Inferring the Solar Meridional Circulation Flow Profile by Applying Bayesian Methods to Time-distance Helioseismology
Aleczander Herczeg, Jason Jackiewicz

TL;DR
This paper develops a Bayesian MCMC framework to infer the Sun's subsurface meridional circulation flow profile from helioseismic data, revealing potential cycle-dependent structural differences.
Contribution
The study introduces a novel Bayesian approach for inferring solar meridional flow profiles, capable of recovering complex flow structures from long-term helioseismic data.
Findings
Method accurately recovers input flow profiles in tests.
Cycle 23 shows a large single-cell flow structure.
Cycle 24 indicates weaker, possibly double-cell flows.
Abstract
Mapping the large-scale subsurface plasma flow profile within the Sun has been attempted using various methods for several decades. One such flow in particular is the meridional circulation, for which numerous studies have been published. However, such studies often show disagreement in structure. In an effort to constrain the 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 efficient exploration of high-dimensional parameter spaces. This study utilizes helioseismic travel-time difference data covering a span of twenty-one years and a parametrized model of the meridional circulation to find the most likely flow profiles. Tests were carried out on artificial data to determine the ability of this method to recover expected solar-like flow profiles as well as a few extreme…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSolar and Space Plasma Dynamics · Global Energy and Sustainability Research · Market Dynamics and Volatility
