Bayesian Coronal Seismology

Abstract

In contrast to the situation in a laboratory, the study of the solar atmosphere has to be pursued without direct access to the physical conditions of interest. Information is therefore incomplete and uncertain and inference methods need to be employed to diagnose the physical conditions and processes. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves to infer plasma and magnetic field properties. A recent development in solar atmospheric seismology consists in the use of inversion and model comparison methods based on Bayesian analysis. In this paper, the philosophy and methodology of Bayesian analysis are first explained. Then, we provide an account of what has been achieved so far from the application of these techniques to solar atmospheric seismology and a prospect of possible future extensions.

19 pages, accepted in Advances in Space Research

References in corpus (5)

• Bayes in the sky: Bayesian inference and model selection in cosmology
• Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
• Analytic approximate seismology of transversely oscillating coronal loops
• Prominence seismology using the period ratio of transverse thread oscillations
• Determination of the cross-field density structuring in coronal waveguides using the damping of transverse waves