arXiv subjects

Alexander G. Kosovichev

Publications and source records attributed to Alexander G. Kosovichev.

At least 19 recordsLinked to original sources

arXiv2026

3D Radiative MHD Modeling of Particle Beam Heating of the Solar Atmosphere

While solar flares are primarily associated with enhanced ultraviolet and X-ray emission, a subset of flares exhibit significant continuum brightening in visible light and are classified as white-light flares (WLFs). Despite extensive observational and modeling efforts, the physical mechanisms responsible for the compact, short-lived photospheric brightenings in WLF kernels observed during the impulsive phase of solar flares remain uncertain. Thick-target electron-beam models typically deposit energy in the upper chromosphere, and their ability to reproduce the magnitude and spatial localization of photospheric continuum enhancements observed in white-light flare kernels remains an open question. To investigate the role of self-consistent atmospheric structuring and multidimensional hydrodynamic transport in flare energy deposition, we perform three-dimensional radiative MHD simulations of electron-beam heating using the StellarBox code for a beam energy flux density of $10^{12}$ erg\,s$^{-1}$\,cm$^{-2}$ and low-energy cutoffs of 10--25\,keV. We then compute Fe\,I 6173\,Å~Stokes profiles using the RH 1.5D radiative transfer code for direct comparison with Helioseismic and Magnetic Imager (HMI) observations. The simulations produce strong upper-chromospheric heating, multiple shock fronts, and continuum enhancements up to a factor of 2.5 relative to pre-flare levels, comparable to continuum enhancements observed during strong X-class white-light flares. Comparison with one-dimensional RADYN simulations highlights the influence of fine-scale structuring on flare dynamics and continuum emission that arises in three-dimensional geometry.

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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

Time-Dependence of Subsurface Solar Convection Using the Time-Distance Deep-Focus Method

We re-examine the deep-focus methodology of time-distance helioseismology previously used to estimate the power spectrum of the solar convection at a depth of about 30 Mm, which was found to be significantly weaker than predicted by theory and simulations. The Global Acoustic, Linearized Euler (GALE) and Eulerian Lagrangian (EULAG) codes are used to generate ground-truth simulations to evaluate the accuracy of the inferred convective power spectrum. This validation process shows that the power spectrum derived using the time-distance methodology diverges significantly from ground truth beyond spatial scales corresponding to the spherical harmonic degree $\ell=15$--$30$ because of the limited resolution of helioseismic measurements at that depth. However, the power estimated at larger spatial scales ($\ell<15$) is sufficiently accurate. We then apply the methodology to solar data selected from throughout Solar Cycle 24 and find some evidence that the magnitude of the convective power changes throughout the Cycle. An average of the convective power across the Solar Cycle reveals a spectrum that is qualitatively similar to previous estimates, though about half an order of magnitude greater. The disagreement between observations of solar convection and the magnitudes predicted by simulations persists.

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arXiv2026

Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers

Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches for forecasting the continuum intensity decrease associated with AR emergence, this work expands the modeling with new architectures and targets. We investigate a sliding-window Transformer architecture to forecast continuum intensity evolution up to 12 hours ahead using data from 46 ARs observed by SDO/HMI. We conduct a systematic ablation study to evaluate two key components: (1) the inclusion of a temporal 1D convolutional (Conv1D) front-end and (2) a novel `Early Detection' architecture featuring attention biases and a timing-aware loss function. Our best-performing model, combining the Early Detection architecture without the Conv1D layer, achieved a Root Mean Square Error (RMSE) of 0.1189 (representing a 10.6% improvement over the LSTM baseline) and an average advance warning time of 4.73 hours (timing difference of -4.73h), even under a stricter emergence criterion than previous studies. While the Transformer demonstrates superior aggregate timing and accuracy, we note that this high-sensitivity detection comes with increased variance compared to smoother baseline models. However, this volatility is a necessary trade-off for operational warning systems: the model's ability to detect micro-changes in precursor signals enables significantly earlier detection, outweighing the cost of increased noise. Our results demonstrate that Transformer architectures modified with early detection biases, when used without temporal smoothing layers, provide a high-sensitivity alternative for forecasting AR emergence that prioritizes advance warning over statistical smoothness.

