We develop a perturbative framework with which to discuss departures from exact Lorentz invariance and explore their potentially observable ramifications. Tiny noninvariant terms introduced into the standard model Lagrangian are assumed to be renormalizable (dimension \ensuremath{\leqslant}4), invariant under $\mathrm{SU}(3)\ensuremath{\bigotimes}\mathrm{SU}(2)\ensuremath{\bigotimes}U(1)$ gauge transformations, and rotationally and translationally invariant in a preferred frame. There are a total of 46 independent CPT-even perturbations of this kind, all of which preserve anomaly cancellation. They define the energy-momentum eigenstates and their maximal attainable velocities in the high-energy limit. The effects of these perturbations increase rapidly with energy in the preferred frame, more rapidly than those of CPT-odd perturbations. Our analysis of Lorentz-violating kinematics reveals several striking new phenomena that are relevant both to cosmic-ray physics (e.g., by undoing the Greisen, Zatsepin, and Kuz'min cutoff) and neutrino physics (e.g., by generating novel types of neutrino oscillations). These may lead to new and sensitive high-energy tests of special relativity.
Dark energy appears to be the dominant component of the physical Universe, yet there is no persuasive theoretical explanation for its existence or magnitude. The acceleration of the Universe is, along with dark matter, the observed phenomenon that most directly demonstrates that our theories of fundamental particles and gravity are either incorrect or incomplete. Most experts believe that nothing short of a revolution in our understanding of fundamental physics will be required to achieve a full understanding of the cosmic acceleration. For these reasons, the nature of dark energy ranks among the very most compelling of all outstanding problems in physical science. These circumstances demand an ambitious observational program to determine the dark energy properties as well as possible.
Argha Ghosh, Arnab Mandal, Bishakha Priyadarshini
et al.
Abstract Semi-arid regions are ecologically fragile systems where vegetation dynamics are strongly governed by hydro-meteorological variability. Western Odisha, typifies this climatic sensitivity, with recurrent droughts, erratic rainfall, and high evapotranspiration challenging sustainable land use and agriculture. This study assessed long-term (2001 to 2024) trends of hydro-meteorological and biophysical parameters including precipitation, maximum and minimum air temperature, land surface temperature (LST), actual evapotranspiration (AET), and Normalized Difference Vegetation Index (NDVI) across eight districts of semi-arid Western Odisha using remote sensing datasets and non-parametric trend analysis. Results revealed significant seasonal and spatial heterogeneity. AET exhibited statistically significant declined across most districts during winter and pre-monsoon months, with maximum reductions occurring in April. LST demonstrated widespread cooling during February- May, with rates between − 0.15 and − 0.27 °C year⁻¹ in several districts, while monsoon months showed near-neutral or weakly positive changes. Maximum air temperature displayed strong pre-monsoon warming (0.15–0.22 °C year⁻¹), whereas minimum temperature increased primarily during winter nights (0.05–0.10 °C year⁻¹), indicating intensifying nocturnal warming. NDVI displayed a general greening tendency in pre-monsoon, linked to agricultural intensification, despite declining monsoon precipitation across all districts. LST was highly correlated with maximum air temperature (0.91) and negatively with NDVI (− 0.63). Rainfall exhibited a moderate positive relationship with AET (0.69). The findings highlight a complex climatic transition wherein surface cooling coexists with atmospheric warming, night-time warming, and rainfall reduction. These shifts have direct implications for agricultural water demand, vegetation resilience, and regional climate adaptation planning.
