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Haimin Wang

Publications and source records attributed to Haimin Wang.

At least 19 recordsLinked to original sources
arXiv2026

Collective Characterization of Polar Crown Filament Eruptions

Polar crown filaments (PCFs) are crucially related to the structure of solar polar magnetic fields. They are physically significant because of the ``rush-to-the-pole'' motion, which is an indicator of the poleward drift of magnetic fields from decayed active regions. The formation and evolution of PCFs offer important insights into the Sun's magnetic activity at high latitudes and the solar cycle variation. In this study, we present a collective analysis of 50 PCF disappearance events covering solar cycle 24 and 25. Among them, 24 show rotational motions, and nine have sufficiently clear magnetic configurations for comparison with numerical model predictions. In all nine events, the observed rotation directions agree with the model predictions, suggesting possible magnetic-flux-rope-like structures in these events. All except one of the PCFs in our sample disappeared through eruption rather than thermal disappearance, and most associated CMEs were slow ($<800$ km s$^{-1}$). For events accompanied by transient coronal holes, the estimated magnetic flux associated with the dimming regions is on the order of $10^{20}$--$10^{21}$ Mx. Time--distance analysis identifies 22 events with a clear slow-rise to fast-rise transition, with onset heights ranging from several tens to about 160 Mm and a mean onset velocity of about 2.8 km s$^{-1}$ after excluding the highest-velocity event. Together, these findings characterize the magnetic structure and dynamics of PCF eruptions.

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arXiv2026

Tiny Quiet-Sun EUV Ejections Revealed by High-resolution Multiwavelength Observations

We investigate two successive tiny quiet-Sun EUV ejections on 2022 October 29 using coordinated Solar Orbiter/EUI, IRIS, SDO/AIA, and Goode Solar Telescope observations. We examine their low-lying geometry and the chromospheric and transition-region response before and between the releases. A curved H-alpha thread is present beforehand, and Mg ii and Si iv spectra reveal a compact multithermal precursor 2.6--2.7 min before the first EUV brightening. EUI--AIA stereoscopy places the recurrent compact EUV kernel near 2.2 Mm and the sampled emission at approximately 0.6--4.2 Mm along inclined, locally arched structures. The first ejection shows multithreaded, helical-looking EUV strands; the second shows straighter strands and a decrease in the cross-ridge separation of redshifted and blueshifted Mg ii k3 samples from 1.47'' to 0.89''. Nevertheless, the two peaks have comparable emission-measure increments and estimated event-associated thermal contents of a few 10^26 erg under the same assumptions. Before the second ejection, the regional Si iv and k3 blueshift signals remain enhanced, while the Mg ii central reversal along the activated ridge remains shallower than before the event. Component reconnection between the curved structure and the surrounding field offers a possible explanation. The observations show that jet-like EUV emission can occur in low-lying structures. The second ejection begins before the chromospheric and transition-region spectra have returned to their pre-event states. Its spectral signatures may therefore depend on the atmospheric changes associated with the first ejection as well as on the additional energy input.

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arXiv2026

Cross-Modal Solar Image Synthesis: Adapting the Surya Foundation Model from He I 10830 Å to EUV Translation and Coronal Hole Segmentation

The long observational record of He I 10830 Å offers a means to investigate solar morphology before modern extreme-ultraviolet (EUV) imaging. We adapt the Surya solar foundation model to predict Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 94, 193, and 304 Å images and a coronal hole (CH) probability map from full-disk helium observations. A convolutional input adapter, low-rank backbone updates, and dedicated output decoders learn from temporally paired, geometrically registered observations, with Spatial Possibilistic Clustering Algorithm (SPOCA) catalog polygons providing CH supervision. On observations held out from downstream fine-tuning, the selected dedicated models achieve disk-restricted correlation coefficients (CCs) of 0.8196, 0.8885, and 0.8398 for the three AIA channels, respectively; the CH model achieves an intersection over union (IoU) of 0.4360. The predictions recover broad solar structure, although local agreement varies substantially by channel. An optional residual refiner addresses spatial detail, and its application on pre-SDO dates improves the correlation of synthetic AIA 304 with Solar and Heliospheric Observatory/Extreme-ultraviolet Imaging Telescope (SOHO/EIT) 304 references from 0.6511 to 0.7085. Together, these results support the feasibility of helium-conditioned EUV morphological proxies and motivate their use in historical reconstruction, within the scope of the downstream test and cross-instrument evaluation.

