Stokes Inversion Techniques with Neural Networks: Analysis of Uncertainty in Parameter Estimation

Abstract

Magnetic fields are responsible for a multitude of solar phenomena, including potentially destructive events such 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 been investigated for a long time. 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 methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, degeneracies, and 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 results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulations and real data samples.