arXiv subjects

Vasyl Yurchyshyn

Publications and source records attributed to Vasyl Yurchyshyn.

At least 19 recordsLinked to original sources

arXiv2026

Deep Learning-Enabled Prediction of Geoeffective CMEs Using SOHO and SDO Observations

Understanding and forecasting the geoeffectiveness of a coronal mass ejection (CME) is crucial for protecting infrastructure in the near-Earth space environment and on Earth. In this study, we present a novel fusion model to forecast the geoeffectiveness of CME events. Our model combines convolutional neural networks for feature learning and a prediction network for feature fusion and event classification. The model is trained by observations from instruments including the Large Angle Spectroscopic Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) and the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). The trained model is then used to predict whether an Earth-reaching CME will cause a geomagnetic storm and/or the probability that the CME will cause such a storm. Experimental results based on a five-fold cross validation scheme demonstrate the good performance of our fusion model, achieving a mean true skill statistic (TSS) score of 0.703 when the model is used as a deterministic prediction tool, and a mean Brier score of 0.095 when the model is used as a probabilistic forecasting tool, where a TSS score of 1 or a Brier score of 0 indicates perfect performance. This work contributes to forecasting the causal relationship between Earth-directed CMEs and geomagnetic storms in solar-terrestrial interactions.

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arXiv2026

Predicting Associations between Solar Flares and Coronal Mass Ejections Using SDO/HMI Magnetograms and a Hybrid Neural Network

Solar eruptions, including flares and coronal mass ejections (CMEs), have a significant impact on Earth. Some flares are associated with CMEs, and some flares are not. The association between flares and CMEs is not always obvious. In this study, we propose a new deep learning method, specifically a hybrid neural network (HNN) that combines a vision transformer with long short-term memory, to predict associations between flares and CMEs. HNN finds spatio-temporal patterns in the time series of line-of-sight magnetograms of solar active regions (ARs) collected by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory and uses the patterns to predict whether a flare projected to occur within the next 24 hours will be eruptive (i.e., CME-associated) or confined (i.e., not CME-associated). Our experimental results demonstrate the good performance of the HNN method. Furthermore, the results show that magnetic flux cancellation in polarity inversion line regions may well play a role in triggering flare-associated CMEs, a finding consistent with literature.

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arXiv2026

Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning

The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensitive and could improve the reaction of thermospheric density to solar stimulation. In this study, we present a new deep learning model, named the Solar Index Network, or SINet for short, to predict daily values of the F10.7 and F30 solar indices. The SINet model is designed to make medium-term predictions of the index values (1-60 days in advance). The observed data used for SINet training were taken from the National Oceanic and Atmospheric Administration (NOAA) as well as Toyokawa and Nobeyama facilities. Our experimental results show that SINet performs better than five closely related statistical and deep learning methods for the prediction of F10.7. Furthermore, to our knowledge, this is the first time deep learning has been used to predict the F30 solar index.

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arXiv2025

Solar Chromospheric Network as a Source for Solar Wind Switchbacks

Recent studies suggest that the magnetic switchbacks (SBs) detected by the Parker Solar Probe (PSP) carry information on the scales of solar supergranulation (large scale) and granulation (medium scale). We test this claim using high-resolution H-alpha images obtained with the visible spectro-polarimeters (VIS) of the Goode Solar Telescope (GST) in Big Bear Solar Observatory (BBSO). As possible solar sources, we count all the spicule-like features standing along the chromospheric networks near the coronal hole boundary visible in the H-alpha blue-wing but absent in the red-wing images and measure the geometric parameters of dense sections of individual flux tubes. Intervals between adjacent spicules located along the chromospheric networks are found in the range of 0.4-1.5 Mm (0.03 deg - 0.12 deg) tending to be smaller than the medium scale of SBs. Inter-distances between all pairs of the flux tubes are also counted and they appear in a single peak distribution around 0.7 Mm (0.06 deg) unlike the waiting time distribution of SBs in a scale-free single power-law form. Length-to-diameter ratio of the dense section of flux tubes is as high as 6-40, similar to the aspect ratio of SBs. Number of spicules along a network can be as high as 40-100, consistent with numerous SBs within a patch. With these numbers, it is agued that the medium scale of SBs can be understood as an equilibrium distance resulting from random walk within each diverging magnetic field funnels connected to the chromospheric networks.