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arXiv2026

Study of Chromospheric Global-Scale Flows using Local Correlation Tracking Across the past 10 Solar Cycles

Global-scale solar surface flows, including differential rotation and meridional circulation, provide key observational constraints on the solar dynamo. We investigate these flows using local correlation tracking (LCT) of chromospheric intensity structures in digitized \ion{Ca}{2}~K observations from the Kodaikanal Solar Observatory covering Solar Cycles (SC) 14 to 23 (1907 -- 2007). The most valuable data coverage are from cycles 14 to 19 for two reasons: (1) it provided record for early cycles that are not commonly available; (2) it had much better duty cycle than the data of cycles 20-23, therefore the measurements had much less errors. On the other hand, the information loss in cycles 20-23 are compensated by other modern space observations. The recovered differential rotation shows relatively small cycle-to-cycle variations and remains close to the Howard reference profile. Recurring zonal-flow residual bands at low and middle latitudes are broadly associated with the activity belts during the well-sampled SC~15--19. The meridional flow is predominantly poleward in the earlier record and exhibits larger relative cycle-to-cycle variations and uncertainties. Incomplete coverage of SC~14 and sparse sampling during SC~21--23 limit detailed comparisons to the better-covered SC~15--20. Compared with its immediate successor SC~19, the strongest cycle in the record, SC~18 exhibits a stronger and continuous northern poleward meridional branch, peaking two to three years before its sunspot maximum. SC~19 shows a minor enhancement that peaks earlier prior to its own maximum. An independent comparison using PSPT observations yields a full-period correlation of $r=0.91$ between the LCT and SDO/HMI meridional-flow profiles. These results extend the observational context for solar-cycle-related flows over a century and motivate further refinement through data-driven reconstruction.

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arXiv2026

Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra

Strong chromospheric absorption lines such as H$α$ 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere. Multilayer spectral inversion (MLSI) offers a physically interpretable framework for modeling these lines using a finite number of radiative-transfer layers, but conventional MLSI relies on pixel-by-pixel nonlinear least-squares fitting, making it computationally expensive for large imaging spectroscopic data sets. Here, we introduce a physics-informed neural-network (PINN) framework to accelerate MLSI while preserving its analytic radiative-transfer formulation. The network predicts MLSI parameters directly from observed line profiles and passes them through a differentiable MLSI forward model to synthesize spectra. Training follows a two-stage approach: an initial stage optimized solely via spectral reconstruction loss, followed by fine-tuning that combines spectral consistency with parameter-space supervision from conventional MLSI results on a single reference image. This strategy eliminates the need for large precomputed training sets while maintaining physical interpretability. Applied to Fast Imaging Solar Spectrograph (FISS) observations from the Goode Solar Telescope (GST) targeting both quiet-Sun and active-region regions, MLSI-PINN parameter maps reproduce the primary spatial structures of direct inversions, achieving an arithmetic mean pixel-wise Pearson correlation coefficient of 0.933 across all evaluated parameters. The reconstructed spectra closely match both observed profiles and conventional MLSI fits. Post-training, MLSI-PINN processes a raster in approximately 5-15 seconds compared to 3-5 minutes for conventional MLSI, delivering an inference speedup of about 12-60 times without substantial loss in reconstruction quality, enabling efficient MLSI analysis on large chromospheric data sets.

