Distinguishing between flaring and nonflaring active regions

¹ Université Paris-Saclay, CNRS, CEA, Astrophysique, Instrumentation et Modélisation de Paris-Saclay, 91191 Gif-sur-Yvette, France
e-mail: soumitra.hazra@cea.fr, soumitra.hazra@gmail.com
² Université Paris-Saclay, CNRS, Institut d’Astrophysique Spatiale, 91405 Orsay, France
³ Center of Excellence and Space Sciences India, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, West Bengal, India
⁴ Department of Physical Sciences, Indian Institute of Technology Jodhpur, Jodhpur 342011, India
⁵ Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, West Bengal, India
⁶ University College of Science and Technology, Department of Chemical Technology, University of Calcutta, 92, A.P.C. Road, Kolkata 700009, West Bengal, India

Received: 28 December 2019
Accepted: 19 May 2020

Abstract

Context. Large-scale solar eruptions significantly affect space weather and damage space-based human infrastructures. It is necessary to predict large-scale solar eruptions; it will enable us to protect the vulnerable infrastructures of our modern society.

Aims. We investigate the difference between flaring and nonflaring active regions. We also investigate whether it is possible to forecast a solar flare.

Methods. We used photospheric vector magnetogram data from the Solar Dynamic Observatory’s Helioseismic Magnetic Imager to study the time evolution of photospheric magnetic parameters on the solar surface. We built a database of flaring and nonflaring active regions observed on the solar surface from 2010 to 2017. We trained a machine-learning algorithm with the time evolution of these active region parameters. Finally, we estimated the performance obtained from the machine-learning algorithm.

Results. The strength of some magnetic parameters such as the total unsigned magnetic flux, the total unsigned magnetic helicity, the total unsigned vertical current, and the total photospheric magnetic energy density in flaring active regions are much higher than those of the non-flaring regions. These magnetic parameters in a flaring active region evolve fast and are complex. We are able to obtain a good forecasting capability with a relatively high value of true skill statistic. We also find that time evolution of the total unsigned magnetic helicity and the total unsigned magnetic flux provides a very high ability of distinguishing flaring and nonflaring active regions.

Conclusions. We can distinguish a flaring active region from a nonflaring region with good accuracy. We confirm that there is no single common parameter that can distinguish all flaring active regions from the nonflaring regions. However, the time evolution of the top two magnetic parameters, the total unsigned magnetic flux and the total unsigned magnetic helicity, have a very high distinguishing capability.