A Statistical Study on Photospheric Active-Region Magnetic Nonpotentiality and Associated Flares During Solar Cycles 22-23

YANG Xiao, yangx@nao.cas.cn, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, China
ZHANG HongQi, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences
GAO Yu, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences
GUO Juan, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences
LIN GangHua, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences

Abstract
The accumulation of magnetic nonpotentiality in active regions may provide enormous energy for the severe solar eruptions such as flares and coronal mass ejections, which are prone to affect the solar-terrestrial environment. Using photospheric data obtained by vector magnetograph in Huairou Solar Observing Station of China, we statistically studied the strength evolution of several magnetic nonpotentiality measures, along with a quantified parameter characterizing the active-region magnetic complexity -- effective distance, and their relationship with associated flares during the latest 22nd and 23rd solar cycles. The results show that, the mean unsigned current helicity density, mean free magnetic energy density, and effective distance weighted by the mean strength of magnetic field present high correlation with the variation of the solar cycle. Meanwhile, the magnitude of two mean magnetic shear angles around sunspot penumbras and polarity inversion lines, mean unsigned vertical current density, unsigned force-free field factor, and effective distance reveal weak trend of evolution with the solar cycle. And all of the measures studied show positive correlation with the flare productivity of active regions. Furthermore, verified by a machine learning technique, it shows good flare-prediction performance to utilize these magnetic nonpotentiality and complexity parameters as predictors.