arXiv subjects

Tamas Gombosi

Publications and source records attributed to Tamas Gombosi.

At least 19 recordsLinked to original sources

arXiv2026

A multi-source dataset for solar energetic particle forecasting

Solar energetic particle (SEP) event forecasting often involves integrating heterogeneous observations that differ in cadence, temporal coverage, format, and historical availability, posing challenges for reproducible analysis of data-driven approaches. This paper presents SEP-PRISM Data, a curated multi-source dataset designed for 24-hour-ahead forecasting of operational SEP events, defined as proton flux exceeding 10 pfu in the GOES > 10 MeV channel. SEP-PRISM Data integrates flare records, active-region magnetic field parameters, coronal mass ejection (CME) catalog data, GOES soft X-ray flux, and historical proton flux into a common window-based representation spanning 3 February 1986 to 10 September 2025. To improve temporal coverage and cross-source consistency, SHARP and SMARP magnetic products were aligned into a unified SMHARP archive, and CME records from DONKI and CDAW were aligned into a unified CDAWDONKI event set. Predictor variables were summarized over fixed non-overlapping 24-hour historical windows using minimum, mean, and maximum statistics and paired with targets defined over the subsequent 24-hour window, forming a supervised learning dataset. The resulting SEP-PRISM Data contains 14,464 labeled samples, including 650 positive operational SEP cases, and is intended to support reproducible benchmarking, model development, feature analysis, and future studies of space weather forecasting.

astro-ph.SR↗

arXiv2026

Realtime forecasting of solar energetic particle event and proton flux using multi-source solar observations and multi-task deep learning

Solar energetic particle (SEP) events, defined by proton flux exceeding 10 pfu in the > 10 MeV channel, pose major risks to spacecraft operations, astronaut safety, and high-latitude aviation. Due to the complexity and rarity of SEP events, reliable operational SEP forecasting remains an important challenge in space weather. Here we present a novel 24-hour-ahead realtime forecasting framework, SEPNET-PRISM, based on a multi-task learning structure and a thoroughly constructed list of features from multiple sources spanning multiple solar cycles, that jointly predicts SEP event occurrence and future proton and soft X-ray fluxes. SEPNET-PRISM extends the earlier-introduced SEPNET-based models by integrating a broader range of solar observations, including active-region magnetic parameters from SHARP and SMARP, solar-flare information, coronal mass ejections, soft X-ray flux, and historical > 10 MeV proton flux. As compared with SEPNET, the inclusion of SMARP data expands the temporal coverage of magnetic-field predictors to earlier solar cycles, while flux-based inputs provide additional precursor information. Evaluation on the CLEAR SEP benchmark dataset shows improved classification performance over the earlier SEPNET-O (operational version of SEPNET) on the newly aligned dataset. The best operational model is obtained when magnetic, radiative, and proton-flux predictors are combined, highlighting the value of expanded historical coverage and complementary precursor information for improving realtime SEP forecasting.

astro-ph.SR↗

arXiv2026

Defects and Inconsistencies in Solar Flare Data Sources: Implications for Machine Learning Forecasting

Machine learning models for forecasting solar flares have been trained and evaluated using a variety of data sources, including Space Weather Prediction Center (SWPC) operational and science-quality data. Typically, data from these sources is minimally processed before being used to train and validate a forecasting model. However, predictive performance can be affected if defects and inconsistencies between these data sources are ignored. For a set of commonly used data sources, along with the software that queries and outputs processed data, we identify their defects and inconsistencies, quantify their extent, and show how they can affect predictions from data-driven machine-learning forecasting models. We also outline procedures for fixing these issues or at least mitigating their impacts. Finally, based on thorough comparisons of the effects of data sources on the trained forecasting model's predictive skill scores, we offer recommendations for using different data products in operational forecasting.

astro-ph.SR↗

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.

