arXiv subjects

Gelu M. Nita

Publications and source records attributed to Gelu M. Nita.

At least 19 recordsLinked to original sources

arXiv2026

MeV-peaked electrons in a coronal source of a solar flare

Analysis of γ-rays in solar flares has suggested a distinct continuum component dominating at MeV energies, which differs from the well-studied X-ray continuum produced by flare-accelerated electrons with spectra steeply falling with energy. The origin, precise spatial location, and extent of this mysterious MeV component have been unknown up to now. If it is produced by bremsstrahlung, such a γ-ray component requires an unusual population of electrons peaked at a few MeV. Here we report a joint study of this MeV-peaked electron population in the 2017-Sep-10 solar flare with Fermi MeV γ-ray data and EOVSA spatially resolved microwave imaging spectroscopy data. We demonstrate that the microwave spectrum from the MeV-peaked distribution has a distinctly different shape from that produced by the electrons with falling energy spectrum. We inspected microwave maps of the flare and identified an evolving area where the measured microwave spectra matched the theoretically expected one for the MeV-peaked population, thus pinpointing the site where this MeV component resides. The locations are in a coronal volume adjacent to the region where prominent release of magnetic energy and bulk electron acceleration were detected, which implies that transport effects play a key role in forming this population.

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arXiv2026

Review of Machine Learning Models for Solar Energetic Particle Prediction

Solar energetic particle (SEP) events have attracted increasing attention due to their significant radiation hazards for aviation, spacecraft electronics, and human missions beyond Earth's magnetosphere. From a scientific perspective, SEP events are intriguing because they arise from a set of physical processes extending from the solar surface and corona through the heliosphere, offering insight into particle acceleration and transport mechanisms that are widely applicable across astrophysics. Therefore, advancing our ability to understand and predict SEP events is essential both for deepening our knowledge of such mechanisms and for safeguarding space technologies and exploration. Traditionally, researchers have modeled SEPs using physics-based simulations and empirical methods. More recently, machine learning (ML) has emerged as a new tool for understanding and predicting SEP events. The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.

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arXiv2026

Modeling of AR 12760 with GX Simulator and Evidence for the Extended Transition Region in Peripheral Active Region Loops

In order to understand solar atmospheric heating it is important to test heating models against spatially resolved data from solar active regions. Here we model a small active region, AR~12760 observed on 2020 April 28, with the GX Simulator package by fitting the EUV intensities in wavebands observed by Solar Dynamics Observatory's Atmospheric Imaging Assembly. We assume the temporally and spatially averaged heating rate along a loop has a power-law dependence on loop length, $L$ and average magnetic field strength along the loop, $B_{avg}$. We find that the best fit heating model for the 211~Å band is $<\!\!Q\!\!>\approx 7\times 10^{-3} (B_{avg}/{100 \mbox{G}})^{1.5}(L/{10^9\mbox{cm}})^{-1}$ erg cm$^{-3}$ s$^{-1}$ but that there is a range of parameters that give qualitatively reasonable fits, which we conclude is due to a correlation between $B_{avg}$ and $L$. In addition, we find that the models of the bands including cooler emission (131 and 171~Å) greatly underestimate the extent of the emission in the legs of the longer loops at the peripheries of the active region that are the strongest contributors of the emission in those bands. We conclude that this is because the modeling assumes that all transition region emission is confined to the loop foot points, but in reality the upper transition region of longer loops extends significantly farther into the loop. It is important to consider this aspect of the transition region in future efforts to model EUV emission.

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arXiv2026

First Detailed MeerKAT Imaging Spectroscopy of a Solar Flare

Radio observations provide powerful diagnostics of energy release, particle acceleration, and transport processes in solar flares. However, despite recent progress in radio interferometric imaging spectroscopy, current instruments still face limitations in image fidelity and resolution, restricting detailed spectroscopic studies of flaring regions. Here we present high-fidelity imaging spectroscopy of a M1.3 GOES class flare with MeerKAT, a precursor to the future-generation array SKA-Mid. Radio emissions at the observed frequencies typically originate in the low corona, offering valuable insights into magnetic reconnection and primary energy-release sites. The obtained images achieve an unprecedented dynamic range exceeding 10^3, enabling simultaneous analysis of bright coherent bursts and faint incoherent emission from the active region. Multiple spatially distinct coherent sources are identified, implying contributions from different populations of accelerated electrons. The incoherent emission extends beyond AIA structures, highlighting MeerKAT's ability to detect dilute but hot plasma invisible to Extreme Ultraviolet instruments. Combined with co-temporal Hard X-ray images and magnetic field extrapolations, the radio sources are located within distinct magnetic structures, further revealing their association with different populations of accelerated electrons. These results demonstrate MeerKAT imaging spectroscopy as powerful diagnostics of solar flares and pave the way for future solar flare studies with SKA-Mid.