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arXiv2026

Toward Operational Solar Flare Peak Flux Nowcasting: A Strategy Combining Real-Time Data, Machine Learning, and NOAA Flare Detection Criteria

We present the RMN strategy (Real-time data, machine learning, and NOAA flare detection criteria) for nowcasting the peak soft X-ray flux of ongoing solar flares under operationally realistic conditions. The strategy combines real-time GOES 0.1-0.8 nm X-ray observations with an attention-based sequence-to-sequence Long Short-Term Memory model. Under the NOAA flare detection criteria, predictions are evaluated at one-minute intervals from three minutes after the cataloged onset to the observed peak using the preceding 60 minutes of X-ray observations. We apply the RMN strategy to C-, M-, and X-class flares observed by GOES-8-18 from 1997 to 2024 using four-fold cross-validation. The major results of this study are as follows. First, the model nowcasts peak soft X-ray flux with RMSE and PE values of 0.26 and 3.11\% for the $\geq$C-class group, 0.45 and 5.59\% for the $\geq$M-class group, and 0.87 and 12.76\% for the X-class group. The higher discrepancy toward stronger flare groups indicates that peak-flux prediction is more challenging for higher-intensity flares. Second, the model performance depends on flare rise time and prediction time, with larger errors for longer rise time events and improved performance as the prediction time approaches the flare peak. Shorter rise time events approach their final peak more rapidly, providing a clearer indication of the eventual peak, whereas the larger difference for longer rise time events may partly reflect more complex temporal evolution. Third, empirical coverage based on total uncertainty remains high but decreases for stronger flares, with noise uncertainty contributing more than model uncertainty.

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arXiv2026

Statistical Analysis of Minifilament Eruptions Using Full-disk H$α$ Blue-wing Observations at Big Bear Solar Observatory

This paper presents a comprehensive statistical analysis of minifilament eruptions (MFEs) using high-cadence full-disk H$α$ blue-wing observations from Big Bear Solar Observatory. Despite the recognized importance of MFEs for coronal dynamics and solar wind structuring, previous efforts were often limited by small sample sizes or restricted fields of view, leaving open questions about their global occurrence and characteristic properties. We developed an algorithm incorporating intensity thresholding and temporal tracking to detect sudden enhancements in H$α$ blue-wing images. A total of 1986 such events were identified during a 4 hr period on 2020 June 9. These detections were cross-validated using H$α$ line-center observations to confirm the presence of associated filament structures. The analysis yields an occurrence rate of $\sim$6.6$\times 10^4$ per day, an average length of $\sim$17 Mm, and a typical lifetime of $\sim$21 minutes. A power-law distribution in eruption lengths (with slope $\sim$-4.8) and a sublinear scaling between duration and length suggest that larger eruptions tend to last longer. The length distribution of MFEs was further analyzed relative to active region proximity, revealing that eruptions occurring nearby a sunspot region were, on average, larger than the global mean eruption length. Additionally, an eruption density map was used to analyze the spatial distribution of MFEs relative to coronal hole boundaries. The results indicate a mild suppression of activity inside coronal holes and intermediate eruption frequency in the boundary regions. We briefly discuss their potential role in structuring the small-scale solar wind features such as magnetic switchbacks and small-scale magnetic flux ropes.

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arXiv2026

A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares

We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.5 nm over three consecutive days during significant solar flares, using multi-instrument observations from NASA's Solar Dynamics Observatory (SDO). We consider 33 significant flares in the period between 2011 and 2014 in Solar Cycle 24. The SDO observations include 13 co-aligned full-disk images, comprising eight AIA EUV/UV and five HMI magnetic/continuum products. FlareEUV learns the relationship between magnetic structure and coronal emission from the raw imaging data using a lightweight attention-based architecture. Our experimental results demonstrate the good performance of FlareEUV in short-term EUV irradiance forecasting during the significant flares and its superiority over baseline methods.