This paper systematically constructs a mathematical physics grand unified theoretical framework based on the holistic view of the universe. The framework is centered on six fundamental axioms: the Cosmic Holism Axiom, the Vacuum Duality Axiom, the Entanglement-Geometry-Medium Equivalence Axiom, the Algebraic Hierarchy Correspondence Axiom, the Cosmic-Scale Zero-Point Fluctuation Axiom, and the Dual Universe Generation Axiom. Within this framework, the fundamental commutation relations of quantum mechanics are generalized into three pairs of dual operators: time-energy, space-momentum, and angle-angular momentum, forming the basis for the Quantum Relativistic STEM unified equations. The imaginary unit is endowed with physical reality, corresponding to concepts such as imaginary time, imaginary space, imaginary mass, and imaginary energy, rigorously proven in Theorem 3.4. The four fundamental interactions are unified under the algebraic hierarchy of real numbers, complex numbers, and quaternions: gravity corresponds to the real unit 1 (like masses attract), electromagnetism corresponds to the complex unit \(i\) (like charges repel), the strong interaction corresponds to the quaternion imaginary units \(I, J, K\) (color confinement), and the weak interaction emerges as a relativistic effect of color charge interactions. The Left-Handed Multiplication Rule is rigorously formalized in Definition 4.2, and its equivalence to the chirality of the weak interaction is proven in Theorem 4.2. From this, the weak mixing angle in the pure color charge limit is rigorously derived to be \(30^\circ\) , with the electromagnetic correction yielding the precise expression \(\theta_W = 30^\circ \times \sqrt{1 - \sqrt{\alpha}} + \mathcal{O}(\alpha)\), which aligns remarkably with the experimental value of \(28.74^\circ\).
The difference in the proton and neutron separation energies ($\Delta S$) of the weakly bound $^{15}$C ground state is -19.86 MeV, an extreme value. Data from intermediate-energy heavy-ion induced (HI-induced) knockout reactions on nuclei spanning $-20\lesssim\Delta S\lesssim+20$ MeV, suggest that the degree to which single-particle strength is quenched, $R\mathrm{_{s}}$, has a negative correlation with $\Delta S$, decreasing from unity around $-20$~MeV to around 0.2 at $+20$~MeV. For the $^{15}$C ground state ($R_s=0.96(4)$ in HI-induced knockout), contrasting results have recently been obtained via the neutron-adding transfer reaction, which reveal a value of $R_s=0.64(15)$, similar to the value observed at modest $\Delta S$ and more extreme values of $\Delta S$ with reaction probes other than HI knockout. In order to explore the any potential differences between $adding$ and $removing$ processes in transfer reactions at extreme $\Delta S$, single-neutron removal transfer reactions on $^{15}$C were performed at 7.1MeV/u in inverse kinematics. The removal of a valence neutron in 2$s_{1/2}$ orbit using both ($p$,$d$) and ($d$,$t$) reactions shows consistent quenching factors and agrees with those from the neutron-adding reaction. The present results, which can be compared with neutron knockout reaction, suggest that correlations, represented by the quenching factor, show limited dependence on neutron-proton asymmetry under the most extreme asymmetry conditions so far achieved in transfer reactions.
Abstract The future tropical sea surface temperature (SST) changes profoundly impact global and regional climate. Under greenhouse warming, the reduction of Antarctic sea ice concentration (SIC) acts as an extratropical energy perturbation, exerting a substantial influence on the spatial distribution of tropical SST change. This study reveals a strong correlation between the current Antarctic SIC and tropical SST change, especially the interhemispheric asymmetry and El Niño‐like pattern under greenhouse warming among CMIP6 models. Considering the commonly underestimated Antarctic SIC in CMIP6 models, this study applies an emergent constraint on the projected tropical SST response to greenhouse warming using the observed Antarctic SIC. The interhemispheric asymmetry in projected tropical SST warming can be markedly diminished in the multi‐model ensemble mean, with a 30% reduction in the intermodel uncertainty. The spatial constraints on the projected tropical Pacific SST change produce a more pronounced and westward‐extended El Niño‐like warming pattern.
