arXiv subjects

Ju Jing

Publications and source records attributed to Ju Jing.

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.

astro-ph.SR↗

arXiv2025

Large Magnetic Flux Rope Formation in an X2.1 Flare observed on September 6, 2011

Solar active region 11283 produced an X2.1 flare associated with a solar eruption on September 6, 2011. Observations revealed a preflare sigmoidal structure and a circular flare ribbon surrounding the typical two ribbon structure, along with remote brightenings located at a considerable distance from the main flare site. To interpret these observations in terms of the three dimensional (3D) coronal magnetic field dynamics, we conducted data constrained magnetohydrodynamic (MHD) simulations. Using a non linear force free field (NLFFF) as the initial condition, we reconstructed a realistic pre flare magnetic environment, capturing a sheared sigmoid above the polarity inversion line (PIL) surmounted by a fan spine structure. Our simulations revealed that reconnection between the sigmoidal field, the adjacent fan dome field lines, and the neighboring large loops facilitated the transfer of magnetic twist and led to the formation of a large magnetic flux rope (MFR). This transfer and propagation of twist are clearly visible throughout the MFR. As reconnection progresses, the entire fan spine structure expands along with the evolving MFR. A notable outcome of the simulation is that the footpoints of the newly formed MFR align closely with the observed circular flare ribbon and the remote brightening region. Our findings suggest that a large MFR formed during the X2.1 flare, providing a coherent explanation for the observed phenomena.

astro-ph.SR↗

arXiv2025

A Data-constrained Magnetohydrodynamic Simulation of Successive X-class Flares in Solar Active Region 13842. II. Dynamics of the Solar Eruption Associated with the X9.0 Solar Flare

Active region NOAA 13842 produced two successive solar flares: an X7.1-class flare on October 1, 2024, and an X9.0-class flare on October 3, 2024. This study continues our previous simulation work that successfully reproduced the X7.1-class solar flare (Matsumoto et al. 2025). In this study, we performed a data-constrained magnetohydrodynamic (MHD) simulation using the nonlinear force-free field (NLFFF) as the initial condition to investigate the X9.0-class solar flare. The NLFFF showed the sheared field lines, resulting in the tether-cutting reconnection, the magnetic flux ropes (MFRs), and eventually led to eruption. The magnetic reconnection during the pre-eruption phase plays a critical role in accelerating the subsequent eruption, which is driven by torus instability and magnetic reconnection. Furthermore, our simulation results are consistent with several observational features associated with the X9.0 flare. This simulation could reproduce diverse phenomena associated with the X9.0 flare, including the tether-cutting reconnection, the flare ribbons and the flare loops, the transverse field enhancement, and the remote brightening away from the flare ribbons. However, the initial trigger, magnetic flux emergence, was inferred from observations rather than explicitly modeled, and future comprehensive simulations should incorporate this mechanism directly.

astro-ph.SR↗

arXiv2025

Acceleration of Solar Eruptions via Enhanced Torus Instability Driven by Small-Scale Flux Emergence

Despite decades of research, the fundamental processes involved in the initiation and acceleration of solar eruptions remain not fully understood, making them long-standing and challenging problems in solar physics. Recent high-resolution observations by the Goode Solar Telescope have revealed small-scale magnetic flux emergence in localized regions of solar active areas prior to eruptions. Although much smaller in size than the entire active region, these emerging fluxes reached strengths of up to 2000 G. To investigate their impact, we performed data-constrained magnetohydrodynamic (MHD) simulations. We find that while the small-scale emerging flux does not significantly alter the pre-eruption evolution, it dramatically accelerates the eruption during the main phase by enhancing the growth of torus instability, which emerges in the nonlinear stage. This enhancement occurs independently of the decay index profile. Our analysis indicates that even subtle differences in the pre-eruption evolution can strongly influence the subsequent dynamics, suggesting that small-scale emerging flux can play a critical role in accelerating solar eruptions.

astro-ph.SR↗

arXiv2025

A Data-constrained Magnetohydrodynamic Simulation of Successive X-class Flares in Solar Active Region 13842 I. Dynamics of the Solar Eruption Associated with the X7.1 Solar Flare