astro-ph.SR↗

arXiv2026

Solar Energetic Particle Forecasting with Multi-Task Deep Learning: SEPNET

Solar energetic particle (SEP) events pose severe threats to spacecraft, astronaut safety, and aviation operations. Accurate SEP forecasting remains a critical challenge in space weather research due to their complex origins and highly variable propagation. In this work, we built SEPNET, an innovative multi-task neural network that jointly predicts future solar eruptive events, including solar flares and coronal mass ejections (CMEs) and SEPs, incorporating long short-term memory and transformer architectures that capture contextual dependencies. SEPNet is a machine learning framework for SEP prediction that utilizes an extensive set of predictors, including solar flares, CMEs, and space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET is rigorously evaluated on the SEPVAL SEP dataset (Whitman, 2025b), which is used to evaluate the performance of the current SEP prediction models. The performance of SEPNet is compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET, particularly with SHARP parameters, achieves higher detection rates and skill scores while maintaining suitable for real-time space weather alert operations. Although class imbalance in the data leads to relatively high false alarm rates, SEPNET consistently outperforms reference methods and provides timely SEP forecasts, highlighting the capability of deep multi-task learning for next-generation space weather prediction. All data and code are available on GitHub at https://github.com/yuyian/SEP-Prediction.git.

physics.space-ph↗

arXiv2025

Evidence of Time-Dependent Diffusive Shock Acceleration in the 2022 September 5 Solar Energetic Particle Event

On 2022 September 5, a large solar energetic particle (SEP) event was detected by Parker Solar Probe (PSP) and Solar Orbiter (SolO), at heliocentric distances of 0.07 and 0.71 au, respectively. PSP observed an unusual velocity-dispersion signature: particles below $\sim$1 MeV exhibited a normal velocity dispersion, while higher-energy particles displayed an inverse velocity arrival feature, with the most energetic particles arriving later than those at lower energies. The maximum energy increased from about 20-30 MeV upstream to over 60 MeV downstream of the shock. The arrival of SEPs at PSP was significantly delayed relative to the expected onset of the eruption. In contrast, SolO detected a typical large SEP event characterized by a regular velocity dispersion at all energies up to 100 MeV. To understand these features, we simulate particle acceleration and transport from the shock to the observers with our newly developed SEP model - Particle ARizona and MIchigan Solver on Advected Nodes (PARMISAN). Our results reveal that the inverse velocity arrival and delayed particle onset detected by PSP originate from the time-dependent diffusive shock acceleration processes. After shock passage, PSP's magnetic connectivity gradually shifted due to its high velocity near perihelion, detecting high-energy SEPs streaming sunward. Conversely, SolO maintained a stable magnetic connection to the strong shock region where efficient acceleration was achieved. These results underscore the importance of spatial and temporal dependence in SEP acceleration at interplanetary shocks, and provide new insights to understand SEP variations in the inner heliosphere.

astro-ph.SR↗

arXiv2023

Solar Wind with Field Lines and Energetic Particles (SOFIE) Model: Application to Historical Solar Energetic Particle Events

In this paper, we demonstrate the applicability of the data-driven and self-consistent solar energetic particle model, Solar-wind with FIeld-lines and Energetic-particles (SOFIE), to simulate acceleration and transport processes of solar energetic particles. SOFIE model is built upon the Space Weather Modeling Framework (SWMF) developed at the University of Michigan. In SOFIE, the background solar wind plasma in the solar corona and interplanetary space is calculated by the Aflvén Wave Solar-atmosphere Model(-Realtime) (AWSoM-R) driven by the near-real-time hourly updated Global Oscillation Network Group (GONG) solar magnetograms. In the background solar wind, coronal mass ejections (CMEs) are launched by placing an imbalanced magnetic flux rope on top of the parent active region, using the Eruptive Event Generator using Gibson-Low model (EEGGL). The acceleration and transport processes are modeled by the Multiple-Field-Line Advection Model for Particle Acceleration (M-FLAMPA). In this work, nine solar energetic particle events (Solar Heliospheric and INterplanetary Environment (SHINE) challenge/campaign events) are modeled. The three modules in SOFIE are validated and evaluated by comparing with observations, including the steady-state background solar wind properties, the white-light image of the CME, and the flux of solar energetic protons, at energies of > 10 MeV.

