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Register a new userDeep Flare Net (DeFN) model for solar flare prediction
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We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., >=M class versus <M class or >=C class versus <C class). From 3x10^5 observation images taken during 2010-2015 by Solar Dynamic Observatory, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 A (T>=10^7 K) and the X-ray and 131 A intensity data 1 and 2 h before an image. For operational evaluation, we divided the database into two for training and testing: the dataset in 2010-2014 for training and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks, formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS=0.80 for >=M-class flares and TSS=0.63 for >=C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.
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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 h after observing images, along with the event occurrence probability. We detected active regions from 3x10^5 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. (2018); for example, line-of-sight/vector magnetograms in the photosphere, brightening in the corona, and the X-ray emissivity 1 and 2 h before an image. We adopted a chronological split of the database into two for training and testing in an operational setting: the dataset 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.
All three components of the current density are required to compute the heating rate due to free magnetic energy dissipation. Here we present a first test of a new model developed to determine if the times of increases in the resistive heating rate in active region (AR) photospheres are correlated with the subsequent occurrence of M and X flares in the corona. A data driven, 3 D, non-force-free magnetohydrodynamic model restricted to the near-photospheric region is used to compute time series of the complete current density and the resistive heating rate per unit volume $(Q(t))$ in each pixel in neutral line regions (NLRs) of 14 ARs. The model is driven by time series of the magnetic field ${bf B}$ measured by the Helioseismic & Magnetic Imager on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series for ${bf B}$ in every AR pixel. For each AR, the cumulative distribution function (CDF) of the values of the NLR area integral $Q_i(t)$ of $Q(t)$ is found to be a scale invariant power law distribution essentially identical to the observed CDF for the total energy released in coronal flares. This suggests that coronal flares and the photospheric $Q_i$ are correlated, and powered by the same process. The model predicts spikes in $Q_i$ with values orders of magnitude above background values. These spikes are driven by spikes in the non-force free component of the current density. The times of these spikes are plausibly correlated with times of subsequent M or X flares a few hours to a few days later. The spikes occur on granulation scales, and may be signatures of heating in horizontal current sheets. It is also found that the times of relatively large values of the rate of change of the NLR unsigned magnetic flux are also plausibly correlated with the times of subsequent M and X flares, and spikes in $Q_i$.
Understanding how energy is released in flares is one of the central problems of solar and stellar astrophysics. Observations of high temperature flare plasma hold many potential clues as to the nature of this energy release. It is clear, however, that flares are not composed of a few impulsively heated loops, but are the result of heating on many small-scale threads that are energized over time, making it difficult to compare observations and numerical simulations in detail. Several previous studies have shown that it is possible to reproduce some aspects of the observed emission by considering the flare as a sequence of independently heated loops, but these studies generally focus on small-scale features while ignoring the global features of the flare. In this paper, we develop a multithreaded model that encompasses the time-varying geometry and heating rate for a series of successively-heated loops comprising an arcade. To validate, we compare with spectral observations of five flares made with the MinXSS CubeSat as well as light curves measured with GOES/XRS and SDO/AIA. We show that this model can successfully reproduce the light curves and quasi-periodic pulsations in GOES/XRS, the soft X-ray spectra seen with MinXSS, and the light curves in various AIA passbands. The AIA light curves are most consistent with long duration heating, but elemental abundances cannot be constrained with the model. Finally, we show how this model can be used to extrapolate to spectra of extreme events that can predict irradiance across a wide wavelength range including unobserved wavelengths.
We conduct a post hoc analysis of solar flare predictions made by a Long Short Term Memory (LSTM) model employing data in the form of Space-weather HMI Active Region Patches (SHARP) parameters calculated from data in proximity to the magnetic polarity inversion line where the flares originate. We train the the LSTM model for binary classification to provide a prediction score for the probability of M/X class flares to occur in next hour. We then develop a dimension-reduction technique to reduce the dimensions of SHARP parameter (LSTM inputs) and demonstrate the different patterns of SHARP parameters corresponding to the transition from low to high prediction score. Our work shows that a subset of SHARP parameters contain the key signals that strong solar flare eruptions are imminent. The dynamics of these parameters have a highly uniform trajectory for many events whose LSTM prediction scores for M/X class flares transition from very low to very high. The results demonstrate the existence of a few threshold values of SHARP parameters that when surpassed indicate a high probability of the eruption of a strong flare. Our method has distilled the knowledge of solar flare eruption learnt by deep learning model and provides a more interpretable approximation, which provides physical insight to processes driving solar flares.
We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data.
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