arXiv subjects

Alexander Kosovichev

Publications and source records attributed to Alexander Kosovichev.

At least 19 recordsLinked to original sources

arXiv2026

Origin of Coronal Extreme Ultraviolet Shockwaves without a Coronal Mass Ejection Event

A leading theory of sunquake generation involves flare-accelerated particles depositing energy into the photosphere. Simulations of sunquake excitation suggest co-excitation with wavefronts propagating in the corona, similar to large-scale coronal propagating fronts (LCPFs), and also generate Moreton-Ramsey waves in the chromosphere. To investigate observational evidence for the particle-driven mechanism in LCPFs, we compare populations of events associated with and without coronal mass ejections (CMEs). CMEs are known to generate coronal shock waves also observed in EUV emission. We employ visual inspection of flare events that generate LCPFs using Atmospheric Imaging Assembly (AIA) and Large Angle and Spectrometric Coronagraph (LASCO) coronagraph images to find that the large-scale coronal waves associated with CMEs propagate noticeably faster. Then we examine standalone flare events (those that generate coronal waves without CMEs), using soft X-ray (SXR) data from the GOES satellite and focusing on characteristics related to magnetic energy release rate. This reveals that such standalone or confined flares differ from sunquake flares: they are less impulsive and energetic than sunquake flares. However, they are more impulsive but less energetic than LCPF-associated flares with a CME. In particular, coronal waves accompanied by CMEs exhibit significantly higher volume emission measures, suggesting a different generation mechanism.

astro-ph.SR↗

arXiv2026

Sharper Than Ever: Do Modern Observations Pin Down the Solar Radius to Converge on New Standards?

Solar radius measurements and their variations -- if any -- are a difficult problem that has vexed researchers for decades. In this paper, we have attempted to clarify the various ways of expressing the definition ''solar diameter'', from a physical point of view. The concept of diameter is taken here in its broadest sense, leaving aside the issue concerning the oblateness caused by surface and internal angular velocity variations, as deviations from sphericity are negligible in our context. Astrometric time-series observations are still needed, and we advocate strengthening long-term metrological measures to achieve greater consensus on the subject. To date, modern observations of the solar diameter provide a frame of reference, and we give a new glossary. By comparing the best values obtained to date, it is shown that the ''seismic radius'' obtained from the Solar and Heliospheric Observatory (SOHO) and the Solar Dynamics Observatory (SDO) provides the best determination, a finding supported by observations made at the Calern (F) and Pic du Midi (F) observatories. The latest results on the leptocline show that it is more important than ever to consider at which layers of the Sun radius measurements are carried out. On this basis, we hope to converge on new standards.

astro-ph.SR↗

arXiv2026

Prediction of Magnetic Flux Evolution During Solar Active Region Emergence using Long Short-Term Memory Networks

Solar active regions (ARs) are the primary drivers of space weather events, making their early prediction crucial for operational forecasting systems. We develop machine learning models capable of predicting the evolution of magnetic flux during AR emergence using 1D time series of the continuum intensity and solar oscillation power maps for 53 active regions and their surrounding quiet-Sun areas. Each observable is sampled over a fixed 30.66°x30.66° field of view. These observations capture the temporal evolution of each active region and serve as inputs for training and validation of our MagFluxLSTM and MagFluxEnc-Dec models. The MagFluxLSTM architecture implements a single-stage standard Long-Short Term Memory (LSTM) network. MagFluxEnc-Dec represents an LSTM encoder-decoder with teacher forcing. To test and evaluate the models' performance, we use the continuum intensity and oscillation power maps (calculated for several frequency bands from Doppler velocity) as input to predict the magnetic flux. Among the top 100 hyperparameter configurations ranked by validation derivative RMSE, 98% correspond to MagFluxLSTM, compared to only 2% for MagFluxEnc-Dec. Thus, although the MagFluxEnc-Dec architecture has higher model complexity, it leads to poorer generalization to ARs outside the training set and less stable training than the simpler MagFluxLSTM, which can predict magnetic flux emergence 3-10 hours in advance within a 12-hour prediction window in both experimental and operational-type settings for the 5 testing active regions.

astro-ph.SR↗

arXiv2025

An Acoustic Model for Sunquakes Unifying the Solar Interior and Atmosphere

One of the leading hypotheses for sunquake generation suggests that flare-accelerated particles originating from the reconnection site in the corona travel down to the chromosphere and photosphere, where they deposit energy through collisions and subsequently drive acoustic oscillations. To properly encompass this top-down excitation mechanism, we extend the domain of a semi-spectral 3D acoustic model of the global Sun up to several 10's of Mm above the photosphere, where the transition region and lower corona are resolved. We then use the radially-dependent heating rates derived from the flare radiative hydrodynamic (RADYN) simulations -- extrapolated to a 3D profile -- to realistically excite sunquakes. In addition to the usual sunquake wavefronts, we also observe waves that propagate through the chromosphere and corona in a similar fashion to Moreton-Ramsey waves and large-scale coronal propagating fronts (LCPFs). We examine the dynamics of these waves and discuss how they may be used to constrain models of sunquake excitation.

astro-ph.SR↗

arXiv2025

Operational and Exploration Requirements and Research Capabilities for SEP Environment Monitoring and Forecasting

Mitigating risks posed by solar energetic particles (SEPs) to operations and exploration in space and Earth's atmosphere motivates the development of advanced, synergistic approaches for monitoring, modeling, and analyzing space weather conditions. The consequences of SEPs and their interactions with the near-Earth space environment are numerous, including elevated radiation levels at aviation altitudes during major events, satellite damage, and health risks to astronauts, resulting in economic impacts and potential hazards for space exploration. This contribution will present a high-level overview of the operational requirements and research capabilities for SEP event environment monitoring and forecasting that were highlighted during a workshop at Georgia State University, held on October 16-19, 2024. Specifically, it summarizes the presented activities concerning the following: (1) Identifying needs for SEP event forecasting and nowcasting, including practical forecast timeframes; (2) Reviewing availability and coverage of the current observational data and identifying tangible data resources for research, operations and the R2O2R loop; (3) Mapping existing forecast capabilities and identifying meaningful modeling advances for research and operations.

astro-ph.SR↗

arXiv2025

Structure and Dynamics of the Sun's Interior Revealed by Helioseismic and Magnetic Imager

High-resolution helioseismology observations with the Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) provide a unique three-dimensional view of the solar interior structure and dynamics, revealing a tremendous complexity of the physical processes inside the Sun. We present an overview of the results of the HMI helioseismology program and discuss their implications for modern theoretical models and simulations of the solar interior.

astro-ph.SR↗

arXiv2025

Improving Our Knowledge of the Solar Near-Surface Shear Layer: The Special Case of the Leptocline

The discovery of the solar activity cycle was linked from the outset to the observation of the temporal variability of sunspots, which we know to be the result of complex processes associated with the dynamics of inner layers. Numerous recent studies have highlighted changes in the Sun's Near-Surface Shear Layer (NSSL), pointing to the role of the leptocline, a shallow and sharp rotational shear layer in the top around 8 Mm. The leptocline, mainly characterized by a strong radial rotational gradient at middle latitudes and self-organized meridional flows, is the cradle of numerous phenomena: opacity, superadiabaticity, and turbulent pressure changes; the hydrogen and helium ionization processes; a sharp decrease in the sound speed; and, probably, variations of the seismic radius associated with a nonmonotonic expansion of subsurface layers with depth. In addition, the leptocline may play a key role in forming the magnetic butterfly diagram. Such results are a starting point for further systematic investigations of the structure and dynamics of this layer, which will lead to a better understanding of solar activity.

astro-ph.SR↗

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.

astro-ph.SR↗

arXiv2023

Can Proton Beam Heating Flare Models Explain Sunquakes?