astro-ph.SR↗

arXiv2023

Modeling the Solar Wind During Different Phases of the Last Solar Cycle

We describe our first attempt to systematically simulate the solar wind during different phases of the last solar cycle with the Alfvén Wave Solar atmosphere Model (AWSoM) developed at the University of Michigan. Key to this study is the determination of the optimal values of one of the most important input parameters of the model, the Poynting flux parameter, which prescribes the energy flux passing through the chromospheric boundary of the model in the form of Alfvén wave turbulence. It is found that the optimal value of the Poynting flux parameter is correlated with the area of the open magnetic field regions with the Spearman's correlation coefficient of 0.96 and anti-correlated with the average unsigned radial component of the magnetic field with the Spearman's correlation coefficient of -0.91. Moreover, the Poynting flux in the open field regions is approximately constant in the last solar cycle, which needs to be validated with observations and can shed light on how Alfvén wave turbulence accelerates the solar wind during different phases of the solar cycle. Our results can also be used to set the Poynting flux parameter for real-time solar wind simulations with AWSoM.

astro-ph.SR↗

arXiv2022

Incorporating Polar Field Data for Improved Solar Flare Prediction

In this paper, we consider incorporating data associated with the sun's north and south polar field strengths to improve solar flare prediction performance using machine learning models. When used to supplement local data from active regions on the photospheric magnetic field of the sun, the polar field data provides global information to the predictor. While such global features have been previously proposed for predicting the next solar cycle's intensity, in this paper we propose using them to help classify individual solar flares. We conduct experiments using HMI data employing four different machine learning algorithms that can exploit polar field information. Additionally, we propose a novel probabilistic mixture of experts model that can simply and effectively incorporate polar field data and provide on-par prediction performance with state-of-the-art solar flare prediction algorithms such as the Recurrent Neural Network (RNN). Our experimental results indicate the usefulness of the polar field data for solar flare prediction, which can improve Heidke Skill Score (HSS2) by as much as 10.1%.

astro-ph.SR↗

arXiv2022

High Resolution Finite Volume Method for Kinetic Equations with Poisson Brackets

Simulation of plasmas in electromagnetic fields requires numerical solution of a kinetic equation that describes the time evolution of the particle distribution function. In this paper we propose a finite volume scheme based on integral relation for Poisson brackets to solve the Liouville equation, the most fundamental kinetic equation. The proposed scheme conserves the number of particles, maintains the total-variation-diminishing (TVD) property, and provides high-quality numerical results. Other types of kinetic equations may be also formulated in terms of Poisson brackets and solved with the proposed method including the transport equations describing the acceleration and propagation of Solar Energetic Particles (SEPs), which is of practical importance, since the high energy SEPs produce radiation hazards. The proposed scheme is demonstrated to be accurate and efficient, which makes it applicable to global simulation systems analyzing space weather.

physics.comp-ph↗

arXiv2022

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycle 23 and 24, we train and evaluate two deep learning algorithms -- CNN and LSTM -- and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher True Skill Scores (TSS) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves significantly higher TSS than the "select-best" strategy with a confidence level of at least 0.95. (3) A visual attribution method called Integrated Gradients is able to attribute the CNN's predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNN as a flare prediction method using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

astro-ph.SR↗

arXiv2020

Kinetic Modeling of Magnetospheres

This paper presents the state of the art of kinetic modeling techniques for simulating plasma kinetic dynamics in magnetospheres. We describe the critical numerical techniques for enabling large-scale kinetic simulations of magnetospheres: parameter scaling, implicit Particle-in-Cell schemes, and fluid-kinetic coupling. We show an application of these techniques to study particle acceleration and heating in asymmetric magnetic reconnection in the Ganymede magnetosphere.

