Machine learning reveals systematic accumulation of electric current in lead-up to solar flares.

ABSTRACT: Solar flares-bursts of high-energy radiation responsible for severe space weather effects-are a consequence of the occasional destabilization of magnetic fields rooted in active regions (ARs). The complexity of AR evolution is a barrier to a comprehensive understanding of flaring processes and accurate prediction. Although machine learning (ML) has been used to improve flare predictions, the potential for revealing precursors and associated physics has been underexploited. Here, we train ML algorithms to classify between vector-magnetic-field observations from flaring ARs, producing at least one M-/X-class flare, and nonflaring ARs. Analysis of magnetic-field observations accurately classified by the machine presents statistical evidence for (i) ARs persisting in flare-productive states-ch

SUBMITTER: Dhuri DB 

PROVIDER: S-EPMC6561270 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature