Plasma Heating Induced by Tadpole-like Downflows in the Flaring Solar Corona.

As one of the most spectacular energy release events in the solar system, solar flares are generally powered by magnetic reconnection in the solar corona. As a result of the re-arrangement of magnetic field topology after the reconnection process, a series of new loop-like magnetic structures are often formed and are known as flare loops. A hot diffuse region, consisting of around 5-10 MK plasma, is also observed above the loops and is called a supra-arcade fan. Often, dark, tadpole-like structures are seen to descend through the bright supra-arcade fans. It remains unclear what role these so-called supra-arcade downflows (SADs) play in heating the flaring coronal plasma. Here we show a unique flare observation, where many SADs collide with the flare loops and strongly heat the loops to a temperature of 10-20 MK. Several of these interactions generate clear signatures of quasi-periodic enhancement in the full-Sun-integrated soft X-ray emission, providing an alternative interpretation for quasi-periodic pulsations that are commonly observed during solar and stellar flares.

A deep learning virtual instrument for monitoring extreme UV solar spectral irradiance.

Measurements of the extreme ultraviolet (EUV) solar spectral irradiance (SSI) are essential for understanding drivers of space weather effects, such as radio blackouts, and aerodynamic drag on satellites during periods of enhanced solar activity. In this paper, we show how to learn a mapping from EUV narrowband images to spectral irradiance measurements using data from NASA's Solar Dynamics Observatory obtained between 2010 to 2014. We describe a protocol and baselines for measuring the performance of models. Our best performing machine learning (ML) model based on convolutional neural networks (CNNs) outperforms other ML models, and a differential emission measure (DEM) based approach, yielding average relative errors of under 4.6% (maximum error over emission lines) and more typically 1.6% (median). We also provide evidence that the proposed method is solving this mapping in a way that makes physical sense and by paying attention to magnetic structures known to drive EUV SSI variability.

Machine learning reveals systematic accumulation of electric current in lead-up to solar flares.

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. Although 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 vector-magnetic-field observations from flaring ARs, producing at least one M-/X-class flare, and nonflaring ARs. Analysis of magnetic-field observations accurately classified by the machine presents statistical evidence for (i) ARs persisting in flare-productive states-characterized by AR area-for days, before and after M- and X-class flare events; (ii) systematic preflare buildup of free energy in the form of electric currents, suggesting that the associated subsurface magnetic field is twisted; and (iii) intensification of Maxwell stresses in the corona above newly emerging ARs, days before first flares. These results provide insights into flare physics and improving flare forecasting.