Detection of the solar internal flows with numerical simulation and machine learning

TL;DR

This paper introduces a novel approach combining numerical simulations and machine learning to infer solar internal flows, reducing data requirements while maintaining consistency with helioseismology.

Contribution

The study develops a new method that leverages simulations and machine learning to detect solar internal flows with less data than traditional helioseismology.

Findings

Accurately infers large-scale flows at 10 Mm depth from three hourly snapshots.

Method aligns well with helioseismology results.

Reduces data needs for solar interior flow detection.

Abstract

The solar interior is filled with turbulent thermal convection, which plays a key role in the energy and momentum transport and the generation of the magnetic field. The turbulent flows in the solar interior cannot be optically detected due to its significant optical depth. Currently, helioseismology is the only way to detect the internal dynamics of the Sun. However, long-duration data with a high cadence is required and only the temporal average can be inferred. To address these issues effectively, in this study, we develop a novel method to infer the solar internal flows using a combination of radiation magnetohydrodynamic numerical simulations and machine/deep learning. With the application of our new method, we can evaluate the large-scale flow at 10 Mm depth from the solar surface with three snapshots separated by an hour. We also apply it to the observational data. Our method is highly consistent with the helioseismology, whereas the amount of input data is significantly reduced.