IntroductionObservations show that many filaments and hot channels undergo large- angle rotations during eruptions. Such rotations can modify the southward magnetic-field component (Bz) of the resulting coronal mass ejections (CMEs), thereby influencing their geo-effectiveness. Understanding the mechanisms of the flux-rope rotation is therefore important for improving space-weather forecasting.MethodsIn this work, we perform an observationally data-constrained magnetohydrodynamic (MHD) simulation to reproduce the three-dimensional magnetic evolution of the eruption from active region 12371.DiscussionThe simulated flux rope undergoes a rotation exceeding 90°, while the eruption is found to be triggered by torus instability. By decomposing the Lorentz force, we show that the rotation is primarily driven by the interaction between the flux-rope current and external magnetic fields (JFR × Be), whereas the self-force of the flux rope (JFR × BFR) acts in the opposite direction and tends to suppress the rotation.ConclusionThese results indicate that the flux rope rotation is governed mainly by the surrounding magnetic environment rather than by the intrinsic properties of the flux rope. Consequently, large-angle rotations do not necessarily imply the presence of a strongly twisted pre-eruptive flux rope or that the eruption is triggered by the kink instability.
Aurora mobile applications (apps) have become a common way for aurora chasers and other members of the public to access space weather data. For many users, an aurora app is the first encounter with geomagnetic indices, solar wind conditions, and auroral probability maps. These interfaces often support time-sensitive decisions about whether to go outside, where to drive, and how to interpret changing conditions. This supports public engagement and informal science learning but presents communication challenges when complex geophysical products are translated into simplified aurora viewing maps. We examine how current aurora apps present auroral visibility. We draw on a community poll identifying commonly used apps, followed by an exploratory review of app interfaces, focusing on displayed data streams, communication methods, and the explanatory content available to users. We also assessed the readability of in-app explanatory text using the Flesch–Kincaid Grade Level formula. Geomagnetic indices such as Kp and empirical auroral oval models such as OVATION were the most common ways apps conveyed visibility expectations. However, apps often omitted whether displayed products were based on measurements or model output, and when they were last updated. Readability varied widely, with explanatory text ranging from secondary school to college level in the subset of apps with sufficient text for assessment. These findings suggest that modest changes in labeling, time framing, provenance metadata, and plain-language explanation could support more consistent interpretation. Aurora apps offer a lens into how space weather products reach the public, and how communication design may shape trust, comprehension, and decision-making.
The SunRISE Ground Radio Lab (GRL) at the University of Michigan develops and deploys Long Wavelength Array (LWA) antennas operating over 40–160 MHz to support low-frequency radio heliophysics and student training. This paper presents the scientific and educational outcomes of a dual-site observing campaign conducted during the 8 April 2024, total solar eclipse, which provided a valuable natural laboratory to investigate rapid, eclipse-driven ionospheric variability driven by the sudden reduction of solar extreme ultraviolet (EUV) and X-ray input. LWA systems were deployed at Upland, Indiana, located along the path of totality, and at Peach Mountain Observatory, Michigan, which experienced approximately 95% solar obscuration. The dynamic spectra at both sites were dominated by persistent and intermittent narrowband features consistent with radio-frequency interference, while the integrated intensity curves showed complex, nonmonotonic variability that did not follow eclipse obscuration in a simple manner. At Upland, the intensity minimum occurred before eclipse onset and was followed by a gradual increase through the eclipse interval. At Peach Mountain, the signal exhibited smaller and more irregular variations during and after maximum obscuration. These results indicate that the measured radio intensity reflects the combined influence of evolving solar-disk and coronal emission, ionospheric propagation, background variability, and residual interference rather than eclipse geometry alone. Complementary GNSS-derived total electron content measurements near Upland showed eclipse-associated F-region variability of approximately 5–8 TECU. Because the LWA and GNSS observations probe different ionospheric regions and physical processes, the two datasets are interpreted as complementary rather than directly equivalent diagnostics. Beyond the scientific observations, the campaign integrated undergraduate and master’s students into antenna deployment, calibration, real-time monitoring, data processing, and scientific communication, providing practical experience in field-based heliophysics research and public outreach.