SDO/HMI observations reveal a class of solar flares with substantial energy and momentum impacts in the photosphere, concurrent with white-light emission and helioseismic responses, known as sunquakes. Previous radiative hydrodynamic modeling has demonstrated the challenges of explaining sunquakes in the framework of the standard flare model of `electron beam' heating. One of the possibilities to explain the sunquakes and other signatures of the photospheric impact is to consider additional heating mechanisms involved in solar flares, for example, via flare-accelerated protons. In this work, we analyze a set of single-loop Fokker-Planck and radiative hydrodynamics RADYN+FP simulations where the atmosphere is heated by non-thermal power-law-distributed proton beams which can penetrate deeper than the electron beams into the low atmospheric layers. Using the output of the RADYN models, we calculate synthetic Fe I 6173 A line Stokes profiles and from those the line-of-sight (LOS) observables of the SDO/HMI instrument, as well as the 3D helioseismic response and compare them with the corresponding observational characteristics. These initial results show that the models with proton beam heating can produce the enhancement of the HMI continuum observable and explain qualitatively generation of sunquakes. The continuum observable enhancement is evident in all models but is more prominent in ones with $E_{c}\geq$500 keV. In contrast, the models with $E_{c}\leq$100 keV provide a stronger sunquake-like helioseismic impact according to the 3D acoustic modeling, suggesting that low-energy (deka- and hecto-keV) protons have an important role in the generation of sunquakes.

astro-ph.SR↗

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.

astro-ph.SR↗

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.

astro-ph.SR↗

arXiv2022

Exploring the Connection between Helioseismic Travel Time Anomalies and the Emergence of Large Active Regions during Solar Cycle 24

We investigate deviations in the mean phase travel time of acoustic waves preceding the emergence of 46 large active regions observed by the Helioseismic and Magnetic Imager (HMI). In our investigation, we consider two different procedures for obtaining the mean phase travel time, by minimizing the difference between cross-correlations and a reference, as well as the Gabor wavelet fitting procedure. We cross-correlate the time series of mean phase travel time deviations with the surface magnetic field and determine the peak correlation time lag. We also compute the perturbation index--the area integrated mean phase travel time deviations exceeding quiet sun thresholds--and compare the time of peak perturbation index with the correlation time lag. We find that the lag times derived from the difference minimization procedure precede the flux emergence for 36 of the 46 active regions, and that this lag time has a noticeable correlation with the maximum flux rate. However, only 28 of the active regions have peak perturbation index times in the range of 24 to 48 hours prior to the flux emergence. Additionally, we examine the relationship between properties of the emerged active regions and the strength of helioseismic signals prior to their emergence.

astro-ph.SR↗

arXiv2022

Revisiting the Solar Research Cyberinfrastructure Needs: A White Paper of Findings and Recommendations

Solar and Heliosphere physics are areas of remarkable data-driven discoveries. Recent advances in high-cadence, high-resolution multiwavelength observations, growing amounts of data from realistic modeling, and operational needs for uninterrupted science-quality data coverage generate the demand for a solar metadata standardization and overall healthy data infrastructure. This white paper is prepared as an effort of the working group "Uniform Semantics and Syntax of Solar Observations and Events" created within the "Towards Integration of Heliophysics Data, Modeling, and Analysis Tools" EarthCube Research Coordination Network (@HDMIEC RCN), with primary objectives to discuss current advances and identify future needs for the solar research cyberinfrastructure. The white paper summarizes presentations and discussions held during the special working group session at the EarthCube Annual Meeting on June 19th, 2020, as well as community contribution gathered during a series of preceding workshops and subsequent RCN working group sessions. The authors provide examples of the current standing of the solar research cyberinfrastructure, and describe the problems related to current data handling approaches. The list of the top-level recommendations agreed by the authors of the current white paper is presented at the beginning of the paper.

