Bayesian Inversion of Stokes Profiles

Abstract

[abridged] Inversion techniques are the most powerful methods to obtain information about the thermodynamical and magnetic properties of solar and stellar atmospheres. In the last years, we have witnessed the development of highly sophisticated inversion codes that are now widely applied to spectro-polarimetric observations. The majority of these inversion codes are based on the optimization of a complicated non-linear merit function. However, no reliable and statistically well-defined confidence intervals can be obtained for the parameters inferred from the inversions. A correct estimation of the confidence intervals for all the parameters that describe the model is mandatory. Additionally, it is fundamental to apply efficient techniques to assess the ability of models to reproduce the observations and to what extent the models have to be refined or can be simplified. Bayesian techniques are applied to analyze the performance of the model to fit a given observed Stokes vector. The posterior distribution, is efficiently sampled using a Markov Chain Monte Carlo method. For simplicity, we focus on the Milne-Eddington approximate solution of the radiative transfer equation and we only take into account the generation of polarization through the Zeeman effect. However, the method is extremely general and other more complex forward models can be applied. We illustrate the ability of the method with the aid of academic and realistic examples. We show that the information provided by the posterior distribution turns out to be fundamental to understand and determine the amount of information available in the Stokes profiles in these particular cases.

15 pages, 12 figures, accepted for publication in A&A

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