Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation

TL;DR

This paper introduces neural network-based inversion techniques for solar magnetic field analysis that provide both accurate parameter estimates and reliable uncertainty intervals, improving upon traditional methods.

Contribution

It presents end-to-end neural network inversion codes that estimate solar atmospheric parameters and their uncertainties, adaptable to various models and data types.

Findings

High accuracy in parameter estimation demonstrated

Reliable uncertainty intervals provided

Effective on both simulated and real data

Abstract

Magnetic fields are responsible for a multitude of Solar phenomena, including such destructive events as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has long been investigated. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion technique methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, the degeneracies, and the uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated directly into the model. It is demonstrated that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulation and real data samples.