During the earliest phases of star and planet formation, a young star is surrounded by a disc that can become gravitationally unstable. Indeed, if the protoplanetary disc is sufficiently massive, its self-gravity triggers the formation of large scale spiral arms, transporting angular momentum, trapping solid particles and, potentially, collapsing to form sub-stellar companions. In this paper, we review the theoretical and observational advances in the study of gravitational instability in protoplanetary discs over the last 10 years, since the advent of ALMA. These developments have transformed our understanding of gravitational instability, moving beyond its historical role as a theoretical mechanism for giant planet formation toward a physically rich framework that can be directly tested against high-resolution observations.
To elucidate the relationship between electric field and electron-density variations in the high-latitude ionosphere associated with Pc5 ultralow-frequency (ULF) waves from subauroral to high latitudes, we analyzed the global navigation satellite system (GNSS)-total electron content (TEC), ionospheric plasma flow observed by the Super Dual Auroral Radar Network (SuperDARN), and electron density in the inner magnetosphere measured by the Arase satellite. On 23 November 2022, the SuperDARN Prince George (PGR) radar in the dusk sector detected meridional plasma flow oscillations with periods and amplitudes of 5 min and 10–60 m/s, respectively. The plasma flow oscillations began at approximately 01:10 UT and persisted until 03:30 UT over a magnetic latitude range of 65°–72°. The amplitude increased as the magnetic latitude increased. The electron density profile observed by the Arase satellite did not exhibit a sharp gradient during the inner magnetosphere. This indicates that the plasmasphere extended beyond the apogee of the Arase satellite (6.1 Re, where Re is Earth’s radius) under quiet geomagnetic conditions. A detailed comparison between SuperDARN radar and GNSS-TEC data revealed that meridional plasma flow oscillations occurred in the mid-latitude trough and the auroral oval (increased TEC region). Additionally, the equatorward boundary of the auroral oval was located between magnetic latitudes of 72° and 74°. The 15-min detrended TEC measured over the Fort Simpson radar, inside the field-of-view of the PGR radar, showed oscillations similar to the ionospheric plasma flow variations. Through a spectral analysis of the detrended TEC and meridional plasma flow oscillations, we identified a phase difference of ∼135° (∼1.9 min) between them. This phase difference indicates that the upward and downward motion of the ionosphere, driven by an external electric field resulting from Alfvén waves propagating from the dusk-side magnetosphere, was responsible for the observed GNSS-TEC perturbation.
One of the main concerns for Ground-Based Augmentation Systems (GBAS) is the occurrence of anomalous ionospheric conditions. In particular, ionospheric scintillation poses a significant threat, as strong scintillation events can cause GNSS receivers to lose signal lock, thereby reducing the number of available satellites for position computation. This often results in a degradation of system continuity and availability. To address this issue, this work develops a methodology to predict GBAS availability at specific airports (e.g., Tenerife Norte Airport) using a DLR-developed dynamical model of Equatorial Plasma Bubbles (EPBs), which are typically associated with scintillation events. Due to the limited number of GNSS monitoring stations surrounding Tenerife Norte Airport—a consequence of its island location and the surrounding oceanic environment—EPBs and the associated scintillation effects are simulated. The proposed approach combines reduced-order EPB modeling with a phase-gradient screen technique to reproduce scintillation generated by steep electron density gradients at EPB boundaries. By integrating these simulations with GISM-based ambient scintillation estimates and a time-varying ionospheric background model, synthetic scintillation indices are derived and subsequently used to assess DFMC GBAS availability.