We investigated the initiation and the evolution of an X7.1-class solar flare observed in solar active region NOAA 13842 on October 1, 2024, based on a data-constrained magnetohydrodynamic (MHD) simulation. The nonlinear force-free field (NLFFF) extrapolated from the photospheric magnetic field about 1 hour before the flare was used as the initial condition for the MHD simulations. The NLFFF reproduces highly sheared field lines that undergo tether-cutting reconnection in the MHD simulation, leading to the formation of a highly twisted magnetic flux rope (MFR), which then erupts rapidly driven by both torus instability and magnetic reconnection. This paper focuses on the dynamics of the MFR and its role in eruptions. We find that magnetic reconnection in the pre-eruption phase is crucial in the subsequent eruption driven by the torus instability. Furthermore, our simulation indicates that magnetic reconnection also directly enhances the torus instability. These results suggest that magnetic reconnection is not just a byproduct of the eruption due to reconnecting of post-flare arcade, but also plays a significant role in accelerating the MFR during the eruption.

astro-ph.SR↗

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.

astro-ph.SR↗

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

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.

astro-ph.IM↗

arXiv2023

Extreme Red-wing Enhancements of UV Lines During the 2022 March 30 X1.3 Solar Flare

Here we present the study of a compact emission source during an X1.3 flare on 2022-March-30. Within a $\sim41$~s period (17:34:48 UT to 17:35:29 UT), IRIS observations show spectral lines of Mg II, C II and Si IV with extremely broadened, asymmetric red-wings. This source of interest (SOI) is compact, $\sim$ 1\arcsec.6, and is located in the wake of a passing ribbon. Two methods were applied to measure the Doppler velocities associated with these red wings: spectral moments and multi-Gaussian fits. The spectral moments method considers the averaged shift of the lines, which are 85 km s$^{-1}$, 125 km s$^{-1}$ and 115 km s$^{-1}$ for the Mg II, C II and Si IV lines respectively. The red-most Gaussian fit suggests a Doppler velocity up to $\sim$160 km s$^{-1}$ in all of the three lines. Downward mass motions with such high speeds are very atypical, with most chromospheric downflows in flares on the order 10-100 km s$^{-1}$. Furthermore, EUV emission is strong within flaring loops connecting two flare ribbons located mainly to the east of the central flare region. The EUV loops that connect the SOI and its counterpart source in the opposite field are much less brightened, indicating that the density and/or temperature is comparatively low. These observations suggest a very fast downflowing plasma in transition region and upper chromosphere, that decelerates rapidly since there is no equivalently strong shift of the O I chromospheric lines. This unusual observation presents a challenge that models of the solar atmosphere's response to flares must be able to explain.

astro-ph.SR↗

arXiv2023

Coronal Magnetic Field Extrapolation and Topological Analysis of Fine-Scale Structures during Solar Flare Precursors

Magnetic field plays an important role in various solar eruptions like flares, coronal mass ejections, etc. The formation and evolution of characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origination of these eruptions in the solar source regions. With the development of advanced techniques and instruments, observations with higher resolutions in different wavelengths and fields of view have provided more quantitative information for finer structures. So it is essential to improve our method to study the magnetic field topology in the solar source regions by taking advantage of high-resolution observations. In this study, we employ a nonlinear force-free field (NLFFF) extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results employing the embedded magnetogram for the bottom boundary are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/H$\alpha$ and SDO/AIA observations for fine-scale structures associated with precursor brightenings. Then we perform a topological analysis of the field line connectivity corresponding to fine-scale magnetic field structures based on the extrapolation results. The results indicate that by combining the high-resolution GST magnetogram with a larger HMI magnetogram, the derived magnetic field topology is consistent with a scenario of magnetic reconnection among sheared field lines across the main polarity inversion line during solar flare precursors.

astro-ph.SR↗

arXiv2023

Multi-instrument Comparative Study of Temperature, Number Density, and Emission Measure during the Precursor Phase of a Solar Flare