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arXiv2026

SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions

The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore, it is crucial to detect Active Regions (ARs) before they start forming on the solar surface. This will enable the development of early-warning capabilities for upcoming space weather disturbances. For this reason, we prepared the Solar Active Region Emergence Dataset (SolARED). The dataset is derived from full-disk maps of the Doppler velocity, magnetic field, and continuum intensity, obtained by the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). SolARED includes time series of remapped, tracked, and binned data that characterize the evolution of acoustic power of solar oscillations, unsigned magnetic flux, and continuum intensity for 50 large ARs before, during, and after their emergence on the solar surface, as well as surrounding areas observed on the solar disc between 2010 and 2023. The resulting ML-ready SolARED dataset is designed to support enhancements of predictive capabilities, enabling the development of operational forecasts for the emergence of active regions. The SolARED dataset is available at https://sun.njit.edu/sarportal/, through an interactive visualization web application.

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arXiv2026

Simulations of Global Solar Convection with a Fully Compressible CHORUS++ Code

Fluid-dynamics simulations of global solar convection are a critically important tool for assessing the dynamics of the solar interior. However, simulation studies with a fully compressible hydrodynamics code are not yet common. The CHORUS++ code solves robustly and efficiently the fully compressible hydrodynamics equations using a compact local spectral method and semi-unstructured grid system. Using the CHORUS++ code, we simulate the solar interior plasma flows from 0.7 to 0.99 of the solar radius using the values of the solar total luminosity and the sidereal rotation rate. In this paper, we analyze the simulated global flow structures before the statistically stable state to assess the compressibility of the plasma flows obtained with the fully compressible hydrodynamic code. The divergence of mass flux with the compressible model is overall small in the examined state before reaching the equilibrium state of the convection, which implies that the differences between the fully compressible flows and those obtained with the anelastic, incompressible, or linear-equation models are small in the simulated inner part of the convection zone. Although a fully-relaxed stationary convection has not been achieved yet in the examined state, the model qualitatively reproduces the solar-type differential rotation. Simulations for longer periods are needed to achieve the system relaxation state. This work, assessing the early phase of the hydrodynamic evolution of solar convection, is our first step toward a better understanding of the nature of solar convection and the dynamo processes.

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arXiv2026

Helioseismic Evidence That the Solar Dynamo Originates near the Tachocline

The exact location of the solar dynamo remains uncertain--whether it operates primarily in the near-surface shear layer, throughout the entire convection zone, or near the tachocline, a region of sharp transition in the solar rotation, located at the base of the convection zone, approximately 200,000 km beneath the surface. Various studies have supported each of these possibilities. Notably, the solar magnetic "butterfly" diagram and the pattern of zonal flows ("torsional oscillations") exhibit strikingly similar characteristics, suggesting a link between magnetic field evolution and solar flows. Since magnetic fields cannot be measured directly in the deep solar interior, torsional oscillations and rotation gradients are employed as diagnostic proxies. Our analysis reveals that the gradient of rotation displays "butterfly"-like behavior near the tachocline, which is similar to the magnetic butterfly diagram at the surface. This result supports the idea that the solar dynamo has a deep-seated origin, likely operating either near the tachocline or throughout the convection zone, thereby disfavoring the recent scenario of a shallow, near-surface dynamo. This finding may also have important implications for understanding how stellar dynamos operate in general.

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arXiv2025

Activity-Cycle Variations of Convection Scales in Subsurface Layers of the Sun

We use subsurface-flow velocity maps inferred by time--distance helioseismology from Doppler measurements with the Helioseismic and Magnetic Imager (HMI) of the Solar Dynamics Observatory (SDO) to investigate variations of large-scale convection during Solar Cycles 24 and 25 in the 19-Mm-deep layer. The spatial power spectra of the horizontal-flow divergence reveal well-defined characteristic scales of solar supergranulation in the upper 4 Mm layer, while the giant-cell scale is prominent below levels of d ~ 8 Mm. We find that the characteristic scales of supergranulation remain stable while the giant scales increase during the periods of the 11-year activity cycle maxima. The power of the giant-cell scales increases with the enhancement of solar activity. This may be due to large-scale flows around active regions and, presumably, solar-cycle variations of the convection-zone stratification.

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arXiv2025

Fe I 6173 Å Emission During White-Light Solar Flares

Between 2017 and 2024, the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory has observed numerous white-light solar flares (WLFs). HMI spectro-polarimetric observations of certain WLFs, in particular the X9.3 flare of September 6, 2017, reveal one or more locations within the umbra or along the umbra/penumbra boundary of the flaring active region where the Fe\,I 6173\,Å~line briefly goes into full emission, indicating significant heating of the photosphere and lower chromosphere. For five flares featuring Fe\,I 6173\,Å~line-core emission, we perform spectro-polarimetric analysis using HMI 90\,s cadence Stokes data. For all investigated flares, line-core emission is observed to last for a single 90\,s frame and is either concurrent with or followed by an increase in the line continuum intensity lasting one to two frames (90\,s\,--\,180\,s). Additionally, permanent changes to the Stokes Q, U, and/or V profiles were observed, indicating long-lasting non-transient changes to the photospheric magnetic field. These emissions coincided with local maxima in hard X-ray emission observed by Konus-Wind, as well as local maxima in the time derivative of soft X-ray emission observed by GOES 16-18. Comparison of the Fe\,I 6173\,Å~line profile synthesis for the ad-hoc heating of the initial empirical VAL-S umbra model and quiescent Sun (VAL-C-like) model indicates that the Fe\,I 6173\,Å~line emission in the white-light flare kernels could be explained by the strong heating of initially cool photospheric regions.