We examined the thermospheric wind and temperature response over the South American sector during the main and early recovery phases of the 10–11 May 2024 geomagnetic superstorm. Our coordinated set of Fabry-Perot Interferometer (FPI), satellite-derived, and radar observations were obtained from observatories located at El Leoncito (Argentina, 31.8°S, 69.3°W), Cachoeira Paulista (Brazil, 22.7°S, 45°W), Santarém (Brazil, 2.4°S, 54.7°W), and Jicamarca (Peru, 11.96°S, 76.86°W), as well as from the Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) 1 satellite. The observations reveal a prompt low-latitude response to the storm sudden commencement, characterized by strong upward and westward plasma drifts and nearly simultaneous westward neutral wind disturbances, followed by intense nighttime thermospheric perturbations with pronounced longitudinal and latitudinal variability. Zonal winds were initially weakly eastward over El Leoncito and strongly westward over Cachoeira Paulista before evolving to strongly westward and weakly eastward at early morning over each site respectively. Transhemispheric meridional wind surges of ∼100 m/s at equatorial latitudes coincided with large-scale traveling ionospheric disturbances, while El Leoncito and Cachoeira Paulista exhibited mild meridional disturbances. Thermospheric temperatures were significantly enhanced throughout the night, with superimposed impulsive enhancements. These features are consistent with wind-field reorganizations and vertical motions, suggesting adiabatic processes associated with traveling atmospheric disturbances (TADs). These results provide new observational constraints on the low-latitude thermospheric response during an extreme geomagnetic storm and highlight the importance of neutral dynamics in storm-time thermosphere–ionosphere coupling.
Space weather is a recognized source of risk to civil aviation through its effects on communication, navigation, surveillance, and radiation systems. Historically, aviation space weather research has achieved substantial progress in identifying the physical mechanisms and operational vulnerabilities associated with individual subsystems, specific technologies, and sensitive regions, which laid the physical foundation for modern operational standards. Growing evidence suggests that the impacts of space weather may extend beyond individual subsystems and manifest themselves in operational metrics such as flight delays, cancellations, safety risks, and economic losses. This paper summarizes recent key advances over the past decade with a particular emphasis on an emerging transition from traditional subsystem-oriented studies toward quantitative investigations of system-level aviation impacts. We further discuss the development of aviation space weather services and the increasing necessity to translate scientific knowledge into understandable indicators, quantifiable consequences, and actionable decision-support tools.
The deployment of deep learning models directly onboard satellites is emerging as a promising approach to real-time space-weather monitoring. However, applying this paradigm to solar flare forecasting introduces three challenges: limited computational resources, the susceptibility of onboard weight storage to radiation-induced multiple-bit upsets (MBUs), and variations in power availability across orbital phases. In this work, we propose N-Slim, a network slimming framework that addresses all three challenges through a novel N-balanced structured channel pruning method. N-Slim proceeds through three phases: baseline pretraining, sparsity learning, and iterative slimming. The core idea of N-balanced pruning is to partition each layer’s output channels into non-overlapping groups of width N and distribute retained channels approximately uniformly across groups, preventing retained channels from clustering together and limiting the number of active channels that a single MBU event can simultaneously corrupt. By applying this method across a geometric sequence of group sizes, N-Slim generates a portfolio of subnets spanning a range of channel pruning rates and inference costs, providing a set of candidate models that could, in principle, support power-adaptive inference during different orbital phases. N-Slim was evaluated on VGG, ResNet, and DenseNet using solar active-region magnetogram images from the Solar Dynamics Observatory (SDO). Experiments demonstrate that N-Slim achieves competitive or improved forecasting accuracy across a range of channel pruning rates, with substantial MBU robustness gains on ResNet and DenseNet and comparable robustness to standard pruning on VGG.