physics.space-ph↗

arXiv2020

Solar Flare Intensity Prediction with Machine Learning Models

We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP). The model makes use of (1) the Space-weather HMI Active Region Patch (SHARP) parameters as predictors and (2) the exact flare intensities instead of class labels recorded in the Geostationary Operational Environmental Satellites (GOES) data set, which serves as the source of the response variables. Compared to solar flare classification, the model offers us more detailed information about the exact maximum flux level, i.e. intensity, for each occurrence of a flare. We also consider classification models built on top of the regression model and obtain better results in solar flare classifications. Our results suggest that the most efficient time period for predicting the solar activity is within 24 hours before the prediction time using the SHARP parameters and the LSTM model.

astro-ph.SR↗

arXiv2019

The Surface Distributions of the Production of the Major Volatile Species, H2O, CO2, CO and O2, from the Nucleus of Comet 67P/Churyumov-Gerasimenko throughout the Rosetta Mission as Measured by the ROSINA Double Focusing Mass Spectrometer

The Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) suite of instruments operated throughout the over two years of the Rosetta mission operations in the vicinity of comet 67P/Churyumov-Gerasimenko. It measured gas densities and composition throughout the comet's atmosphere, or coma. Here we present two-years' worth of measurements of the relative densities of the four major volatile species in the coma of the comet, H2O. CO2, CO and O2, by one of the ROSINA sub-systems called the Double Focusing Mass Spectrometer (DFMS). The absolute total gas densities were provided by the Comet Pressure Sensor (COPS), another ROSINA sub-system. DFMS is a very high mass resolution and high sensitivity mass spectrometer able to resolve at a tiny fraction of an atomic mass unit. We have analyzed the combined DFMS and COPS measurements using an inversion scheme based on spherical harmonics that solves for the distribution of potential surface activity of each species as the comet rotates, changing solar illumination, over short intervals and as the comet changes distance from the sun and orientation of its spin axis over long time intervals. We also use the surface boundary conditions derived from the inversion scheme to simulate the whole coma with our fully kinetic Direct Simulation Monte Carlo model and calculate the production rates of the four major species throughout the mission. We compare the derived production rates with revised remote sensing observations by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) as well as with published observations from the Microwave Instrument for the Rosetta Orbiter (MIRO). Finally we use the variation of the surface production of the major species to calculate the total mass loss over the mission and, for different estimates of the dust/gas ratio, calculate the variation of surface loss over the nucleus.

astro-ph.EP↗

arXiv2019

Studying dawn-dusk asymmetries of Mercury's magnetotail using MHD-EPIC simulations

MESSENGER has observed a lot of dawn-dusk asymmetries in Mercury's magnetotail, such as the asymmetries of the cross-tail current sheet thickness and the occurrence of flux ropes, dipolarization events and energetic electron injections. In order to obtain a global pictures of Mercury's magnetotail dynamics and the relationship between these asymmetries, we perform global simulations with the magnetohydrodynamics with embedded particle-in-cell (MHD-EPIC) model, where Mercury's magnetotail region is covered by a PIC code. Our simulations show that the dawnside current sheet is thicker, the plasma density is larger, and the electron pressure is higher than the duskside. Under a strong IMF driver, the simulated reconnection sites prefer the dawnside. We also found the dipolarization events and the planetward electron jets are moving dawnward while they are moving towards the planet, so that almost all dipolarization events and high-speed plasma flows concentrate in the dawn sector. The simulation results are consistent with MESSENGER observations.

