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RESEARCH PRODUCT

A prospective new diagnostic technique for distinguishing eruptive and noneruptive active regions

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This research has received funding from the Science and Technology Facilities Council (UK) through the consolidated grant ST/N000609/1 and the European Research Council (ERC) under the European Union Horizon 2020 research and innovation program (grant agreement No. 647214). This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The equipment was funded by BEIS capital funding via STFC capital grants ST/P002293/1, ST/R002371/1, and ST/S002502/1, Durham University and STFC operations grant ST/R000832/1. DiRAC is part of the National e-Infrastructure. S.L.Y. would like to acknowledge STFC for support via the Consolidated Grants SMC1/YST025 and SMC1/YST037. D.H.M. would like to thank both the UK STFC and the ERC (Synergy Grant: WHOLE SUN, Grant Agreement No. 810218) for financial support. 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 nonlinear 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 noneruptive 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 noneruptive ones in our data sample. A meaningful prediction of the metric can be made between 6 and 16 hr in advance. This initial study presents an interesting first step in the prediction of CME onset using only line-of-sight magnetogram observations combined with NLFFF modeling. Future studies will address how to generalize the model such that it can be used in a more operational sense and for a variety of simulation approaches. Publisher PDF Peer reviewed