The nature of spacetime in quantum gravity remains a fundamental problem, rooted in the conflict between the background independence of general relativity and the fixed-background structure of quantum field theory. This paper proposes a concrete, emergentist framework in which classical continuous spacetime is not fundamental but arises as a low-energy effective description of an underlying quantum information structure. We construct this emergence explicitly, starting from a unitary fusion category as the “algebraic DNA”, realizing it via a string-net condensate, and employing an equivariant tensor renormalization group flow to reach a geometric fixed point. Within this derived geometry, we introduce two key macroscopic variables: the entropy current vector (ECV) sμ, which describes the flow of entanglement entropy in spacetime, and the cosmological enthalpy (CE) Hc, an effective thermodynamic potential that incorporates pressure-volume work. We show that the Einstein field equations emerge as conditions of entanglement equilibrium, with the effective gravitational constant determined by categorical data. Crucially, this framework is not merely a conceptual narrative; it yields concrete, falsifiable predictions. We provide an explicit blueprint for near-term quantum simulation experiments—using ultracold atoms or superconducting qubit arrays—to measure the predicted area-law entanglement, the topological entanglement entropy, and the emergent graviton mode, thereby transforming the hypothesis of spacetime emergence into an empirically testable scientific program.
The persistent discrepancy between the various measured values of the Hubble constant is one of the most interesting problems in modern cosmology. This problem, in which a discrepancy of approximately 5σ arises between measurements of the early and late universe, is known as the Hubble Tension. On the other hand, Fast Radio Bursts (FRBs) are high-energy, transient extragalactic phenomena that are sensitive to the Hubble constant through their dispersion measure and therefore offer an alternative approach to alleviating this tension. In this work, we compiled a database of 126 FRBs with confirmed host galaxies, classified into three types based on IllustrisTNG simulations. We used a Bayesian approach to estimate H0 across seven host models. Three models treat the host contribution as independent of z; three others treat it as dependent on z, reflecting the repeating nature of FRB, and apply only to the corresponding subpopulation. The seventh is applied to the entire FRB population, and each FRB is assigned the appropriate host model. The most reliable result was obtained for the model that considers the entire population, distinguishing the repeater type of each FRB. For this last case, we obtained H0=71.36−3.94+3.59 km s−1Mpc−1. Two models of the Milky Way were considered, and the YMW16 model consistently yielded higher H0 values than NE2001 model, ranging from +1.12 to +13.24 km s−1Mpc−1 across different host galaxy assumptions. The H0 values obtained for each repeater type are implausible, suggesting that further modeling may be needed for these subpopulations. Finally, we used a catalog of 500 synthetic FRBs that allowed us to generate a deviation of 2.6% from the value obtained with the observed data, but allowed us to decrease the statistical precision from 5.5% to 1.2%, thus demonstrating the great potential that FRBs have as an alternative method to alleviate the Hubble Tension.
We have developed deep learning algorithms to detect and classify pulsating auroras in video data captured by the Time History of Events and Macroscale Interactions during Substorms (THEMIS) All-Sky Imagers (ASIs). We label auroral data into four categories: “bad viewing condition”, “no aurora”, “other aurora” and “pulsating aurora”. In contrast to all prior studies centered on auroral image classification, our primary goal revolves around the classification of pulsating aurora. We introduce two distinct deep learning approaches: first, a convolutional neural network (CNN) model for single-frame classification combined with a smoothing algorithm; second, a hybrid model that combines a CNN with a recurrent neural network (RNN), meaning we are allowing temporal information to inform our classifications. Our models are trained on a large dataset comprised of 100,000 images classified by expert auroral observers. We tested our algorithms on a new dataset with 58 full-night videos and found real world accuracy values of 63.9% for the CNN method and 55.9% for the RNN method. An important outcome of this work is that we used our techniques to classify every one of the more than one billion images that comprise the THEMIS-ASI dataset and thereby produced the largest dataset of automatically classified pulsating aurora to date. Further, this work sets the stage for assimilation of auroral machine-learning outputs into geospace simulations.