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arXiv2025

Magneto-Thermal Coupling and Coronal Heating in Solar Active Regions Inferred from Microwave Observations

The solar corona is much hotter than the photosphere and chromosphere, but the physical mechanism responsible for heating the coronal plasma remains unidentified yet. The thermal microwave emission, which is produced in 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 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 favour 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 the 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.

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arXiv2025

Steady-State Heating of Diffuse Coronal Plasma in a Solar Active Region

Solar corona is much hotter than lower layers of the solar atmosphere-photosphere and chromosphere. The coronal temperature is up to 1MK in quiet sun areas, while up to several MK in active regions, which implies a key role of magnetic field in coronal heating. This means that understanding coronal heating requires reliable modeling of the underlying three-dimensional (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 magneto-thermal 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.

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arXiv2025

Energy budget in the 2017-09-07 "cold" solar flare

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 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.

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arXiv2024

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-Weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager onboard Solar and Heliospheric Observatory) Active Region Patches (SMARP) and Space-Weather HMI (Helioseismic and Magnetic Imager onboard Solar Dynamics Observatory) Active Region Patches (SHARP), which are two currently available data products containing magnetic field characteristics of solar active regions. The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP with SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI-HMI dataset currently spans the period between April 4, 1996, and December 13, 2022, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites (GOES). Time-lagged cross-correlation indicates that a relationship may exist, where some magnetic field properties of active regions lead the flare index in time. Applying the rolling window technique makes it possible to see how this leader-follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.

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arXiv2024

The Random Hivemind: An Ensemble Deep Learner Application to Solar Energetic Particle Prediction Problem

The application of machine learning and deep learning, including the wide use of non-ensemble, conventional neural networks (CoNN), for predicting various phenomena has become very popular in recent years thanks to the efficiencies and the abilities of these techniques to find relationships in data without human intervention. However, certain CoNN setups may not work on some datasets, especially if the parameters passed to it, including model parameters and hyperparameters, are arguably arbitrary in nature and need to continuously be updated with the need to retrain the model. This concern can be partially alleviated by employing committees of neural networks that are identical in terms of input features and architectures, initialized randomly, and "vote" on the decisions made by the committees as a whole. Yet, it is possible for the committee members to "agree" on identical sets of weights and biases for all nodes and edges. Members of these committees also cannot be expanded to accommodate new features and entire committees must therefore be retrained in order to do so. We propose the Random Hivemind (RH) approach, which helps to alleviate this concern by having multiple neural network estimators make decisions based on random permutations of features and prescribing a method to determine the weight of the decision of each individual estimator. The effectiveness of RH is demonstrated through experimentation in the predictions of hazardous Solar Energetic Particle (SEP) events by comparing it to that of using both CoNNs and the aforementioned setup of committees. Our results demonstrate that RH, while having a comparable or better performance than the CoNN and a Committee-based approach, demonstrates a lesser score spread for the individual experiments, and shows promising results with respect to capturing almost every single flare instance leading to SEPs.

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arXiv2023

Predicting Solar Proton Events of Solar Cycles 22-24 using GOES Proton & soft X-Ray flux features

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, Sadykov et al. 2021, we found that it may be sufficient to utilize only proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording $\geq$ 10 MeV $\geq$ 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 one and two SCs, evaluating how transferable a model can be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker TSS and HSS2, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes towards 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.

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arXiv2023

Data-constrained 3D modeling of a solar flare evolution: acceleration, transport, heating, and energy budget

Solar flares are driven by release of the 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 and 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 multi-loop 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, such as the observables synthesized from the model matched the observations. Once the evolving model has been validated 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 to disentangle contributions from the overlapping loops. It reveals new facets of electron acceleration and transport, as well as heating and cooling 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.

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arXiv2023

Statistical Study of the Correlation between Solar Energetic Particles and Properties of Active Regions

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 (CMEs). Observational records of the Space Weather Prediction Center (SWPC NOAA) enable the creation of time-indexed databases containing information about ARs and particle flux enhancements, most widely known as Solar Energetic Particle events (SEPs). 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 ($\approx$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 $δ$ component. The resulting database containing information about SEP events and ARs is publicly available and can be used for the development of Machine Learning (ML) models to predict the occurrence of SEPs.

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arXiv2023

Data-Constrained Solar Modeling with GX Simulator

To facilitate the study of solar active regions and flaring loops, we have created a modeling framework, the freely distributed GX Simulator IDL package, that combines 3D magnetic and plasma structures with thermal and non-thermal models of the chromosphere, transition region, and corona. The package has integrated tools to visualize the model data cubes, compute multi-wavelength emission maps from them, and quantitatively compare the resulting maps with observations. Its object-based modular architecture, which runs on Windows, Mac, and Unix/Linux platforms, offers capabilities that include 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. The application integrates FORTRAN and C++ libraries for fast calculation of radio emission (free-free, gyroresonance, and gyrosynchrotron emission) along with soft and hard X-ray and EUV codes developed in IDL. To facilitate the creation of models, we have developed a fully automatic model production pipeline that downloads the required SDO/HMI vector magnetic field data and (optionally) the contextual SDO/AIA images, 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 a set of interactive tools provided by the graphical user interface. Here we describe the GX Simulator framework and its applications.

