Solar Flare Predictive Features Derived from Polarity Inversion Line Masks in Active Regions Using an Unsupervised Machine Learning Algorithm

摘要

The properties of the polarity inversion line (PIL) in solar active regions (ARs) are strongly correlated to flare occurrences. The PIL mask, enclosing the PIL areas, has shown significant potential for improving machine-learning-based flare prediction models. In this study, an unsupervised machine-learning algorithm, Kernel Principle Component Analysis (KPCA), is adopted to directly derive features from the PIL mask and difference PIL mask, and use those features to classify ARs into two categories—non-strong flaring ARs and strong-flaring (M-class and above flares) ARs—for time-in-advance from one hour to 72 hr at a 1 hr cadence. The two best features are selected from the KPCA results to develop random-forest classifiers for predicting flares, and the models are then evaluated and compared to similar models based on the R value and difference R value. The results show that the features derived from the PIL masks by KPCA are effective in predicting flare occurrence, with overall better Fisher ranking scores and similar predictive statistics as the R value characteristics.