Solar flares -bursts of high-energy radiation responsible for severe space-weather effects -are a consequence of the occasional destabilization of magnetic fields rooted in active regions (ARs). The complexity of AR evolution is a barrier to a comprehensive understanding of flaring processes and accurate prediction. Though machine learning (ML) has been used to improve flare predictions, the potential for revealing precursors and associated physics has been underexploited. Here, we train ML algorithms to classify between vectormagnetic-field observations from flaring ARs, producing at least one M-/X-class flare, and non-flaring ARs. Analysis of magnetic-field observations accurately classified by the machine presents statistical evidence for (1) ARs persisting in flare-productive states -characterized by AR area -for days, before and after M-and X-class flare events, (2) systematic pre-flare build-up of free energy in the form of electric currents, suggesting that associated subsurface magnetic field is twisted, (3) intensification of Maxwell stresses in the corona above newly emerging ARs, days before first flares. These results provide new insights into flare physics and improving flare forecasting.Solar Flares | Space Weather | Solar Magnetic Fields | Machine Learning
Solar flares are explosions in the solar atmosphere that release intense bursts of short-wavelength radiation and are capable of producing severe space-weather. Flares release free energy built up in coronal fields, which are rooted in active regions (ARs) on the photosphere, via magnetic reconnection. The exact processes that lead to reconnection are not fully known and therefore reliable forecasting of flares is challenging. Recently, photospheric magnetic-field data has been extensively analyzed using machine learning (ML) and these studies suggest that flare-forecasting accuracy does not strongly depend on how long in advance flares are predicted. Here, we use ML to understand the evolution of AR magnetic fields before and after flares. We explicitly train convolutional neural networks (CNNs) to classify Solar Dynamics Observatory/Helioseismic and Magnetic Imager line-of-sight magnetograms into ARs producing at least one M- or X-class flare or as nonflaring. We find that flaring ARs remain in flare-productive states—marked by recall > 60% with a peak of ∼80%—days before and after flares. We use occlusion maps and statistical analysis to show that the CNN pays attention to regions between the opposite polarities from ARs and the CNN output is dominantly decided by the total unsigned line-of-sight flux of ARs. Using synthetic bipole magnetograms, we find spurious dependencies of the CNN output on magnetogram dimensions for a given bipole size. Our results suggest that it is important to use CNN designs that eliminate such artifacts in CNN applications for processing magnetograms and, in general, solar image data.
We analyse a linear lattice Boltzmann (LB) formulation for simulation of linear acoustic wave propagation in heterogeneous media. We employ the single-relaxation-time Bhatnagar-Gross-Krook (BGK) as well as the general multi-relaxation-time (MRT) collision operators. By calculating the dispersion relation for various 2D lattices, we show that the D2Q5 lattice is the most suitable model for the linear acoustic problem. We also implement a grid-refinement algorithm for the LB scheme to simulate waves propagating in a heterogeneous medium with velocity contrasts. Our results show that the LB scheme performance is comparable to the classical second-order finite-difference schemes. Given its efficiency for parallel computation, the LB method can be a cost effective tool for the simulation of linear acoustic waves in complex geometries and multiphase media.
Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events—such as flares and coronal mass ejections, and therefore understanding emergence is useful for space-weather forecasting. Evidence of any pre-emergence signatures will also shed light on subsurface processes responsible for emergence. In this paper, we present a first analysis of EARs from the Solar Dynamics Observatory/Helioseismic Emerging Active Regions dataset using deep convolutional neural networks (CNN) to characterize pre-emergence surface magnetic field properties. The trained CNN classifies between pre-emergence line-of-sight magnetograms and a control set of nonemergence magnetograms with a true skill statistic (TSS) score of approximately 85% about 3 hr prior to emergence and approximately 40% about 24 hr prior to emergence. Our results are better than a baseline classification TSS obtained using discriminant analysis (DA) of only the unsigned magnetic flux, although a multivariable DA produces TSS values consistent with the CNN. We develop a network-pruning algorithm to interpret the trained CNN and show that the CNN incorporates filters that respond positively as well as negatively to the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms, we demonstrate that the CNN output is sensitive to the length scale of the magnetic regions, with small-scale and intense fields producing maximum CNN output and possibly a characteristic pre-emergence pattern. Given increasing popularity of deep learning, the techniques developed here to interpret the trained CNN—using network pruning and synthetic data—are relevant for future applications in solar and astrophysical data analysis.
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