Using synoptic magnetograms from the National Solar Observatory/Kitt Peak spanning 19 February 1975, to 25 September 2003, we investigate the phase relationships among different types of magnetic elements at low and high solar latitudes. At low latitudes, the magnetic fields of sunspots and ephemeral regions significantly lead the network magnetic fields by approximately half a solar cycle. At high latitudes, conversely, the network magnetic fields lag the intranetwork magnetic fields (horizontal components) by a comparable half-cycle interval, also with statistical significance. In addition, the low-latitude magnetic fields of sunspots and ephemeral regions significantly lead the high-latitude network magnetic fields by about half a solar cycle. We offer a plausible physical interpretation for these observed phase relationships. These systematic phase offsets suggest a common underlying transport mechanism connecting the equatorial and polar magnetic fields.
A high resolution two-dimensional multi-fluid model of sporadic-E layers was developed and driven with physically realistic mesosphere, lower thermosphere (MLT) winds measured over Albuquerque, New Mexico. The realistic E-region winds are produced by the HYdrodynamic Point-wise Environment Reconstructor (HYPER) model that ingests meteor derived wind observations from a Spread-spectrum Interferometric Multistatic meteor radar Observing Network (SIMONe) system combined with the Navier-Stokes equations to provide high resolution three-dimensional wind fields over time. Sporadic-E dynamics are simulated using both realistic winds from HYPER as well as idealized hyperbolic tangent windshears to compare and contrast. Overall, the model shows greater inhomogeneity and irregularity using realistic winds with no clear peaks in the spectra, unlike the periodic density structures from the idealized windshears. Furthermore, range-time-frequency (RTF) observations from a local ionosonde were used to compare sporadic-E observations with the simulations. In general, the simulations show Kelvin-Helmholtz billow formation during the periods with range-spread sporadic-E in ionosonde observations, but the ionosonde virtual heights are 5–10 km above the simulated peak densities, likely due to altitude limitations from meteor radar observations. Ultimately, the use of realistic winds to drive sporadic-E models provides more insight to study complex dynamics and decipher observations of turbulent layers.
Accurate prediction of stratospheric wind and temperature profiles is essential for understanding regional atmospheric dynamics over the complex terrain of the Tibetan Plateau. However, conventional numerical weather prediction models are computationally expensive, while statistical time-series models are limited in capturing the nonlinear evolution of atmospheric variables across both temporal and vertical dimensions. To address these limitations, MTPV-HDRNet is proposed as a fixed-site, multi-level intelligent forecasting model driven by ECMWF ERA5 pressure-level reanalysis data, with the fixed site defined as a selected ERA5 grid cell rather than an observational station. MTPV-HDRNet jointly predicts zonal wind (U), meridional wind (V), and temperature (T) for the next 24 h across 11 ERA5 pressure levels from 100 to 1 hPa, which correspond to heights of approximately 16–48 km. The model adopts a hybrid encoder–decoder architecture that explicitly represents temporal evolution and vertical stratification in a decoupled but complementary manner, thereby enhancing its ability to capture multi-lead-time profile evolution and cross-level dependencies. The model was trained using ERA5 data from 2016 to 2022, validated using data from 2023, and independently tested using data from 2024 at the fixed site in the central Tibetan Plateau. Results demonstrate that MTPV-HDRNet consistently outperforms the baseline models at all forecast lead times. On the 2024 test set, the mean RMSEs are 3.48 m s−1 for U wind, 3.54 m s−1 for V wind, and 1.62 K for temperature. Compared with the strongest baseline, ConvLSTM, MTPV-HDRNet reduces the RMSE by 11.9%, 11.3%, and 7.4% for U wind, V wind, and temperature, respectively. The model also maintains strong correlation performance at longer lead times, demonstrating its robustness in fixed-site atmospheric profile forecasting over complex terrain.
