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Abstract The solar corona is much hotter than the photosphere and chromosphere, but the physical mechanism responsible for heating the coronal plasma remains unidentified. The thermal microwave emission, which is produced in a strong magnetic field above sunspots, is a promising but barely exploited tool for studying the coronal magnetic field and plasma. We analyzed the microwave observations of eight solar active regions obtained with the Siberian Radioheliograph in the years 2022–2024 in the frequency range of 6–12 GHz. We produced synthetic microwave images based on various coronal heating models, and determined the model parameters that provided the best agreement with the observations. The observations and simulations strongly favor either a steady-state (continuous) plasma heating process or high-frequency heating by small energy release events with a short cadence. The average magnetic field strength in a coronal loop was found to decrease with the loop length, following a scaling law with the most probable index of about −0.55. In the majority of cases, the estimated volumetric heating rate was weakly dependent on the magnetic field strength and decreased with the coronal loop length following a scaling law with an index of about −2.5. Among the known theoretical heating mechanisms, the model based on wave transmission or reflection in coronal loops acting as resonance cavities was found to provide the best agreement with the observations. The obtained results did not demonstrate a significant dependence on the emission frequency in the considered range. 

Abstract The solar corona is much hotter than lower layers of the solar atmosphere—the photosphere and chromosphere. The coronal temperature is up to 1 MK in quiet Sun areas, while up to several megakelvins in active regions, which implies a key role of the magnetic field in coronal heating. This means that understanding coronal heating requires reliable modeling of the underlying 3D magnetic structure of an active region validated by observations. Here, we employ synergy between 3D modeling, optically thick gyroresonant microwave emission, and optically thin EUV emission to (i) obtain and validate the best magnetothermal model of the active region and (ii) disentangle various components of the EUV emission known as diffuse component, bright loops, open-field regions, and “moss” component produced at the transition region. Surprisingly, the best thermal model corresponds to high-frequency energy release episodes, similar to a steady-state heating. Our analysis did not reveal significant deviations of the elemental abundances from the standard coronal values. 

Abstract A subclass of early impulsive solar flares, cold flares, was proposed to represent a clean case, where the release of the free magnetic energy (almost) entirely goes to the acceleration of the nonthermal electrons, while the observed thermal response is entirely driven by the nonthermal energy deposition to the ambient plasma. This paper studies one more example of a cold flare, which was observed by a unique combination of instruments. In particular, this is the first cold flare observed with the Expanded Owens Valley Solar Array and, thus, for which the dynamical measurement of the coronal magnetic field and other parameters at the flare site is possible. With these new data, we quantified the coronal magnetic field at the flare site but did not find statistically significant variations of the magnetic field within the measurement uncertainties. We estimated that the uncertainty in the corresponding magnetic energy exceeds the thermal and nonthermal energies by an order of magnitude; thus, there should be sufficient free energy to drive the flare. We discovered a very prominent soft-hard-soft spectral evolution of the microwave-producing nonthermal electrons. We computed energy partitions and concluded that the nonthermal energy deposition is likely sufficient to drive the flare thermal response similarly to other cold flares. 

pyAMPP Premature Release This is an early preview of pyAMPP – the Python Automatic Model Production Pipeline for solar coronal modeling. Expect things to change quickly as we continue development! What's included: Core functionality for generating 3D solar atmosphere models Tools to download HMI and (optionally) AIA data Magnetic field extrapolations (Potential/NLFFF) Synthetic plasma and chromospheric model generation Interactive GUIs: gxampp (time/coord selector) and gxbox (modeling & visualization) Documentation: pyampp.readthedocs.io Getting Started pip install -U pyampp After installing, launch the GUIs with: gxampp # Time & location selector gxbox ... # Run the modeling viewer with your options Heads up: • This is a very early release—features may be missing or change without warning. • Please report bugs or suggestions via issues. Copyright (c) 2024, SUNCAST team. Released under the 3-clause BSD license. 

Abstract 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. 

Abstract 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. 

Abstract Solar flares, driven by prompt release of free magnetic energy in the solar corona 1,2 , are known to accelerate a substantial portion (ten per cent or more) 3,4 of available electrons to high energies. Hard X-rays, produced by high-energy electrons accelerated in the flare 5 , require a high ambient density for their detection. This restricts the observed volume to denser regions that do not necessarily sample the entire volume of accelerated electrons 6 . Here we report evolving spatially resolved distributions of thermal and non-thermal electrons in a solar flare derived from microwave observations that show the true extent of the acceleration region. These distributions show a volume filled with only (or almost only) non-thermal electrons while being depleted of the thermal plasma, implying that all electrons have experienced a prominent acceleration there. This volume is isolated from a surrounding, more typical flare plasma of mainly thermal particles with a smaller proportion of non-thermal electrons. This highly efficient acceleration happens in the same volume in which the free magnetic energy is being released 2 . 

Abstract 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. 

Abstract 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.