Computing Helioseismic Sensitivity Kernels for the Sun's Large-Scale Internal Flows using Global-Scale Wave-Propagation Simulations

TL;DR

This paper introduces a new method to derive three-dimensional sensitivity kernels for large-scale horizontal flows inside the Sun using global wave-propagation simulations, improving understanding of solar interior dynamics.

Contribution

The authors develop a simulation-based approach to compute 3D sensitivity kernels for solar interior flows, capturing both longitudinal and transverse components, advancing beyond previous approximation methods.

Findings

Kernels exhibit a 'banana' shape similar to Born approximation results.

Transverse flow components are significant and should be included in inversions.

Method enables more accurate probing of the Sun's interior flows.

Abstract

Helioseismic waves observable at the solar surface can be used to probe the properties of the Sun's interior. By measuring helioseismic travel times between different location on the surface, flows and other interior properties can be inferred using so-called sensitivity kernels which relate the amount of travel-time shift with variations in interior proporties. In particular, sensitivity kernels for flows have been developed in the past, using either ray or Born approximation, and have been used to infer solar interior flows such as the meridional circulation which is of particular interest for understanding the structure and dynamics of the Sun. Here we introduce a new method for deriving three-dimensional sensitivity kernels for large-scale horizontal flows in the solar interior. We perform global-Sun wave-propagation simulations through 784 small flow perturbations placed individually in the interior of a simulated Sun, and measure the shifts in helioseismic travel times caused by these perturbations. Each measurement corresponds to a linear equation connecting the flow perturbation velocities and the sensitivity kernels. By solving the resulting large set of coupled linear equations, we derive three-dimensional sensitivity kernels for horizontal flows which have a longitudinal component (parallel to the wave's travel direction) and a transverse component (perpendicular to the wave's travel direction). The kernels exhibit a "banana" shape, similar to kernels derived using Born approximation methods, and show that transverse components are not negligible in inversions for interior flows.