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arXiv2025

Investigating Variations in Solar Differential Rotation by Helioseismology

Helioseismic signatures of dynamo waves have recently been discovered in variations of the solar differential rotation, offering valuable insights into the type of dynamo mechanism operating in the solar convection zone. To characterize these variations, we analyze p-mode frequency-splitting data estimated using time intervals of various lengths to enhance the signal-to-noise ratio in inversions of zonal flows. We introduce a novel time-dependent inversion method that inherently smooths the solution over time, eliminating the need for separate post-processing smoothing. By applying this approach to observational data from the SOHO Michelson Doppler Imager, SDO Helioseismic Magnetic Imager, and Global Oscillation Network Group, we identify similar dynamo wave patterns in both the zonal acceleration and the zonal flow throughout the entire convection zone. Our analysis shows that while using longer time series smooths out temporal variations, the fundamental features observed in the short time series (i.e. 72-day long) persist when inverting datasets covering different time periods. These findings reinforce earlier detections and offer further validation of solar dynamo models. We additionally investigate the dimensionless radial gradient of rotation. Its value is close to -1 and increases in the deeper layers, remaining nearly constant from the equator to mid-latitudes within the depth range of 13 to 35 Mm below the surface; the results at high latitudes remain somewhat inconclusive. The variation of this quantity displays a torsional oscillation-like pattern, albeit with certain differences.

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arXiv2025

Temporal Variations in Asteroseismic Frequencies of KIC 6106415: Insights into the Solar-Stellar Activity from GOLF and Kepler Observations

The Global Oscillations at Low Frequencies instrument aboard the Solar and Heliospheric Observatory has provided over two decades of continuous, high-precision data, enabling detailed measurements of the Sun's oscillation frequencies. These oscillations, analyzed through Doppler velocity shifts, offer invaluable insights into the Sun's internal structure and dynamics using the methods of helioseismology. This methodology has been extended beyond the Sun to the study of other stars, leveraging data from various space missions. Notably, NASA's Kepler mission, in operation from 2009 until 2018, observed over 500,000 stars, analyzing brightness variations over time and generating a vast database for asteroseismic studies. This investigation focuses on the solar-type star KIC 6106415, comparing its oscillation frequencies with those derived from GOLF data. By analyzing frequency patterns and mode lifetimes, we explore the similarities and differences in internal structures, stellar evolution, and magnetic activity cycles between KIC 6106415 and the Sun. Our analysis reveals that KIC 6106415 exhibits starspot numbers similar to the Sun, peaking at an estimated 175, which is consistent with its faster rotation rate. The data suggest that KIC 6106415 may have shorter magnetic activity cycles than the Sun, reinforcing the established link between stellar rotation and magnetic field generation in solar-type stars.

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arXiv2025

Properties of Turbulent Convection and Large-Scale Flows in a Rotating F-type Star Revealed by 3D Realistic Radiative Hydrodynamic Simulations

The nonlinear coupling between stellar convection and rotation is of great interest because it relates to understanding both stellar evolution and activity. We investigated the influence of rotation and the Coriolis force on the dynamics and thermodynamic structure of an F-type main-sequence star with a shallow outer convection zone. We performed a series of 3D radiative hydrodynamic simulations of a 1.47Msun star for different rotation rates (periods of rotation 1 and 14 days) and with computational domains placed at latitudes of 0degrees (equator), 30degrees, and 60degrees. Because the star has a relatively shallow convection zone (28.5 Mm thick or about 2.81% R*), we model its dynamics from the upper layers of the radiative zone, the whole convection zone, and the low atmosphere. The simulation results show a weak shift of the ionization zones to the photosphere and a decrease of the stellar radius by about 29 km at the equator and about 58 km at higher latitudes in the presence of rotation with a period of 1 day. The models presented reveal the formation of radial differential rotation, meridional flows, latitude-dependent roll-like structures of convection, a tachocline, the presence of a gravity-darkening effect, and others. In this paper, we primarily discuss the properties of the outer convection zone for different rotation rates. Detailed analysis of the properties of the tachocline, the overshoot layer, and small-scale turbulence will be discussed in follow-on papers.

