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Bayesian seismology of the solar atmosphere

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The study of the solar atmosphere has to be pursued under conditions in which information is incomplete and uncertain. Inference methods need to be employed to diagnose the physical conditions and processes of interest. One of such methods, solar atmospheric seismology, makes use of observed and theoretically predicted properties of waves and oscillations to infer plasma and magnetic field properties. In this talk, we first justify the use of a probabilistic approach to seismology diagnostics. Then, we report on recent results from the application of Bayesian analysis techniques to quantify the plausibility of physical parameter values and alternative models from the observed and modelled wave dynamics. Examples are shown in which parameter inference, model comparison, and model averaging are used to obtain information about the magnetic field strength, the plasma density structuring, and the most plausible damping mechanisms in solar coronal loops and prominence fine structures.

This talk is part of the DAMTP Astro Mondays series.

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