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Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from oneandtwo SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores₂, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.

 

Solar flares are driven by the release of free magnetic energy and its conversion to other forms of energy—kinetic, thermal, and nonthermal. Quantification of partitions between these energy components and their evolution is needed to understand the solar flare phenomenon including nonthermal particle acceleration, transport, and escape as well as the thermal plasma heating and cooling. The challenge of remote-sensing diagnostics is that the data are taken with finite spatial resolution and suffer from line-of-sight (LOS) ambiguity including cases when different flaring loops overlap and project one over the other. Here, we address this challenge by devising a data-constrained evolving 3D model of a multiloop SOL2014-02-16T064620 solar flare of GOES class C1.5. Specifically, we employed a 3D magnetic model validated earlier for a single time frame and extended it to cover the entire flare evolution. For each time frame we adjusted the distributions of the thermal plasma and nonthermal electrons in the model so that the observables synthesized from the model matched the observations. Once the evolving model had been validated in this way, we computed and investigated the evolving energy components and other relevant parameters by integrating over the model volume. This approach removes the LOS ambiguity and permits us to disentangle contributions from the overlapping loops. It reveals new facets of electron acceleration and transport as well as of the heating and cooling of the flare plasma in 3D. We find signatures of substantial direct heating of the flare plasma not associated with the energy loss of nonthermal electrons.

 

The flux of energetic particles originating from the Sun fluctuates during the solar cycles. It depends on the number and properties of active regions (ARs) present in a single day and associated solar activities, such as solar flares and coronal mass ejections. Observational records of the Space Weather Prediction Center NOAA enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as solar energetic particle (SEP) events. In this work, we utilize the data available for solar cycles 21–24 and the initial phase of cycle 25 to perform a statistical analysis of the correlation between SEPs and properties of ARs inferred from the McIntosh and Hale classifications. We find that the complexity of the magnetic field, longitudinal location, area, and penumbra type of the largest sunspot of ARs are most correlated with the production of SEPs. It is found that most SEPs (≈60%, or 108 out of 181 considered events) were generated from an AR classified with the “k” McIntosh subclass as the second component, and these ARs are more likely to produce SEPs if they fall in a Hale class containing aδcomponent. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of machine learning models to predict the occurrence of SEPs.

 

To facilitate the study of solar flares and active regions, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and nonthermal models of the chromosphere, transition region, and corona. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers the ability to either import 3D density and temperature distribution models, or to assign numerically defined coronal or chromospheric temperatures and densities, or their distributions, to each individual voxel. GX Simulator can apply parametric heating models involving average properties of the magnetic field lines crossing a given voxel, as well as compute and investigate the spatial and spectral properties of radio, (sub)millimeter, EUV, and X-ray emissions calculated from the model, and quantitatively compare them with observations. The package includes a fully automatic model production pipeline that, based on minimal users input, downloads the required SDO/HMI vector magnetic field data, performs potential or nonlinear force-free field extrapolations, populates the magnetic field skeleton with parameterized heated plasma coronal models that assume either steady-state or impulsive plasma heating, and generates non-LTE density and temperature distribution models of the chromosphere that are constrained by photospheric measurements. The standardized models produced by this pipeline may be further customized through specialized IDL scripts, or a set of interactive tools provided by the graphical user interface. Here, we describe the GX Simulator framework and its applications.

 

Using multi-wavelength observations, we analysed magnetic field variations associated with a gradual X1.2 flare that erupted on January 7, 2014 in active region (AR) NOAA 11944 located near the disk center. A fast coronal mass ejection (CME) was observed following the flare, which was noticeably deflected in the south-west direction. A chromospheric filament was observed at the eruption site prior to and after the flare. We used SDO/HMI data to perform non-linear force-free field extrapolation of coronal magnetic fields above the AR and to study the evolution of AR magnetic fields prior to the eruption. The extrapolated data allowed us to detect signatures of several magnetic flux ropes present at the eruption site several hours before the event. The eruption site was located under slanted sunspot fields with a varying decay index of 1.0-1.5. That might have caused the erupting fields to slide along this slanted magnetic boundary rather than vertically erupt, thus explaining the slow rise of the flare as well as the observed direction of the resulting CME. We employed sign-singularity tools to quantify the evolutionary changes in the model twist and observed current helicity data, and found rapid and coordinated variations of current systems in both data sets prior to the event as well as their rapid exhaustion after the event onset. 

Abstract Nonpotential magnetic energy promptly released in solar flares is converted to other forms of energy. This may include nonthermal energy of flare-accelerated particles, thermal energy of heated flaring plasma, and kinetic energy of eruptions, jets, upflows/downflows, and stochastic (turbulent) plasma motions. The processes or parameters governing partitioning of the released energy between these components are an open question. How these components are distributed between distinct flaring loops and what controls these spatial distributions are also unclear. Here, based on multiwavelength data and 3D modeling, we quantify the energy partitioning and spatial distribution in the well-observed SOL2014-02-16T064620 solar flare of class C1.5. Nonthermal emission of this flare displayed a simple impulsive single-spike light curve lasting about 20 s. In contrast, the thermal emission demonstrated at least three distinct heating episodes, only one of which was associated with the nonthermal component. The flare was accompanied by upflows and downflows and substantial turbulent velocities. The results of our analysis suggest that (i) the flare occurs in a multiloop system that included at least three distinct flux tubes; (ii) the released magnetic energy is divided unevenly between the thermal and nonthermal components in these loops; (iii) only one of these three flaring loops contains an energetically important amount of nonthermal electrons, while two other loops remain thermal; (iv) the amounts of direct plasma heating and that due to nonthermal electron loss are comparable; and (v) the kinetic energy in the flare footpoints constitutes only a minor fraction compared with the thermal and nonthermal energies.