A Prospective New Diagnostic Technique for Distinguishing Eruptive and Non-Eruptive Active Regions

TL;DR

This study introduces a new technique using NLFFF models and Lorentz force metrics to predict eruptive active regions 6 to 16 hours in advance, aiding space weather forecasting and observational targeting.

Contribution

The paper develops a novel predictive metric based on Lorentz force within NLFFF models to distinguish eruptive from non-eruptive active regions.

Findings

The metric successfully differentiates eruptive from non-eruptive regions.

Prediction of the metric is feasible 6 to 16 hours before eruptions.

Initial results demonstrate potential for space weather prediction using LOS magnetograms.

Abstract

Active regions are the source of the majority of magnetic flux rope ejections that become Coronal Mass Ejections (CMEs). To identify in advance which active regions will produce an ejection is key for both space weather prediction tools and future science missions such as Solar Orbiter. The aim of this study is to develop a new technique to identify which active regions are more likely to generate magnetic flux rope ejections. The new technique will aim to: (i) produce timely space weather warnings and (ii) open the way to a qualified selection of observational targets for space-borne instruments. We use a data-driven Non-linear Force-Free Field (NLFFF) model to describe the 3D evolution of the magnetic field of a set of active regions. We determine a metric to distinguish eruptive from non-eruptive active regions based on the Lorentz force. Furthermore, using a subset of the observed magnetograms, we run a series of simulations to test whether the time evolution of the metric can be predicted. The identified metric successfully differentiates active regions observed to produce eruptions from the non-eruptive ones in our data sample. A meaningful prediction of the metric can be made between 6 to 16 hours in advance. This initial study presents an interesting first step in the prediction of CME onset using only LOS magnetogram observations combined with NLFFF modelling. Future studies will address how to generalise the model such that it can be used in a more operational sense and for a variety of simulation approaches.