Reliable Probability Forecast of Solar Flares: Deep Flare Net-Reliable (DeFN-R)

摘要

We developed a reliable probabilistic solar-flare forecasting model using a deep neural network, named Deep Flare Net-Reliable (DeFN-R). The model can predict the maximum classes of flares that occur in the following 24 hr after observing images, along with the event occurrence probability. We detected active regions from 3?×?10~(5) solar images taken during 2010–2015 by Solar Dynamic Observatory and extracted 79 features for each region, which we annotated with flare occurrence labels of X-, M-, and C-classes. The extracted features are the same as used by Nishizuka et al.; for example, line-of-sight/vector magnetograms in the photosphere, brightening in the corona, and the X-ray emissivity 1 and 2 hr before an image. We adopted a chronological split of the database into two for training and testing in an operational setting: the data set in 2010–2014 for training and the one in 2015 for testing. DeFN-R is composed of multilayer perceptrons formed by batch normalizations and skip connections. By tuning optimization methods, DeFN-R was trained to optimize the Brier skill score (BSS). As a result, we achieved BSS?=?0.41 for ≥C-class flare predictions and 0.30 for ≥M-class flare predictions by improving the reliability diagram while keeping the relative operating characteristic curve almost the same. Note that DeFN is optimized for deterministic prediction, which is determined with a normalized threshold of 50%. On the other hand, DeFN-R is optimized for a probability forecast based on the observation event rate, whose probability threshold can be selected according to users' purposes.