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arXiv2026

Non-thermal Sources from Stereoscopic Hard X-ray and Earth-based Microwave Observations in a Data-Constrained Magnetohydrodynamic Simulation

We analyze the X7.1 flare on 2024 October 1 from NOAA AR 13842 using hard X-ray (HXR) imaging, microwave observations by the Expanded Owens Valley Solar Array (EOVSA), and a three-dimensional Magnetohydrodynamic (MHD) simulation. The flare was observed from two vantage points, with Solar Orbiter/Spectrometer Telescope for Imaging X-rays viewing the flare near the limb and Advanced Space-based Solar Observatory/Hard X-ray Imager and EOVSA observing it on the disk. We carried out a data-constrained MHD simulation using a nonlinear force-free field extrapolation as the initial condition and constrained the height of the non-thermal looptop source from stereoscopic HXR and microwave observations. The height is consistent between the stereoscopic analysis and the MHD simulation. A secondary non-thermal microwave source aligned with a southward plasma ejection corresponds to an elongated current sheet. Although the current sheet grows in multiple directions, the secondary microwave emission is observed only from the southern segment. This localization suggests reconnection in regions with different magnetic field strengths. Reconnection in strong-field regions produces flare arcades with dominant looptop emission, whereas reconnection in weaker southern regions gives rise to secondary microwave emission at higher altitudes. The height of the secondary source is consistent between the stereoscopic analysis and the MHD simulation. Microwave spectral fitting suggests a higher low-energy cutoff for non-thermal electrons in the secondary microwave source than in the main looptop source. This may reflect the transport of electrons pre-accelerated near the looptop source by the southward plasma ejection.

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arXiv2026

Reconstructing Synthetic SDO/AIA 193 A EUV Images from He I 10830 A Observations with Diffusion Model Translator

Routine full-disk EUV imaging has been available only since the modern era, such as SOHO and SDO. To extend EUV coronal context into earlier periods, we leverage the multi-decade availability of full-disk \HeI{} observations, whose absorption is modulated by coronal irradiance and magnetic topology and is widely used as a proxy for open-field regions. We present a diffusion-based conditional image translation framework, Coronal Hole-aware Diffusion Model Translator (CH-aware DMT), to reconstruct synthetic SDO/AIA 193 Å EUV images from \HeI{} inputs. The model is trained on temporally co-aligned SOLIS \HeI{} and AIA 193 Å pairs spanning 2011--2015 using a month-based split, where January--October are used for training, November is used for validation, and December for testing. On the held-out test set, the reconstructions preserve dominant full-disk EUV morphology (CC=0.92) and recover CH-related low-intensity structure (CC=0.84). We further assess historical applicability by (1) comparing reconstructed AIA 193 Å morphology with SOHO/EIT 195 Å over 2005--2015; (2) comparing reconstructed AIA 193 Å images generated from KPVT \HeI{} inputs against Yohkoh/SXT soft X-ray observations; and (3) evaluating long-term reconstructed disk-integrated emission statistics against observational EUV series and independent solar activity proxies (sunspot number and F10.7 radio flux over 1974--2015). These results indicate that CH-aware DMT conditioned on \HeI{} can provide a physically plausible synthetic AIA 193 Å coronal proxy for historical studies, supporting multi-decade analyses of large-scale coronal evolution before the direct EUV imaging was available.

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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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arXiv2026

Few Made It Out: A Multi-Messenger Study of an In Situ Solar Energetic Electron Event Driven by a Solar Jet

When in situ solar energetic electron (SEE) events are closely associated with nonthermal flares, the escaping electron population is frequently observed to be much smaller than the nonthermal-radiation-emitting population near the solar surface. If a single accelerated population drives both signatures, the physical mechanism causing this severe deficit of upward-propagating electrons remains poorly understood.Focusing on one of the 2022 November 10--12 SEE events associated with recurrent solar jets and interplanetary type III radio bursts, we present a new, combined microwave--X-ray analysis using the Expanded Owens Valley Solar Array (EOVSA) and the Spectrometer/Telescope for Imaging X-rays (STIX) aboard Solar Orbiter. This synergy enables, for the first time for such an event, spatially resolved diagnostics over a broad energy spectrum of the near-Sun energetic electrons, complemented by in situ measurements made by spacecraft at multiple heliocentric longitudes and distances. Consistent with earlier results based on in situ and X-ray data, our results show that only 0.1--1\% of energetic electrons escape into interplanetary space. Crucially, the new microwave spectral imaging analysis suggests that energetic electrons are strongly concentrated in a compact region just above a mini-flare arcade at the base of the jet spire, and that their number density decreases by at least two orders of magnitude in the direction of the jet spire away from this region. This steep gradient, revealed by the microwave diagnostics, points to efficient local acceleration and trapping in the region analogous to the above-the-looptop ``magnetic bottle'' region in major eruptive flares, allowing only a small fraction of electrons to access open magnetic field lines and enter interplanetary space.

