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Mapping Subsurface Solar Plasma Flows Using Bayesian Probabilistic Inversion Techniques

Presentation #404.01 in the session Solar Interior.

Published onOct 20, 2022
Mapping Subsurface Solar Plasma Flows Using Bayesian Probabilistic Inversion Techniques

Mapping the subsurface plasma flow profile within the Sun has been attempted using various methods for several decades. Still, a consensus has yet to be reached as to the nature of these flows. In particular, numerous studies have been published showing disagreement for the solar meridional circulation.

In an effort to disentangle the true flow profile from the data, a Bayesian Markov chain Monte Carlo framework has been developed to take advantage of the advances in computing power that allow for the exploration of highly-dimensional parameter spaces in reasonable time frames. This study utilizes pre-processed travel-time difference data covering a span of twenty-one years and a parametrized model of the meridional circulation from Liang et al. 2018 to find the most likely flow profiles that match observed travel-time differences across the two most recent solar cycles.

Tests were carried out on artificial data to determine the ability of this method to recover both typical, solar-like flow profiles as well as unusual and more extreme flow profiles. The method described in this work was capable of recovering the initial flow profiles in all cases barring extreme noise. Importantly, the model is able to confidently recover both single- and double-celled flow profiles.

Findings indicate significant differences in modeled flow profiles between the two solar cycles in terms of both magnitude and morphology. The most likely solutions show that solar cycle 23 has a large, single-celled profile, while cycle 24 shows some indication of a double-celled structure.

In addition, preliminary results are presented regarding the application of similar methods to the study of supergranular flow using helioseismic holography.


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