Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence

Campi, Cristina, Benvenuto, Federico, Massone, Anna Maria, Bloomfield, Shaun, Georgoulis, Manolis K. and Piana, Michele (2019) Feature ranking of active region source properties in solar flare forecasting and the uncompromised stochasticity of flare occurrence. The Astrophysical Journal, 883 (2). p. 150. ISSN 0004-637X

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Official URL: https://doi.org/10.3847/1538-4357/ab3c26

Abstract

Solar flares originate from magnetically active regions but not all solar active regions give rise to a flare. Therefore, the challenge of solar flare prediction benefits by an intelligent computational analysis of physics-based properties extracted from active region observables, most commonly line-of-sight or vector magnetograms of the active-region photosphere. For the purpose of flare forecasting, this study utilizes an unprecedented 171 flare-predictive active region properties, mainly inferred by the Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) in the course of the European Union Horizon 2020 FLARECAST project. Using two different supervised machine learning methods that allow feature ranking as a function of predictive capability, we show that: i) an objective training and testing process is paramount for the performance of every supervised machine learning method; ii) most properties include overlapping information and are therefore highly redundant for flare prediction; iii) solar flare prediction is still - and will likely remain - a predominantly probabilistic challenge.

Item Type: Article
Uncontrolled Keywords: astro-ph.SR, astro-ph.IM, 85A04, 68T05, 92B20
Subjects: F300 Physics
F500 Astronomy
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 16 Sep 2019 14:48
Last Modified: 11 Oct 2019 13:33
URI: http://nrl.northumbria.ac.uk/id/eprint/40700

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