Recipe for inferring sub-surface solar magnetism via local mode-coupling using Slepian basis functions

TL;DR

This paper introduces a novel method using Slepian basis functions to improve the inference of sub-surface solar magnetic fields and Lorentz-stresses through local helioseismic mode-coupling, enhancing imaging precision.

Contribution

It proposes the first use of Slepian functions for parameterizing magnetic perturbations in local helioseismology, increasing data constraints and inference accuracy.

Findings

Slepian functions improve the resolution of magnetic field inferences.

New sensitivity kernels for Lorentz-stresses and magnetic fields are developed.

Enhanced imaging of sub-surface magnetic features like sunspots is demonstrated.

Abstract

Direct seismic imaging of sub-surface flow, sound-speed and magnetic field is crucial for predicting flux tube emergence on the solar surface, an important ingredient for space weather. The sensitivity of helioseismic mode-amplitude cross-correlation to $p$- and $f$-mode oscillations enable formal inversion of such sub-photospheric perturbations. It is well-known that such problems are written in the form of an integral equation that connects the perturbations to the observations via ``sensitivity kernels". While the sensitivity kernels for flow and sound-speed have been known for decades and have been used extensively, formulating kernels for general magnetic perturbations had been elusive. A recent study proposed sensitivity kernels for Lorentz-stresses corresponding to global magnetic fields of general geometry. The present study is devoted to proposing kernels for inferring Lorentz-stresses as well as the solenoidal magnetic field in a local patch on the Sun via Cartesian mode-coupling. Moreover, for the first time in solar physics, Slepian functions are employed to parameterize perturbations in the horizontal dimension. This is shown to increase the number of data constraints in the inverse problem, implying an increase in the precision of inferred parameters. This paves the path to reliably imaging sub-surface solar magnetic features in, e.g., supergranules, sunspots and (emerging) active regions.