The predominant fusion chain in stellar nucleosynthesis produces positron and neutrino byproducts in equal amounts. An extremely high neutrino flux can be directly measured, but the unobservable, yet equally high flux of positrons is traditionally considered to be short-lived antimatter that annihilates upon contact with electrons. The electrons are traditionally assumed to be populated in such excess that their annihilation with positrons could be sustained indefinitely. This paper posits that, considering the extremely high positron and neutrino flux within a stellar core, there is an opportunity for localized positron populations and accumulation. As there are no existing methods to directly observe a positron accumulation within the solar core, evidence must rely largely on observations of the surface and above. The difficulties of direct observation into internal stellar processes introduces a chance to overlook critical components such as a theoretical positron accumulation, thus exacerbating unresolved stellar questions surrounding observed coronal signatures and high-energy emission channels. This investigation examines the potential for stellar positron accumulation, its consequences for core dynamics, and observational signatures that could support the existence of long-lived positrons. The framework is applied to problems in stellar and galactic physics, solar weather forecasting, and high-energy-density plasma systems.
Explanations of the mechanism governing plasmasphere corotation typically begin from the principle of a frozen-in magnetic field, whereby magnetic field lines drive the plasma to corotate synchronously with the Earth. However, this theory is a phenomenological approximation that is not based on first principles. In this study, based on particle dynamics, we propose another possible explanation that ionospheric electric fields map into the plasmasphere, driving E⇀×B⇀ drift of the plasma. We first perform a rigorous mathematical demonstration of the corotation mechanism controlling the plasma on the equatorial plane according to the principle of electric field mapping. We also briefly and qualitatively explain why is the effect of ionospheric neutral winds on plasmasphere corotation not proportional to its wind speed. Second, we calculate the three-dimensional instantaneous E⇀×B⇀ drift of the plasma on the equatorial plane considering the tilt of the magnetic axis, which generates a vortex electric field in the magnetosphere. Therefore, this vortex electric field will have a certain degree of influence on the overall motion of the plasmasphere. Finally, we qualitatively discuss that the tilt of the magnetic axis does not affect the spatial symmetry of polarization charge density in the plasmasphere; thus, the polarization charge density is always symmetric with respect to Earth’s rotational axis rather than Earth’s magnetic axis (only the corotation electric field is taken into account, disregarding complex boundary conditions and external factors like the solar wind). However, this is not a sufficient electrodynamic argument, so still requires further discussion.
The 10 May 2024 geomagnetic storm was one of the most intense events that impacted Earth’s upper atmosphere in the last 30 years, producing major disturbances in the coupled ionosphere-thermosphere (I-T) system. This work investigates the large-scale ionospheric storm effects at mid-latitudes during this event, using a combination of satellite and ground-based observations as well as GITM simulations. In-situ ion density observations from DMSP F16, F17, and F18 are used to identify the onsets and intensities of the positive and negative ionospheric storm phases across four latitude–local time sectors. The storm-time variations in ion density, drift, and temperature measurements from DMSP show the topside ionospheric dynamics of each storm phase. During the positive phase, DMSP dusk-side passes recorded ion density enhancements of up to 4–5 times at northern and 17 times at southern mid-latitudes. This was followed by a strong negative phase, with ion density depletion up to 50% below quiet-time levels at northern mid-latitudes. Comparison of DMSP ion density and drift with GRACE-FO neutral density and wind data shows the role of enhanced ion-neutral coupling in driving the positive phase. The perturbation Poynting flux derived from DMSP measurements and the altitude-integrated Joule heating from GITM show consistent latitudinal extent down to 40°–45° MLAT, identifying intense auroral heating as the underlying driver of the I-T expansion. The drivers of the negative phase are identified using the vertically integrated O/N2 ratio from DMSP-SSUSI FUV measurements and NO emission data from TIMED-SABER, which show that increased recombination from O/N2 depletion and the NO overcooling effect drive the negative phase and produce its hemispheric asymmetry.
