Search for: All records

Abstract 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. 

Given just a few glimpses of a scene, can you imagine the movie playing out as the camera glides through it? That's the lens we take on \emph{sparse-input novel view synthesis}, not only as filling spatial gaps between widely spaced views, but also as \emph{completing a natural video} unfolding through space. We recast the task as \emph{test-time natural video completion}, using powerful priors from \emph{pretrained video diffusion models} to hallucinate plausible in-between views. Our \emph{zero-shot, generation-guided} framework produces pseudo views at novel camera poses, modulated by an \emph{uncertainty-aware mechanism} for spatial coherence. These synthesized frames densify supervision for \emph{3D Gaussian Splatting} (3D-GS) for scene reconstruction, especially in under-observed regions. An iterative feedback loop lets 3D geometry and 2D view synthesis inform each other, improving both the scene reconstruction and the generated views. The result is coherent, high-fidelity renderings from sparse inputs \emph{without any scene-specific training or fine-tuning}. On LLFF, DTU, DL3DV, and MipNeRF-360, our method significantly outperforms strong 3D-GS baselines under extreme sparsity. Our project page is at \url{https://decayale.github.io/project/SV2CGS}. 

Abstract The southward component of the interplanetary magnetic field, often originating from solar coronal mass ejections (CMEs), plays a crucial role in driving geomagnetic storms. Accurate prediction of the flux rope orientation of CMEs as they arrive at Earth requires a clear understanding of how the orientation of magnetic flux ropes evolves from the solar corona to 1 au. In this study, we investigated six geoeffective CMEs, initiated either from active regions (ARs; three events) or quiet-Sun (QS) filament eruptions (three events). The orientation prior to the eruption is determined by the eruptive filament or the magnetic flux rope near the solar surface. During the CME propagation away from the Sun, the graduated cylindrical shell model and the Grad–Shafranov technique are used to estimate the orientation of the CME magnetic field structure. Our results show that the orientation of flux ropes associated with QS eruptions did not change from the Sun to 1 au. For three rotating events initiated from ARs, the direction of rotation remains consistent during the propagation from the Sun to 1 au. The trend indicates that the heliospheric current sheet has a relatively limited influence on these events. For rotating events, the direction of rotation basically follows the prediction of Lynch’s model. 

Abstract 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 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 hr 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 the literature. 

Context.High-resolution magnetograms are crucial for studying solar flare dynamics because they enable the precise tracking of magnetic structures and rapid field changes. The Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory (SDO/HMI) has been an essential provider of vector magnetograms. However, the spatial resolution of the HMI magnetograms is limited and hence is not able to capture the fine structures that are essential for understanding flare precursors. The Near InfraRed Imaging Spectropolarimeter on the 1.6 m Goode Solar Telescope (GST/NIRIS) at Big Bear Solar Observatory (BBSO) provides a better spatial resolution and is therefore more suitable to track the fine magnetic features and their connection to flare precursors. Aims.We propose DeepHMI, a machine-learning method for solar image super-resolution, to enhance the transverse and line-of-sight magnetograms of solar active regions (ARs) collected by SDO/HMI to better capture the fine-scale magnetic structures that are crucial for understanding solar flare dynamics. The enhanced HMI magnetograms can also be used to study spicules, sunspot light bridges and magnetic outbreaks, for which high-resolution data are essential. Methods.DeepHMI employs a conditional diffusion model that is trained using ground-truth images obtained by an inversion analysis of Stokes measurements collected by GST/NIRIS. Results.Our experiments show that DeepHMI performs better than the commonly used bicubic interpolation method in terms of four evaluation metrics. In addition, we demonstrate the ability of DeepHMI through a case study of the enhancement of SDO/HMI transverse and line-of-sight magnetograms of AR 12371 to GST/NIRIS data. 

Abstract On 2024 July 25, while observing the solar active region NOAA 13762 with the high-resolution 1.6 m Goode Solar Telescope at the Big Bear Solar Observatory, we witnessed two mysterious phenomena: the partial detachment of filament strands from its main body in the chromosphere and the sudden disappearance of a sunspot penumbra in the photosphere, the former accompanied by small flares. Our analysis reveals a spatiotemporal correlation between the filament peeling process and the penumbral disappearance. To understand the above observations physically, we performed a magnetohydrodynamic simulation that successfully replicated the disappearance of the penumbra as a consequence of weakened horizontal magnetic field. The simulations demonstrate that both the filament peeling and the penumbral decay are driven by the same underlying process: the upward expansion of the magnetic flux rope induced by null point magnetic reconnection. These results suggest a novel mechanism by which the Sun sheds magnetic flux to interplanetary space in the form of filament peeling and penumbral disappearance. 

Abstract We investigate the aftermath of a giant quiescent solar filament eruption on 2023 December 24. One feature of the eruption is an extensive fan above the filament channel that is about 3 times as wide as similar structures that appear above active regions (ARs) during solar flares. The fan contains numerous supra-arcade downflows (SADs), and we investigate the largest SADs with continuous Hinode X-ray Telescope observations. The measured maximum width of the SADs in this event is at least 3 times the maximum width of SADs observed in AR flares, whereas the velocities of the largest SADs are similar to the typical values of AR SADs. The kinetic characteristics of the largest SADs observed in this event align with previous model predictions, where SADs originate from the nonlinear development of Rayleigh–Taylor-type instabilities. In this scenario, the larger system size allows the existence of larger-scale instabilities, while the development of the velocities of these instabilities is expected to be independent of the system size. Compared to AR flares, the temperature and emission measure in this event are lower, and there is less overall radiation, resulting in no evident Geostationary Operational Environmental Satellite signature. Similar to those in AR flares, SADs show lower temperatures compared to the surrounding fan plasma. Our observations show that SADs are present in a wide variety of eruptions. The reconnection mechanisms present in quiescent filament eruptions are similar to those driving more compact eruptions originating from ARs. 

Abstract 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 hr 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 on board the Solar Dynamics Observatory. We survey and match CME events in the Space Weather Database Of Notification, Knowledge, Information and the Large Angle and Spectrometric Coronagraph CME Catalog, and we compile a list of CMEs, including halo CMEs and nonhalo CMEs, associated with ARs in the period between 2010 November and 2023 August. We use the information gathered above to build the labels (positive vs. 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 statistic (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. 

Abstract 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. 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.