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arXiv2025

Solar oblateness & asphericities temporal variations: outstanding some unsolved issues

Solar oblateness has been the subject of several studies dating back to the nineteenth century. Despite diffculties, both theoretical and observational, tangible results have been achieved. However, variability of the solar oblateness with time is still poorly known. How the solar shape evolves with the solar cycle has been a challenging problem. Analysis of the helioseismic data, which are the most accurate measure of the solar structure up to now, leads to the determination of asphericity coeffcients which have been found to change with time. We show here that by inverting even coeffcients of f-mode oscillation frequency splitting to obtain the oblateness magnitude and its temporal dependence can be inferred. It is found that the oblateness variations lag the solar activity cycles by about 3 years. A major change occurred between solar cycles 23 and 24 is that the oblateness was greater in cycle 24 despite the lower solar activity level. Such results may help to better understand the near-subsurface layers as they strongly impacts the internal dynamics of the Sun and may induce instabilities driving the transport of angular momentum.

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arXiv2024

Solar Active Regions Emergence Prediction Using Long Short-Term Memory Networks

We developed Long Short-Term Memory (LSTM) models to predict the formation of active regions (ARs) on the solar surface. Using the Doppler shift velocity, the continuum intensity, and the magnetic field observations from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI), we have created time-series datasets of acoustic power and magnetic flux, which are used to train LSTM models on predicting continuum intensity, 12 hours in advance. These novel machine learning (ML) models are able to capture variations of the acoustic power density associated with upcoming magnetic flux emergence and continuum intensity decrease. Testing of the models' performance was done on data for 5 ARs, unseen from the models during training. Model 8, the best performing model trained, was able to make a successful prediction of emergence for all testing active regions in an experimental setting and three of them in an operational. The model predicted the emergence of AR11726, AR13165, and AR13179 respectively 10, 29, and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet areas on the solar disc. This work sets the foundations for ML-aided prediction of solar ARs.

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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

Forecasting SEP Events During Solar Cycles 23 and 24 Using Interpretable Machine Learning

Prediction of the Solar Energetic Particle (SEP) events garner increasing interest as space missions extend beyond Earth's protective magnetosphere. These events, which are, in most cases, products of magnetic reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly, space exploration. In this work, we utilize the recently developed dataset that combines the Solar Dynamics Observatory/Helioseismic and Magnetic Imager's (SDO/HMI) Space weather HMI Active Region Patches (SHARP) and the Solar and Heliospheric Observatory/Michelson Doppler Imager's (SoHO/MDI) Space Weather MDI Active Region Patches (SMARP). We employ a suite of machine learning strategies, including Support Vector Machines (SVM) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 +- 0.1, which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive-negative imbalance) reveals a slight increase (+ 0.04 +- 0.05) in the accuracy of a 14-hour SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events.

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arXiv2024

Helioseismic Properties of Dynamo Waves in the Variation of Solar Differential Rotation

Solar differential rotation exhibits a prominent feature: its cyclic variations over the solar cycle, referred to as zonal flows or torsional oscillations, are observed throughout the convection zone. Given the challenge of measuring magnetic fields in subsurface layers, understanding deep torsional oscillations becomes pivotal in deciphering the underlying solar dynamo mechanism. In this study, we address the critical question of identifying specific signatures within helioseismic frequency-splitting data associated with the torsional oscillations. To achieve this, a comprehensive forward modeling approach is employed to simulate the helioseismic data for a dynamo model that, to some extent, reproduces solar-cycle variations of magnetic fields and flows. We provide a comprehensive derivation of the forward modeling process utilizing generalized spherical harmonics, as it involves intricate algebraic computations. All estimated frequency-splitting coefficients from the model display an 11-year periodicity. Using the simulated splitting coefficients and realistic noise, we show that it is possible to identify the dynamo wave signal present in the solar zonal flow from the tachocline to the solar surface. By analyzing observed data, we find similar dynamo wave patterns in the observational data from MDI, HMI, and GONG. This validates the earlier detection of dynamo waves and holds potential implications for the solar dynamo theory models.

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arXiv2024

Predicting the Emergence of Solar Active Regions Using Machine Learning

To create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking of 30.66 x 30.66-degree patches in the vicinity of active regions allowed us to trace the evolution of active regions starting from the pre-emergence state. We have developed a machine learning model to capture the acoustic power flux density variations associated with upcoming magnetic flux emergence. The trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead whether, in a given area of the solar surface, continuum intensity values will decrease. The performed study allows us to investigate the potential of the machine learning approach to predict the emergence of active regions using acoustic power maps as input.

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