Parameters Derived from the SDO/HMI Vector Magnetic Field Data: Potential to Improve Machine-learning-based Solar Flare Prediction Models

Abstract

It is well established that solar flares and coronal mass ejections (CMEs) are powered by the free magnetic energy stored in volumetric electric currents in the corona, predominantly in active regions (ARs). Much effort has been made to search for eruption-related signatures from magnetic field observed mostly in the photosphere; and the signatures are further employed for predicting flares and CMEs. The parameters in the Space-weather HMI Active Region Patches (SHARP) data from the Solar Dynamics Observatory /HMI observation of vector magnetic field are designed and generated for this purpose. In this paper, we report research done on modification of these SHARP parameters with an attempt to improve flare prediction. The newly modified parameters are weighed heavily by magnetic polarity inversion lines (PIL) with high magnetic gradient, as suggested by Schrijver, by multiplying the parameters with a PIL mask. We demonstrate that the number of the parameters that can well discriminate erupted and nonerupted ARs increases significantly by a factor of two, in comparison with the original parameters. This improvement suggests that the high-gradient PILs are tightly related with solar eruption that agrees with previous studies. This also provides new data that possess potential to improve the machine-learning-based solar flare prediction models.