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arXiv2021

Energy budget of plasma motions, heating, and electron acceleration in a three-loop solar flare

Non-potential 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, up/down flows, and stochastic (turbulent) plasma motions. The processes or parameters governing partitioning of the released energy between these components is an open question. How these components are distributed between distinct flaring loops and what controls these spatial distributions is also unclear. Here, based on multi-wavelength 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 emissions of this flare displayed a simple impulsive single-spike light curves 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 up and down flows and substantial turbulent velocities. The results of our analysis suggest that (i) the flare occurs in a multi-loop 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; (v) the kinetic energy in the flare footpoints constitute only a minor fraction compared with the thermal and nonthermal energies.

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arXiv2021

Coronal Heating Law Constrained by Microwave Gyroresonant Emission

The question why the solar corona is much hotter than the visible solar surface still puzzles solar researchers. Most theories of the coronal heating involve a tight coupling between the coronal magnetic field and the associated thermal structure. This coupling is based on two facts: (i) the magnetic field is the main source of the energy in the corona and (ii) the heat transfer preferentially happens along the magnetic field, while is suppressed across it. However, most of the information about the coronal heating is derived from analysis of EUV or soft X-ray emissions, which are not explicitly sensitive to the magnetic field. This paper employs another electromagnetic channel -- the sunspot-associated microwave gyroresonant emission, which is explicitly sensitive to both the magnetic field and thermal plasma. We use nonlinear force-free field reconstructions of the magnetic skeleton dressed with a thermal structure as prescribed by a field-aligned hydrodynamics to constrain the coronal heating model. We demonstrate that the microwave gyroresonant emission is extraordinarily sensitive to details of the coronal heating. We infer heating model parameters consistent with observations.

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arXiv2020

Magnetic Reconnection During the Post-Impulsive Phase of a Long-Duration Solar Flare: Bi-Directional Outflows as a Cause of Microwave and X-ray Bursts

Magnetic reconnection plays a crucial role in powering solar flares, production of energetic particles, and plasma heating. However, where the magnetic reconnections occur, how and where the released magnetic energy is transported, and how it is converted to other forms remain unclear. Here we report recurring bi-directional plasma outflows located within a large-scale plasma sheet observed in extreme ultraviolet emission and scattered white light during the post-impulsive gradual phase of the X8.2 solar flare on 2017 September 10. Each of the bi-directional outflows originates in the plasma sheet from a discrete site, identified as a magnetic reconnection site. These reconnection sites reside at very low altitudes ($< 180$ Mm, or 0.26 $R_{\odot}$) above the top of the flare arcade, a distance only $<3\%$ of the total length of a plasma sheet that extends to at least 10 $R_{\odot}$. Each arrival of sunward outflows at the looptop region appears to coincide with an impulsive microwave and X-ray burst dominated by a hot source (10-20 MK) at the looptop, which is immediately followed by a nonthermal microwave burst located in the loopleg region. We propose that the reconnection outflows transport the magnetic energy released at localized magnetic reconnection sites outward in the form of kinetic energy flux and/or electromagnetic Poynting flux. The sunward-directed energy flux induces particle acceleration and plasma heating in the post-flare arcades, observed as the hot and nonthermal flare emissions.

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arXiv2020

Evolution of Flare-accelerated Electrons Quantified by Spatially Resolved Analysis

Nonthermal electrons accelerated in solar flares produce electromagnetic emission in two distinct, highly complementary domains - hard X-rays (HXRs) and microwaves (MWs). This paper reports MW imaging spectroscopy observations from the Expanded Owens Valley Solar Array of an M1.2 flare that occurred on 2017 September 9, from which we deduce evolving coronal parameter maps. We analyze these data jointly with the complementary Reuven Ramaty High-Energy Solar Spectroscopic Imager HXR data to reveal the spatially-resolved evolution of the nonthermal electrons in the flaring volume. We find that the high-energy portion of the nonthermal electron distribution, responsible for the MW emission, displays a much more prominent evolution (in the form of strong spectral hardening) than the low-energy portion, responsible for the HXR emission. We show that the revealed trends are consistent with a single electron population evolving according to a simplified trap-plus-precipitation model with sustained injection/acceleration of nonthermal electrons, which produces a double-powerlaw with steadily increasing break energy.

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arXiv2019

Statistical discrimination of RFI and astronomical transients in 2-bit digitized time domain signals

We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, while both types of transients may be efficiently detected, their natural or artificial nature cannot be distinguished if only a time domain SK analysis is performed. However, these two types of transients become distinguishable from each other in the spectral domain, after a 32-bit FFT operation is performed on the 2-bit time domain voltages. We discuss the implication of these findings on the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests -- such as pulsar or fast radio bursts (FRB) -- in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.

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