Search for: All records

Abstract 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 simulations. We find that while the small-scale emerging flux does not significantly alter the preeruption 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 preeruption evolution can strongly influence the subsequent dynamics, suggesting that small-scale emerging flux can play a critical role in accelerating solar eruptions. 

Abstract We investigated the initiation and the evolution of an X7.1-class solar flare observed in NOAA Active Region 13842 on 2024 October 1, based on a data-constrained magnetohydrodynamic (MHD) simulation. The nonlinear force-free field (NLFFF) extrapolated from the photospheric magnetic field about 1 hr 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 preeruption 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 by-product of the eruption due to reconnecting of postflare arcade, but also plays a significant role in accelerating the MFR during the eruption. 

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 We conducted data-constrained magnetohydrodynamic (MHD) simulations for solar active region (AR) NOAA AR 11429, which produced two X-class flares within a span of 63 minutes. The simulations were performed using the zero-βMHD approximation, with the initial condition derived from the nonlinear force-free field extrapolated from the photospheric magnetograms taken 2 hr before the first X5.4 flare. During the simulation, we enhanced magnetic reconnection locally by applying anomalous resistivity in the induction equation within the regions of interest. As a result, the simulations successfully reproduced the expansion of two magnetic flux ropes (MFRs) corresponding to the two observed eruptions. The result shows that the difference in stability between the two MFRs is related to the location of the magnetic reconnection that triggers the solar eruptions. Furthermore, comparison with the analysis of failed MFR eruptions indicates that both the initiation reconnection and the subsequent driving mechanism, torus instability, are equally important for a successful eruption. This simulation reveals a new mechanism in which long loops, formed via tether-cutting reconnection, push up the overlying twisted field lines, leading to their destabilization by torus instability. 

Abstract The dynamic structures of solar filaments prior to solar flares provide important physical clues about the onset of solar eruptions. Observations of those structures under subarcsecond resolution with high cadence are rare. We present high-resolution observations covering preeruptive and eruptive phases of two C-class solar flares, C5.1 (SOL2022-11-14T17:29) and C5.1 (SOL2022-11-14T19:29), obtained by the Goode Solar Telescope at Big Bear Solar Observatory. Both flares are ejective, i.e., accompanied by coronal mass ejections (CMEs). High-resolution Hαobservations reveal details of the flares and some striking features, such as a filament peeling process: individual strands of thin flux tubes are separated from the main filament, followed shortly thereafter by a flare. The estimated flux of rising strands is in the order of 10¹⁷Mx, versus the 10¹⁹Mx of the entire filament. Our new finding may explain why photospheric magnetic fields and overall active region and filament structures as a whole do not have obvious changes after a flare, and why some CMEs have been traced back to the solar active regions with only nonerupting filaments, as the magnetic reconnection may only involve a very small amount of flux in the active region, requiring no significant filament eruptions. We suggest internal reconnection between filament threads, instead of reconnection to external loops, as the process responsible for triggering this peeling of threads that results in the two flares and their subsequent CMEs. 

How impulsive solar energetic particle (SEP) events are produced by magnetic-reconnection-driven processes during solar flares remains an outstanding question. Here we report a short-duration SEP event associated with an X-class eruptive flare on 2021 July 3, using a combination of remote sensing observations and in situ measurements. The in situ SEPs were recorded by multiple spacecraft including the Parker Solar Probe. The hard X-ray (HXR) light curve exhibits two impulsive periods. The first period is characterized by a single peak with a rapid rise and decay, while the second period features a more gradual HXR light curve with a harder spectrum. Such observation is consistent with in situ measurements: the energetic electrons were first released during the early impulsive phase when the eruption was initiated. The more energetic in situ electrons were released several minutes later during the second period of the impulsive phase when the eruption was well underway. This second period of energetic electron acceleration also coincides with the release of in situ energetic protons and the onset of an interplanetary type III radio burst. We conclude that these multimessenger observations favor a two-phase particle acceleration scenario: the first, less energetic electron population was produced during the initial reconnection that triggers the flare eruption, and the second, more energetic electron population was accelerated in the region above the loop-top below a well-developed, large-scale reconnection current sheet induced by the eruption. 

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. 

Abstract 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 are 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. 

Abstract Minifilament eruptions producing small jets and microflares have mostly been studied based on coronal observations at extreme-ultraviolet and X-ray wavelengths. This study presents chromospheric plasma diagnostics of a quiet-Sun minifilament of size ∼ 2″ × 5″ with a sigmoidal shape and an associated microflare observed on 2021 August 7 17:00 UT using high temporal and spatial resolution spectroscopy from the Fast Imaging Solar Spectrograph (FISS) and high-resolution magnetograms from the Near InfraRed Imaging Spectropolarimeter (NIRIS) installed on the 1.6 m Goode Solar Telescope at Big Bear Solar Observatory. Using FISS Hαand Caii8542 Å line spectra at the time of the minifilament activation we determined a temperature of 8600 K and a nonthermal speed of 7.9 km s⁻¹. During the eruption, the minifilament was no longer visible in the Caii8542 Å line, and only the Hαline spectra were used to find the temperature of the minifilament, which reached 1.2 × 10⁴K and decreased afterward. We estimated thermal energy of 3.6 × 10²⁴erg from the maximum temperature and kinetic energy of 2.6 × 10²⁴erg from the rising speed (18 km s⁻¹) of the minifilament. From the NIRIS magnetograms we found small-scale flux emergence and cancellation coincident with the minifilament eruption, and the magnetic energy change across the conjugate footpoints reaches 7.2 × 10²⁵erg. Such spectroscopic diagnostics of the chromospheric minifilament complement earlier studies of minifilament eruptions made using coronal images.