astro-ph.IM↗

arXiv2022

On the origin of the rhythmic Sun's radius variation

The Sun shrinks during solar maximum and regrows during solar minimum by a few kilometers. Here, we observe, for the first time, that the solar radius variation shows planetary relationship. This suggests that some remote planetary links must exist, even though this is unexpected within known physics. Therefore, we speculate about its cause, which is the driving idea behind this investigation. Namely, it must be some generic as yet invisible streaming matter from the dark Universe. The associated energy change is massive although it extends from months to several years. The impact by the tentative invisible massive matter accumulates with time, triggering slowly the underlying processes. This study shows that the sun size response is as short as a few months. Then, the solar system is the target and the antenna of still unidentified external impact, assuming to be from the dark sector.

physics.gen-ph↗

arXiv2021

Prediction of Solar Proton Events with Machine Learning: Comparison with Operational Forecasts and "All-Clear" Perspectives

Solar Energetic Particle events (SEPs) are among the most dangerous transient phenomena of solar activity. As hazardous radiation, SEPs may affect the health of astronauts in outer space and adversely impact current and future space exploration. In this paper, we consider the problem of daily prediction of Solar Proton Events (SPEs) based on the characteristics of the magnetic fields in solar Active Regions (ARs), preceding soft X-ray and proton fluxes, and statistics of solar radio bursts. The machine learning (ML) algorithm uses an artificial neural network of custom architecture designed for whole-Sun input. The predictions of the ML model are compared with the SWPC NOAA operational forecasts of SPEs. Our preliminary results indicate that 1) for the AR-based predictions, it is necessary to take into account ARs at the western limb and on the far side of the Sun; 2) characteristics of the preceding proton flux represent the most valuable input for prediction; 3) daily median characteristics of ARs and the counts of type II, III, and IV radio bursts may be excluded from the forecast without performance loss; and 4) ML-based forecasts outperform SWPC NOAA forecasts in situations in which missing SPE events is very undesirable. The introduced approach indicates the possibility of developing robust "all-clear" SPE forecasts by employing machine learning methods.

astro-ph.SR↗

arXiv2021

The Observational Uncertainty of Coronal Hole Boundaries in Automated Detection Schemes

Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely-applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community.

astro-ph.SR↗

arXiv2021

Challenges and Advances in Modeling of the Solar Atmosphere: A White Paper of Findings and Recommendations

The next decade will be an exciting period for solar astrophysics, as new ground- and space-based instrumentation will provide unprecedented observations of the solar atmosphere and heliosphere. The synergy between modeling effort and comprehensive analysis of observations is crucial for the understanding of the physical processes behind the observed phenomena. However, the unprecedented wealth of data on one hand, and the complexity of the physical phenomena on the other, require the development of new approaches in both data analysis and numerical modeling. In this white paper, we summarize recent numerical achievements to reproduce structure, dynamics, and observed phenomena from the photosphere to the low corona and outline challenges we expect to face for the interpretation of future observations.

astro-ph.IM↗

arXiv2020

Machine-learning approach to identification of coronal holes in solar disk images and synoptic maps

Identification of solar coronal holes (CHs) provides information both for operational space weather forecasting and long-term investigation of solar activity. Source data for the first problem are typically most recent solar disk observations, while for the second problem it is convenient to consider solar synoptic maps. Motivated by the idea that the concept of CHs should be similar for both cases we investigate universal models that can learn a CHs segmentation in disk images and reproduce the same segmentation in synoptic maps. We demonstrate that Convolutional Neural Networks (CNN) trained on daily disk images provide an accurate CHs segmentation in synoptic maps and their pole-centric projections. Using this approach we construct a catalog of synoptic maps for the period of 2010-20 based on SDO/AIA observations in the 193 Angstrom wavelength. The obtained CHs synoptic maps are compared with magnetic synoptic maps in the time-latitude and time-longitude diagrams. The initial results demonstrate that while in some cases the CHs are associated with magnetic flux transport events there are other mechanisms contributing to the CHs formation and evolution. To stimulate further investigations the catalog of synoptic maps is published in open access.

astro-ph.SR↗