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arXiv2025

Formation of chromospheric fan-shaped jets through magnetic reconnection

Recurrent chromospheric fan-shaped jets highlight the highly dynamic nature of the solar atmosphere. They have been named as ''light walls'' or ''peacock jets'' in high-resolution observations. In this study, we examined the underlying mechanisms responsible for the generation of recurrent chromospheric fan-shaped jets utilizing data from the Goode Solar Telescope (GST) at Big Bear Solar Observatory, along with data from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamic Observatory (SDO). These jets appear as dark elongated structures in H$α$ wing images, persist for over an hour, and are located in the intergranular lanes between a pair of same-polarity sunspots. Our analysis reveals that magnetic flux cancellation at the jet base plays a crucial role in their formation. HMI line-of-sight magnetograms show a gradual decrease in opposite-polarity fluxes spanning the sequence of jets in H$α$ - 0.8 angstrom images, suggesting that recurrent magnetic reconnection, likely driven by recurrent miniature flux-rope eruptions that are built up and triggered by flux cancellation, powers these jets. Additionally, magnetic field extrapolations reveal a 3D magnetic null-point topology at the jet formation site $\sim$1.25 Mm height. Furthermore, we observed strong brightening in AIA 304 angstrom channel above the neutral line. Based on our observations and extrapolation results, we propose that these recurrent chromospheric fan-shaped jets align with the minifilament eruption model previously proposed for coronal jets. Though our study focuses on fan-shaped jets in between same-polarity sunspots, similar mechanism might be responsible for light bridge-associated fan-shaped jets.

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arXiv2025

Unveiling the Dynamics and Genesis of Small-scale Fine Structure Loops in the Lower Solar Atmosphere

Recent high-resolution solar observations have unveiled the presence of small-scale loop-like structures in the lower solar atmosphere, often referred to as unresolved fine structures, low-lying loops, and miniature hot loops. These structures undergo rapid changes within minutes, and their formation mechanism has remained elusive. In this study, we conducted a comprehensive analysis of two small loops utilizing data from the Interface Region Imaging Spectrograph (IRIS), the Goode Solar Telescope (GST) at Big Bear Solar Observatory, and the Atmospheric Imaging Assembly (AIA) and the Helioseismic Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO), aiming to elucidate the underlying process behind their formation. The GST observations revealed that these loops, with lengths of $\sim$3.5 Mm and heights of $\sim$1 Mm, manifest as bright emission structures in H$α$ wing images, particularly prominent in the red wing. IRIS observations showcased these loops in 1330 angstrom slit-jaw images, with TR and chromospheric line spectra exhibiting significant enhancement and broadening above the loops, indicative of plasmoid-mediated reconnection during their formation. Additionally, we observed upward-erupting jets above these loops across various passbands. Furthermore, differential emission measurement analysis reveals an enhanced emission measure at the location of these loops, suggesting the presence of plasma exceeding 1 MK. Based on our observations, we propose that these loops and associated jets align with the minifilament eruption model. Our findings suggest a unified mechanism governing the formation of small-scale loops and jets akin to larger-scale X-ray jets.

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arXiv2025

Prediction of Halo Coronal Mass Ejections Using SDO/HMI Vector Magnetic Data Products and a Transformer Model

We present a transformer model, named DeepHalo, to predict the occurrence of halo coronal mass ejections (CMEs). Our model takes as input an active region (AR) and a profile, where the profile contains a time series of data samples in the AR that are collected 24 hours before the beginning of a day, and predicts whether the AR would produce a halo CME during that day. Each data sample contains physical parameters, or features, derived from photospheric vector magnetic field data taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO). We survey and match CME events in the Space Weather Database Of Notification, Knowledge, Information (DONKI) and Large Angle and Spectrometric Coronagraph (LASCO) CME Catalog, and compile a list of CMEs including halo CMEs and non-halo CMEs associated with ARs in the period between November 2010 and August 2023. We use the information gathered above to build the labels (positive versus negative) of the data samples and profiles at hand, where the labels are needed for machine learning. Experimental results show that DeepHalo with a true skill statistics (TSS) score of 0.907 outperforms a closely related long short-term memory network with a TSS score of 0.821. To our knowledge, this is the first time that the transformer model has been used for halo CME prediction.

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arXiv2025

Improving the Temporal Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using a Deep Generative Model

We present a novel deep generative model, named GenMDI, to improve the temporal resolution of line-of-sight (LOS) magnetograms of solar active regions (ARs) collected by the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). Unlike previous studies that focus primarily on spatial super-resolution of MDI magnetograms, our approach can perform temporal super-resolution, which generates and inserts synthetic data between observed MDI magnetograms, thus providing finer temporal structure and enhanced details in the LOS data. The GenMDI model employs a conditional diffusion process, which synthesizes images by considering both preceding and subsequent magnetograms, ensuring that the generated images are not only of high-quality, but also temporally coherent with the surrounding data. Experimental results show that the GenMDI model performs better than the traditional linear interpolation method, especially in ARs with dynamic evolution in magnetic fields.

