the density contrast, and the transverse inhomogeneity length scale. The inference results from alternative density models were compared and their differences quantified. Then, the relative plausibility of the considered models was assessed by performing model comparison. Our results indicate that the evidence in favor of any of the three models is minimal, unless the oscillations are strongly damped. In such a circumstance, the application of model-averaging techniques enables the computation of an evidence-weighted inference that takes into account the plausibility of each model in the calculation of a combined inversion for the unknown physical parameters.