The uncertainty of magnetic fields in 3D non-local thermodynamic equilibrium inversions

TL;DR

This paper introduces a meshfree 3D inversion method that enforces magnetic field solenoidality and estimates uncertainty efficiently, using a Monte Carlo approach and a new confidence metric, demonstrated through numerical experiments.

Contribution

It presents a novel meshfree 3D inversion technique ensuring magnetic field solenoidality and a Monte Carlo-based uncertainty estimation method with a new confidence metric.

Findings

The method effectively enforces solenoidality in 3D inversions.

Uncertainty estimation increases computational time by only about twofold.

Numerical experiments validate the feasibility of the proposed approach.

Abstract

We describe our approach to solve the problem of ensuring the solenoidality of the magnetic field vector in three-dimensional (3D) inversions, as well as the estimation of the uncertainty in the inferred magnetic field. The solenoidality of the magnetic field vector is often disregarded in the inversion of spectropolarimetric data due to limitations in the traditional one-dimensional inversion techniques. We propose a method to ensure the solenoidal condition in 3D inversions based on our meshfree approach. The increase in dimensionality with respect to the 1D inversion techniques is such that some of the traditional methods to determine the uncertainties become unfeasible. We propose a method based on a Monte Carlo approach to determine the uncertainty of the magnetic field inference. Due to the physics of the problem, we can compute the uncertainty increasing the total required computational time by just a factor of about two. We also propose a metric to quantify the uncertainty to describe the degree of confidence of the magnetic field inference. Finally, we perform a numerical experiment to demonstrate the feasibility of both the method and the metric proposed to quantify the uncertainty.