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arXiv2025

Prediction of Geoeffective CMEs Using SOHO Images and Deep Learning

The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness is crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth's magnetosphere during which the minimum Dst index value is less than -50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew's correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.

cs.LG↗

arXiv2024

Transition Region Brightenings in a Moss Region and their Relation with Lower Atmospheric Dynamics

Small-scale Brightenings (SBs) are commonly observed in the transition region that separates the solar chromosphere from the corona. These brightenings, omnipresent in active region patches known as "moss" regions, could potentially contribute to the heating of active region plasma. In this study, we investigate the properties of SB events in a moss region and their associated chromospheric dynamics, which could provide insights into the underlying generation mechanisms of the SBs. We analyzed the data sets obtained by coordinated observations using the Interface Region Imaging Spectrograph and the Goode Solar Telescope at Big Bear Solar Observatory. We studied 131 SB events in our region of interest and found that 100 showed spatial and temporal matches with the dynamics observed in the chromospheric H$α$ images. Among these SBs, 98 of them were associated with spicules that are observed in H$α$ images. Furthermore, detailed analysis revealed that one intense SB event corresponded to an Ellerman Bomb (EB), while another SB event consisted of several recurring brightenings caused by a stream of falling plasma. We observed that H$α$ far wings often showed flashes of strong brightening caused by the falling plasma, creating an H$α$ spectral profile similar to an EB. However, 31 of the 131 investigated SB events showed no noticeable spatial and temporal matches with any apparent features in H$α$ images. Our analysis indicated that the predominant TR SB events in moss regions are associated with chromospheric phenomena primarily caused by spicules. Most of these spicules display properties akin to dynamic fibrils.

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arXiv2024

Direct Imaging of a Prolonged Plasma/Current Sheet and Quasiperiodic Magnetic Reconnection on the Sun

Magnetic reconnection is widely believed to be the fundamental process in the solar atmosphere that underlies magnetic energy release and particle acceleration. This process is responsible for the onset of solar flares, coronal mass ejections, and other explosive events (e.g., jets). Here, we report direct imaging of a prolonged plasma/current sheet along with quasiperiodic magnetic reconnection in the solar corona using ultra-high-resolution observations from the 1.6-meter Goode Solar Telescope (GST) at BBSO and Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA). The current sheet appeared near a null point in the fan-spine topology and persisted over an extended period (~20 hours). The length and apparent width of the current sheet were about 6 arcsec and 2 arcsec respectively, and the plasma temperature was ~10-20 MK. We observed quasiperiodic plasma inflows and outflows (bidirectional jets with plasmoids) at the reconnection site/current sheet. Furthermore, quasiperiodic reconnection at the long-lasting current sheet produced recurrent eruptions (small flares and jets) and contributed significantly to the recurrent impulsive heating of the active region. Direct imaging of a plasma/current sheet and recurrent null-point reconnection for such an extended period has not been reported previously. These unprecedented observations provide compelling evidence that supports the universal model for solar eruptions (i.e., the breakout model) and have implications for impulsive heating of active regions by recurrent reconnection near null points. The prolonged and sustained reconnection for about 20 hours at the breakout current sheet provides new insights into the dynamics and energy release processes in the solar corona.

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arXiv2024

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1-minute and 5-minute resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data. When predicting the SYM-H indices (2 hours in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

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arXiv2023

Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models

Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops (CDAW) Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). We use LASCO C2 data in the period between January 1996 and December 2020 to train, validate and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep learning models, including ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.

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arXiv2021

A study of sunspot 3 minute oscillations using ALMA and GST

Waves and oscillations are important solar phenomena, not only because they can propagate and dissipate energy in the chromosphere, but also because they carry information about the structure of the atmosphere in which they propagate. The nature of the three-minute oscillations observed in the umbral region of sunspots is considered to be an effect of propagation of magnetohydrodynamic (MHD) waves upward from below the photosphere. We present a study of sunspot oscillations and wave propagation in NOAA AR 12470 using an approximately one-hour long data set acquired on 2015 December 17 by the Atacama Large Millimeter/submillimeter Array (ALMA), the Goode Solar Telescope (GST) operating at the Big Bear Solar Observatory (BBSO), the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), and the Interface Region Imaging Spectrograph (IRIS). The ALMA data are unique in providing a time-series of direct temperature measurements in the sunspot chromosphere. The two-second cadence of ALMA images allows us to well resolve the three-minute periods typical of sunspot oscillations in the chromosphere. Fourier analysis is applied to ALMA Band 3 ($\sim$100 GHz, $\sim$3 mm) and GST H$α$ data sets to obtain power spectra as well as oscillation phase information. We analysed properties of the wave propagation by combining multiple wavelengths that probe physical parameters of solar atmosphere at different heights. We find that the ALMA temperature fluctuations are consistent with that expected for a propagating acoustic wave, with a slight asymmetry indicating non-linear steepening.

