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The breakdown of a Mott-insulator when subjected to intense laser fields is characterized by the formation of doublon-hole pairs. This breakdown is furthermore evidenced by the production of high harmonics that can be experimentally measured. Here, we present an approach for extracting the doublon-hole correlation length of a Mott insulator. The method is based on a dynamical calculation of the Mott insulator’s rate of charge production in response to an applied strong-field laser pulse. We find that coupling the Mott insulator to a metal drastically increases the correlation length, in support of our recent hypothesis [Phys. Rev. B108,144434(2023)2469-995010.1103/PhysRevB.108.144434] that coupling to a metal enhances the charge fluctuations in the insulator. We confirm our conclusions using density matrix renormalization group (DMRG) calculations. The proposed method can be applied to experimentally measured observables, such as differential reflectivity or the high harmonic generation (HHG) spectrum to extract doublon-hole correlation length.

 

Here, we present the study of a compact emission source during an X1.3 flare on 2022 March 30. Within a ∼41 s period (17:34:48 UT to 17:35:29 UT), Interface Region Imaging Spectrograph observations show spectral lines of Mgii, Cii, and Siivwith extremely broadened, asymmetric red wings. This source of interest (SOI) is compact, ∼1.″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, 125, and 115 km s⁻¹for the Mgii, Cii, and Siivlines respectively. The red-most Gaussian fit suggests a Doppler velocity up to ∼160 km s⁻¹in 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, extreme-UV (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 the transition region and upper chromosphere, which 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.

 

Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a$$\gamma$$$\gamma$-class flare within the next 24 to 72 h. We consider three$$\gamma$$$\gamma$classes, namely the$$\ge$$$\ge$M5.0 class, the$$\ge$$$\ge$M class and the$$\ge$$$\ge$C class, and build three transformers separately, each corresponding to a$$\gamma$$$\gamma$class. Each transformer is used to make predictions of its corresponding$$\gamma$$$\gamma$-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.

 

Light bridges (LBs) are narrow structures dividing sunspot umbra, and their role in active region evolution is yet to be explored. We investigated the magnetic structure of the two LBs: a narrow LB (with width ∼810 km) and a considerably wider LB (2475 km) in the active region NOAA 12371. We employed: (1) the high-spatial-resolution spectropolarimetric data obtained by the Near InfraRed Imaging Spectropolarimeter (NIRIS) of the 1.6 m Goode Solar Telescope (GST) for studying the magnetic structure at the photosphere, and (2) the nonlinear force-free field (NLFFF) models, extrapolated from both the photospheric magnetogram from GST/NIRIS and from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, for studying the three-dimensional (3D) magnetic structure on a larger scale. Our observations reveal the presence of a field-free (or, more precisely, weak-field) region and the different velocity structures inside the two LBs. Analysis of the 3D NLFFF model shows a low-lying magnetic canopy as well as the enhanced current system above the LBs. The substantial difference between the LBs and the umbrae is found in the overall magnetic topology in that the field lines emanating from the two LBs are more twisted than that from the neighboring umbrae.

 

Abstract Cyclical variations of the solar magnetic fields, and hence the level of solar activity, are among the top interests of space weather research. Surface flows in global-scale, in particular differential rotation and meridional flows, play important roles in the solar dynamo that describes the origin and variation of solar magnetic fields. In principle, differential rotation is the fundamental cause of dipole field formation and emergence, and meridional flows are the surface component of a longitudinal circulation that brings decayed field from low latitudes to polar regions. Such flows are key inputs and constraints of observational and modeling studies of solar cycles. Here, we present two methods, local correlation tracking (LCT) and machine learning-based self-supervised optical flow methods, to measure differential rotation and meridional flows from full-disk magnetograms that probe the photosphere and $\text{H}\alpha$ H α images that probe the chromosphere, respectively. LCT is robust in deriving photospheric flows using magnetograms. However, we found that it failed to trace flows using time-sequence $\text{H}\alpha $ H α data because of the strong dynamics of traceable features. The optical flow methods handle $\text{H}\alpha $ H α data better to measure the chromospheric flow fields. We found that the differential rotation from photospheric and chromospheric measurements shows a strong correlation with a maximum of $2.85~\upmu \text{rad}\,\text{s}^{-1}$ 2.85 μrad s − 1 at the equator and the accuracy holds until $60^{\circ }$ 60 ∘ for the MDI and $\text{H}\alpha$ H α , $75^{\circ }$ 75 ∘ for the HMI dataset. On the other hand, the meridional flow deduced from the chromospheric measurement shows a similar trend as the concurrent photospheric measurement within $60^{\circ }$ 60 ∘ with a maximum of $20~\text{m}\,\text{s}^{-1}$ 20 m s − 1 at $40^{\circ }$ 40 ∘ in latitude. Furthermore, the measurement uncertainties are discussed. 

Spectral lines formed at lower atmospheric layers show peculiar profiles at the “leading edge” of ribbons during solar flares. In particular, increased absorption of the BBSO/GST Heiλ10830 line, as well as broad and centrally reversed profiles in the spectra of the Mgiiand Ciilines observed by the IRIS satellite, has been reported. In this work, we aim to understand the physical origin of such peculiar IRIS profiles, which seem to be common of many, if not all, flares. To achieve this, we quantify the spectral properties of the IRIS Mgiiprofiles at the ribbon leading edge during four large flares and perform a detailed comparison with a grid of radiative hydrodynamic models using theRADYN+FPcode. We also studied their transition region (TR) counterparts, finding that these ribbon front locations are regions where TR emission and chromospheric evaporation are considerably weaker compared to other parts of the ribbons. Based on our comparison between the IRIS observations and modeling, our interpretation is that there are different heating regimes at play in the leading edge and the main bright part of the ribbons. More specifically, we suggest that bombardment of the chromosphere by more gradual and modest nonthermal electron energy fluxes can qualitatively explain the IRIS observations at the ribbon leading front, while stronger and more impulsive energy fluxes are required to drive chromospheric evaporation and more intense TR emission in the bright ribbon. Our results provide a possible physical origin for the peculiar behavior of the IRIS chromospheric lines in the ribbon leading edge and new constraints for the flare models.

 

The Sun constantly releases radiation and plasma into the heliosphere. Sporadically, the Sun launches solar eruptions such as flares and coronal mass ejections (CMEs). CMEs carry away a huge amount of mass and magnetic flux with them. An Earth-directed CME can cause serious consequences to the human system. It can destroy power grids/pipelines, satellites, and communications. Therefore, accurately monitoring and predicting CMEs is important to minimize damages to the human system. In this study we propose an ensemble learning approach, named CMETNet, for predicting the arrival time of CMEs from the Sun to the Earth. We collect and integrate eruptive events from two solar cycles, #23 and #24, from 1996 to 2021 with a total of 363 geoeffective CMEs. The data used for making predictions include CME features, solar wind parameters and CME images obtained from the SOHO/LASCO C2 coronagraph. Our ensemble learning framework comprises regression algorithms for numerical data analysis and a convolutional neural network for image processing. Experimental results show that CMETNet performs better than existing machine learning methods reported in the literature, with a Pearson product-moment correlation coefficient of 0.83 and a mean absolute error of 9.75 h.