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

Dattaraj B. Dhuri, Shravan Hanasoge, Mark C.M. Cheung

Research output: Contribution to journalArticle

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—characterized by AR area—for days, before and after M- and X-class flare events; (ii) systematic preflare buildup of free energy in the form of electric currents, suggesting that the associated subsurface magnetic field is twisted; and (iii) intensification of Maxwell stresses in the corona above newly emerging ARs, days before first flares. These results provide insights into flare physics and improving flare forecasting.

Original languageEnglish (US)
Pages (from-to)11141-11146
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume166
Issue number23
DOIs
StatePublished - Jun 4 2019

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Solar Activity
Magnetic Fields
Physics
Weather
Radiation
Lead
Machine Learning

Keywords

  • Machine learning
  • Solar flares
  • Solar magnetic fields
  • Space weather

ASJC Scopus subject areas

  • General

Cite this

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares. / Dhuri, Dattaraj B.; Hanasoge, Shravan; Cheung, Mark C.M.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 166, No. 23, 04.06.2019, p. 11141-11146.

Research output: Contribution to journalArticle

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