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arXiv2020

Detection of Opposite Magnetic Polarity in a Light Bridge: Its Emergence and Cancellation in association with LB Fan-shaped Jets

Light bridges (LBs) are relatively bright structures that divide sunspot umbrae into two or more parts. Chromospheric LBs are known to be associated with various activities including fan-shaped jet-like ejections and brightenings. Although magnetic reconnection is frequently suggested to be responsible for such activities, not many studies present firm evidence to support the scenario. We carry out magnetic field measurements and imaging spectroscopy of a LB where fan-shaped jet-like ejections occur with co-spatial brightenings at their footpoints. We study LB fine structure and magnetic field changes using TiO images, Near-InfraRed Imaging Spectropolarimeter, and Halpha data taken by the 1.6~m Goode Solar Telescope. We detect magnetic flux emergence in the LB that is of opposite polarity to that of the sunspot. The new magnetic flux cancels with the pre-existing flux at a rate of 5.6x10^18 Mx/hr. Both the recurrent jet-like ejections and their base brightenings are initiated at the vicinity of the magnetic flux cancellation, and show apparent horizontal extension along the LB at a projected speed of up to 18.4km/s to form a fan-shaped appearance. Based on these observations, we suggest that the fan-shaped ejections may have resulted from slipping reconnection between the new flux emerging in the LB and the ambient sunspot field.

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arXiv2020

Accelerating and Supersonic Density Fluctuations in Coronal Hole Plumes: Signature of Nascent Solar Winds

Slow magnetoacoustic waves in a static background provide a seismological tool to probe the solar atmosphere in the analytic frame. By analyzing the spatiotemporal variation of the electron number density of plume structure in coronal holes above the limb for a given temperature, we find that the density perturbations accelerate with supersonic speeds in the distance range from 1.02 to 1.23 solar radii. We interpret them as slow magnetoacoustic waves propagating at about the sound speed with accelerating subsonic flows. The average sonic height of the subsonic flows is calculated to be 1.27 solar radii. The mass flux of the subsonic flows is estimated to be 44.1$\%$ relative to the global solar wind. Hence, the subsonic flow is likely to be the nascent solar wind. In other words, the evolution of the nascent solar wind in plumes at the low corona is quantified for the first time from imaging observations. Based on the interpretation, propagating density perturbations present in plumes could be used as a seismological probe of the gradually accelerating solar wind.

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arXiv2020

Generation of Solar Spicules and Subsequent Atmospheric Heating

Spicules are rapidly evolving fine-scale jets of magnetized plasma in the solar chromosphere. It remains unclear how these prevalent jets originate from the solar surface and what role they play in heating the solar atmosphere. Using the Goode Solar Telescope at the Big Bear Solar Observatory, we observed spicules emerging within minutes of the appearance of opposite-polarity magnetic flux around dominant-polarity magnetic field concentrations. Data from the Solar Dynamics Observatory showed subsequent heating of the adjacent corona. The dynamic interaction of magnetic fields (likely due to magnetic reconnection) in the partially ionized lower solar atmosphere appears to generate these spicules and heat the upper solar atmosphere.

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arXiv2020

Spatial Distribution of Origin of Umbral Waves in a Sunspot Umbra

Umbral flashes (UFs) are emissions in the core of chromospheric lines caused by upward propagating waves steepening into shocks. UFs are followed by an expanding blue shifted umbral wave (UW) and red-shifted plasma returning to the initial state. Here we use 5 s cadence images acquired at $\pm$0.04~nm off the Halpha line center by the Visible Imaging Spectrometer (VIS) installed on the Goode Solar Telescope (GST) to detect the origin of UFs and UWs in a sunspot with a uniform umbra free of LBs and clusters of umbral dots. The data showed that UFs do not randomly originate over the umbra. Instead, they appear to be repeatedly triggered at locations with the lowest umbral intensity and the most powerful oscillations of Halpha-0.04 nm intensity. GST magnetic field measurements using Near Infra-Red Imaging Spectropolarimeter (NIRIS) also showed that the dominant location of prevalent UF origin is co-spatial associated with the strongest fields in the umbra. Interface Region Imaging Spectrograph 149.0 nm images showed that no bright UV loops were anchored in the umbra in general and near the UF patches in particular suggesting that UFs and UWs alone can not be responsible for the origin of warm coronal loops. We thus conclude that the existence of locations with prevalent origin of UFs confirms the idea that they may be driven by a sub-surface source located near the axis of a flux rope, while the presence of several UFs trigger centers may indicate the complex structure of a sunspot umbra.

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