We present a multi-instrument study of the two precursor brightenings prior to the M6.5 flare (SOL2015-06-22T18:23) in the NOAA Active Region 12371, with a focus on the temperature (T), electron number density (n), and emission measure (EM). The data used in this study were obtained from four instruments with a variety of wavelengths, i.e., the Solar Dynamics Observatory's Atmospheric Imaging Assembly (AIA), in six extreme ultraviolet (EUV) passbands; the Expanded Owens Valley Solar Array (EOVSA) in microwave (MW); the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) in hard X-rays (HXR); and the Geostationary Operational Environmental Satellite (GOES) in soft X-rays (SXR). We compare the temporal variations of T, n, and EM derived from the different data sets. Here are the key results. (1) GOES SXR and AIA EUV have almost identical EM variations (1.5-3x10^48 per cm^3) and very similar T variations, from 8 to 15 million Kelvin (MK). (2) Listed from highest to lowest, EOVSA MW provides the highest temperature variations (15-60 MK), followed by RHESSI HXR (10-24 MK), then GOES SXR and AIA EUV (8-15 MK). (3) The EM variation from the RHESSI HXR measurements is always less than the values from AIA EUV and GOES SXR by at most 20 times. The number density variation from EOVSA MW is greater than the value from AIA EUV by at most 100 times. The results quantitatively describe the differences in the thermal parameters at the precursor phase, as measured by different instruments operating at different wavelength regimes and for different emission mechanisms.

astro-ph.SR↗

arXiv2023

A Comparative Study of Solar Active Region 12371 with Data-constrained and Data-driven MHD Simulations

We performed two data-based magnetohydrodynamic (MHD) simulations for solar active region 12371 which produced an M6.5 flare. The first simulation is a full data-driven simulation where the initial condition is given by a nonlinear force-free field (NLFFF). This NLFFF was extrapolated from photospheric magnetograms approximately 1 hour prior to the flare, and then a time-varying photospheric magnetic field is imposed at the bottom surface. The second simulation is also a data-driven simulation, but it stops driving at the bottom before the time of flare onset and then switches to the data-constrained simulation, where the horizontal component of the magnetic field varies according to an induction equation while the normal component is fixed with time. Both simulations lead to an eruption, with both simulations producing highly twisted field lines before the eruption which were not found in the NLFFF alone. After the eruption, the first simulation based on the time-varying photospheric magneitic field, continues to produce sheared field lines after the flare without reproducing phenomena such as post-flare loops. The second simulation reproduces the phenomena associated with flares well. However in this case the evolution of the bottom magnetic field is inconsistent with the evolution of the observed magnetic field. In this letter, we report potential advantages and disadvantages in data-constrained and data-driven MHD simulations that need to be taken into consideration by future studies.

astro-ph.SR↗

arXiv2022

A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

astro-ph.SR↗

arXiv2022

Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M-and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and Machine Learning approaches to the prediction of the AR's Flare Index (FI) that quantifies the flare productivity of an AR by taking into account the numbers of different class flares within a certain time interval. Specifically, our sample includes 563 ARs appeared on solar disk from May 2010 to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Over-Sampling Technique for Regression with Gaussian Noise, short by SMOGN). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1-day period. Compared with other 4 popular machine learning algorithms, our methods improve the accuracy of FI prediction, especially for large FI. In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.

astro-ph.SR↗

arXiv2021

Multi-Passband Observations of A Solar Flare over the He I 10830 \AA\ line

This study presents a C3.0 flare observed by the BBSO/GST and IRIS, on 2018-May-28 around 17:10 UT. The Near Infrared Imaging Spectropolarimeter (NIRIS) of GST was set to spectral imaging mode to scan five spectral positions at $\pm$ 0.8 \AA, $\pm$ 0.4 \AA and line center of He I 10830. At the flare ribbon's leading edge the line is observed to undergo enhanced absorption, while the rest of the ribbon is observed to be in emission. When in emission, the contrast compared to the pre-flare ranges from about $30~\%$ to nearly $100~\%$ at different spectral positions. Two types of spectra, "convex" shape with higher intensity at line core and "concave" shape with higher emission in the line wings, are found at the trailing and peak flaring areas, respectively. On the ribbon front, negative contrasts, or enhanced absorption, of about $\sim 10\% - 20\%$ appear in all five wavelengths. This observation strongly suggests that the negative flares observed in He I 10830 with mono-filtergram previously were not caused by pure Doppler shifts of this spectral line. Instead, the enhanced absorption appears to be a consequence of flare energy injection, namely non-thermal collisional ionization of helium caused by the precipitation of high energy electrons, as found in our recent numerical modeling results. In addition, though not strictly simultaneous, observations of Mg II from the IRIS spacecraft, show an obvious central reversal pattern at the locations where enhanced absorption of He I 10830 is seen, which is in consistent with previous observations.