physics.space-ph↗

arXiv2019

A Six-moment Multi-fluid Plasma Model

We present a six-moment multi-fluid model, which solves the governing equations for both ions and electrons, with pressure anisotropy along and perpendicular to the magnetic field direction, as well as the complete set of Maxwell equations. This set of equations includes the Hall effect, different temperatures for different species and pressure anisotropy. It is more comprehensive than the five-moment equations with isotropic pressures and significantly less expensive than the ten-moment equations with a full pressure tensors. Similarly to the five- and ten-moment equations, the wave speeds are naturally limited by the speed of light, which eliminates the issue of unlimited whistler wave speeds present in Hall magnetohydrodynamics (MHD). It is also possible to simulate multiple negatively charged fluids, which cannot be done in MHD models. The six-moment model is a reasonable description of the plasma outside magnetic reconnection regions and therefore well-suited to be coupled with an embedded particle-in-cell model that covers the reconnection region. Our numerical implementation uses a point-implicit scheme for the stiff source terms, and we use a second-order accurate Rusanov-type scheme with carefully selected wave speeds. For the plasma variables and the magnetic field the maximum wave speed is based on the fast magnetosonic speed of MHD with anisotropic pressures that we derive. For the electric field related variables the speed of light is used. The divergence of the magnetic field and Gauss's law are controlled with a hyperbolic-parabolic scheme. We present a number of numerical tests to demonstrate that this numerical model is robust without being excessively diffusive.

physics.comp-ph↗

arXiv2017

The dehydration of water worlds via atmospheric losses

We present a three-species multi-fluid MHD model (H$^+$, H$_2$O$^+$ and e$^-$), endowed with the requisite atmospheric chemistry, that is capable of accurately quantifying the magnitude of water ion losses from exoplanets. We apply this model to a water world with Earth-like parameters orbiting a Sun-like star for three cases: (i) current normal solar wind conditions, (ii) ancient normal solar wind conditions, and (iii) one extreme "Carrington-type" space weather event. We demonstrate that the ion escape rate for (ii), with a value of 6.0$\times$10$^{26}$ s$^{-1}$, is about an order of magnitude higher than the corresponding value of 6.7$\times$10$^{25}$ s$^{-1}$ for (i). Studies of ion losses induced by space weather events, where the ion escape rates can reach $\sim$ 10$^{28}$ s$^{-1}$, are crucial for understanding how an active, early solar-type star (e.g., with frequent coronal mass ejections) could have accelerated the depletion of the exoplanet's atmosphere. We briefly explore the ramifications arising from the loss of water ions, especially for planets orbiting M-dwarfs where such effects are likely to be significant.

astro-ph.EP↗

arXiv2008

Three-Dimensional MHD Simulation of the 2003 October 28 Coronal Mass Ejection: Comparison with LASCO Coronagraph Observations

We numerically model the coronal mass ejection (CME) event of October 28, 2003 that erupted from active region 10486 and propagated to Earth in less than 20 hours causing severe geomagnetic storms. The magnetohydrodynamic (MHD) model is formulated by first arriving at a steady state corona and solar wind employing synoptic magnetograms. We initiate two CMEs from the same active region, one approximately a day earlier that preconditions the solar wind for the much faster CME on the 28th. This second CME travels through the corona at a rate of over 2500 km s$^{-1}$ driving a strong forward shock. We clearly identify this shock in an image produced by the Large Angle Spectrometric Coronagraph (LASCO) C3, and reproduce the shock and its appearance in synthetic white light images from the simulation. We find excellent agreement with both the general morphology and the quantitative brightness of the model CME with LASCO observations. These results demonstrate that the CME shape is largely determined by its interaction with the ambient solar wind and may not be sensitive to the initiation process. We then show how the CME would appear as observed by wide-angle coronagraphs onboard the Solar Terrestrial Relations Observatory (STEREO) spacecraft. We find complex time evolution of the white-light images as a result of the way in which the density structures pass through the Thomson sphere. The simulation is performed with the Space Weather Modeling Framework (SWMF).

astro-ph↗