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arXiv2026

Stereoscopic Observations of Solar X-ray Sources Explained by a Data-Constrained Magnetohydrodynamic Simulation

We investigated the three-dimensional (3D) magnetic structures and dynamics responsible for particle acceleration in an X7.1-class flare that occurred on October 1, 2024, in NOAA active region 13842. We combined stereoscopic hard X-ray (HXR) observations from the Advanced Space-based Solar Observatory/Hard X-ray Imager (HXI) and the Solar Orbiter/Spectrometer Telescope for Imaging X-rays (STIX) with a 3D magnetohydrodynamic (MHD) simulation constrained by observed photospheric magnetic fields. During the two main peaks of the impulsive phase, HXR footpoints appeared at different locations, indicating a migration of the primary reconnection site in the corona. Our data-constrained MHD simulation successfully reproduced the reconnected field lines linking the observed conjugate HXR footpoints. Furthermore, the simulation shows that these primary reconnections occur along a single quasi-separatrix layer (QSL) system. Therefore, the two main peaks of HXR can be interpreted as episodic energy release within the single QSL system. This study demonstrates that the data-constrained MHD model provides a realistic 3D magnetic context for interpreting HXR emission. Notably, STIX observations revealed a vertically distributed thermal HXR source, extending from the footpoints to the looptop, with its centroid migrating between the two peaks. This marks a first step toward understanding the particle acceleration processes in solar flares.

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arXiv2026

Out-of-Sample Validation of MagNet

Machine learning is starting to be used in almost every industry and academic research, and solar physics is no exception. A newly developed machine learning model named MagNet helps us to tackle some of the most serious challenges in data mining by generating transverse fields of solar active regions. Being trained on line-of-sight magnetograms from Michelson Doppler Imager at Solar and Heliospheric Observatory (SOHO/MDI), Hα maps from Big Bear Solar Observatory and Kanzelhohe Solar Observatory and vector magnetograms from Helioseismic and Magnetic Imager at Solar Dynamic Observatory (SDO/HMI), this model provides vector magnetograms in active regions for SOHO/MDI data covering the strong solar cycle 23. In this study, we performed out-of-sample validation of the MagNet model with data from Imaging Vector Magnetograph (IVM) at Mees Solar Observatory, which was not included in the training process. Our results show good correlation between the AI generated data and the observed vector magnetograms and therefore strengthen the confidence of implementing MagNet to the entire SOHO/MDI archive and future scientific analysis of the AI generated data.

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arXiv2025

Driving mechanisms of solar active region geysers: The role of interacting magnetic flux

Active region recurrent jets are manifestations of episodic magnetic energy release processes driven by complex interactions in the lower solar atmosphere. While magnetic flux emergence and cancellation are widely recognized as key contributors to jet formation, the mechanisms behind repeated magnetic reconnection remain poorly understood. In this letter, we report a sequence of nine recurrent jets originating from active region AR 12715 during its decay phase, where the jet activity was associated with a complex distribution of fragmented magnetic flux. Non-linear force-free field (NLFFF) extrapolations reveal the presence of low-lying, current-carrying loops beneath overarching open magnetic fields near the jet footpoints. These magnetic structures were perturbed by (i) emerging flux elements and (ii) interactions between oppositely polarized moving magnetic features (MMFs). To interpret these observations, we compare them with 3D radiative MHD simulation from the Bifrost model, which reproduce jet formation driven by interacting bipolar MMFs, leading to subsequent flux cancellation in the photosphere. Our results emphasize the critical role of MMF-driven flux interactions in initiating and sustaining recurrent jet activity in active regions.

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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.

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