Bayesian approach for modeling solar active region global magnetic parameters

TL;DR

This paper introduces a Bayesian inference method to accurately estimate the intrinsic properties of magnetic flux tubes in solar active regions, effectively removing biases caused by magnetic tongues in magnetogram observations.

Contribution

The paper develops a novel Bayesian approach to infer the properties of flux ropes in active regions, improving the accuracy of magnetic flux and tilt angle measurements.

Findings

Method effectively reduces magnetic tongue bias in AR measurements.

Provides statistical distributions of flux rope parameters.

Enhances understanding of flux emergence in solar active regions.

Abstract

Context. Active regions (ARs) appear in the solar atmosphere as a consequence of the emergence of magnetic flux tubes. The presence of elongated magnetic polarities in line-of-sight (LOS) magnetograms indicates the existence of twist in the flux tubes forming them. These polarity elongations, called magnetic tongues, bias the measurement of AR characteristics obtained during their emergence phase (e.g. their tilt angle and magnetic flux, among others). In particular, obtaining a good estimation of the tilt angle evolution plays a key role in constraining flux-transport dynamo models. Aims. In this work we aim to estimate the intrinsic properties of the twisted flux tubes, or flux ropes, that form ARs by quantitatively comparing observed LOS magnetograms with synthetic ones derived from a toroidal magnetic flux tube model. Methods. For this reason, we develop a Bayesian inference method to obtain the statistical distributions of the inferred model parameters. As an example, we apply the method to NOAA AR 10268. Next, we test the results using a synthetic-AR generator to quantify the effect of small scale perturbations over the inferred parameter distributions. Results. We conclude that this method can significantly remove the effects of magnetic tongues on the derived AR global characteristics, providing a better knowledge of the intrinsic properties of the emerging flux rope. Conclusions. These results provide a framework for future analysis of the physical properties of emerging ARs using Bayesian statistics.