astro-ph.SR↗

arXiv2021

Coronal Magnetic Field Measurements along a Partially Erupting Filament in a Solar Flare

Magnetic flux ropes are the centerpiece of solar eruptions. Direct measurements for the magnetic field of flux ropes are crucial for understanding the triggering and energy release processes, yet they remain heretofore elusive. Here we report microwave imaging spectroscopy observations of an M1.4-class solar flare that occurred on 2017 September 6, using data obtained by the Expanded Owens Valley Solar Array. This flare event is associated with a partial eruption of a twisted filament observed in H{\alpha} by the Goode Solar Telescope at the Big Bear Solar Observatory. The extreme ultraviolet (EUV) and X-ray signatures of the event are generally consistent with the standard scenario of eruptive flares, with the presence of double flare ribbons connected by a bright flare arcade. Intriguingly, this partial eruption event features a microwave counterpart, whose spatial and temporal evolution closely follow the filament seen in H{\alpha} and EUV. The spectral properties of the microwave source are consistent with nonthermal gyrosynchrotron radiation. Using spatially resolved microwave spectral analysis, we derive the magnetic field strength along the filament spine, which ranges from 600-1400 Gauss from its apex to the legs. The results agree well with the non-linear force-free magnetic model extrapolated from the pre-flare photospheric magnetogram. We conclude that the microwave counterpart of the erupting filament is likely due to flare-accelerated electrons injected into the filament-hosting magnetic flux rope cavity following the newly reconnected magnetic field lines.

astro-ph.SR↗

arXiv2021

Tracing Halpha Fibrils through Bayesian Deep Learning

We present a new deep learning method, dubbed FibrilNet, for tracing chromospheric fibrils in Halpha images of solar observations. Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations. The FibrilNet tool is applied to high-resolution Halpha images from an active region (AR 12665) collected by the 1.6 m Goode Solar Telescope (GST) equipped with high-order adaptive optics at the Big Bear Solar Observatory (BBSO). We quantitatively assess the FibrilNet tool, comparing its image segmentation algorithm and fibril-fitting algorithm with those employed by the threshold-based tool. Our experimental results and major findings are summarized as follows. First, the image segmentation results (i.e., detected fibrils) of the two tools are quite similar, demonstrating the good learning capability of FibrilNet. Second, FibrilNet finds more accurate and smoother fibril orientation angles than the threshold-based tool. Third, FibrilNet is faster than the threshold-based tool and the uncertainty maps produced by FibrilNet not only provide a quantitative way to measure the confidence on each detected fibril, but also help identify fibril structures that are not detected by the threshold-based tool but are inferred through machine learning. Finally, we apply FibrilNet to full-disk Halpha images from other solar observatories and additional high-resolution Halpha images collected by BBSO/GST, demonstrating the tool's usability in diverse datasets.

astro-ph.SR↗

arXiv2020

Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning

Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propose a new deep learning method, called SolarUnet, to identify and track solar magnetic flux elements or features in observed vector magnetograms based on the Southwest Automatic Magnetic Identification Suite (SWAMIS). Our method consists of a data pre-processing component that prepares training data from the SWAMIS tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a post-processing component that prepares tracking results. SolarUnet is applied to data from the 1.6 meter Goode Solar Telescope at the Big Bear Solar Observatory. When compared to the widely used SWAMIS tool, SolarUnet is faster while agreeing mostly with SWAMIS on feature size and flux distributions, and complementing SWAMIS in tracking long-lifetime features. Thus, the proposed physics-guided deep learning-based tool can be considered as an alternative method for solar magnetic tracking.

astro-ph.SR↗