Extracting rampart crater ejecta blankets is crucial for understanding impact crater formation and material transport processes, offering key insights into the distribution of subsurface water and ice on Mars. However, traditional methods often fail to extract rampart crater ejecta blankets due to complex terrain, noise interference, and blurred boundaries. To overcome these challenges, we propose an edge-aware segment anything model (SAM) sputter analysis (EASSA) framework for the rapid and accurate extraction of rampart crater ejecta contours. EASSA comprises three key components. First, Wiener filtering and multiscale Retinex preprocessing are applied to suppress terrain noise and enhance the visual distinction between rampart features and the background. Second, an SAM-based unsupervised segmentation module is employed to automatically identify rampart crater boundaries. Finally, we refined the extracted edges by designing a contour optimization pipeline that applies classical image operators, such as Sobel to enhance gradients, Suzuki–Abe to correct contours, and Douglas–Peucker to simplify and smooth shapes. To validate EASSA, we construct a multiscale rampart crater dataset using context camera (targeting craters <1 km) and Thermal Emission Imaging System imagery (for craters >1 km) in the Chryse Planitia and Arabia Terra regions. Experimental results demonstrate that our method achieves a detection accuracy of 97.36%, a recall of 93.36%, and an intersection over union of 0.93, significantly outperforming the baseline SAM segmentation. Morphological analysis further reveals that rampart ejecta in both regions exhibit mobilities greater than 2, an average lobateness coefficient of 1.06, and relatively shallow excavation depths. Additionally, we observe that elevated terrains exhibit lower ejecta flow mobility under similar latitudinal conditions, while geomorphic evidence of past fluvial activity remains evident. These findings provide new insights into Martian subsurface water dynamics and the mobility characteristics of ejecta under varied geologic settings.
Giovanni Anconitano, Elena Arabini, Alessandro Patacchini
et al.
The new Copernicus Radar Observing System for Europe in L-band (ROSE-L), expected to work in synergy with the C-band Sentinel-1 mission, will create a multiplatform Synthetic Aperture Radar (SAR) facility acquiring data in a systematic and coordinated way. This paper investigates the performance of a novel soil moisture retrieval scheme, extending the capability of a previously proposed multitemporal and multipolarization algorithm to the case of multifrequency SAR data. It relies on a Bayesian statistical criterion to invert a forward electromagnetic model based on the hypothesis that soil moisture can change abruptly, whereas soil roughness remains stable over time. The algorithm is applied to simulated data to compare two possible operational scenarios of ROSE-L and Sentinel-1 observations: L-band and C-band coincident (LC) or alternate (L-C) acquisitions. The case of single frequency (L or C) data is also considered in the analysis. In addition, quad-polarization (VV, VH, HH) and dual-polarization (VV, VH) data for ROSE-L are compared when combined with dual-polarization (VV, VH) data for Sentinel-1. The simulated multipolarization C-band and L-band SAR data are generated considering time variant scenarios of bare soil and crop covered fields. The algorithm is also tested on a time-series of non-coincident L-band SAOCOM-1A and C-band Sentinel-1A data to evaluate the improvements of the soil moisture retrieval against in-situ data when the two frequencies are merged in the multitemporal scheme. For the simulated case, results for bare soils show that the alternate configuration reaches a retrieval accuracy higher than that of single frequency, with an average percentage improvement in RMSE of approximately 18% compared to single C-band and 5% compared to single L-band. In many cases, it approaches the performance of the coincident acquisitions, maintaining a key advantage in terms of revisit time. The experiment on real data further confirms the advantage of alternating the acquisitions from the two frequency bands when exploited within a multitemporal framework.
Javier Torres-Quintero, Paul Goyes-Peñafiel, Ana Mantilla-Dulcey
et al.
Seismic data preconditioning is essential for subsurface interpretation. It enhances signal quality while attenuating noise, improving the accuracy of geophysical tasks that would otherwise be biased by noise. Although classical poststack seismic data enhancement methods can effectively reduce noise, they rely on predefined statistical distributions, which often fail to capture the complexity of seismic noise. On the other hand, deep learning methods offer an alternative but require large and diverse data sets. Typically, static databases are used for training, introducing domain bias, and limiting adaptability to new noise poststack patterns. This work proposes a novel two-process dynamic training method to overcome these limitations. Our method uses a dynamic database that continuously generates clean and noisy patches during training to guide the learning of a supervised enhancement network. This dynamic-guided learning workflow significantly improves generalization by introducing variability into the training data. In addition, we employ a domain adaptation via a neural style transfer strategy to address the potential challenge of encountering unknown noise domains caused by specific geological configurations. Experimental results demonstrate that our method outperforms state-of-the-art solutions on both synthetic and field data, within and outside the training domain, eliminating reliance on known statistical distributions and enhancing adaptability across diverse data sets of poststack data.
Antonio Capanema, Pasquale Blasi, Emanuele Sobacchi
Over the past decades, there has been growing observational and theoretical evidence that cosmic-ray-induced instabilities play an important role in both acceleration and transport of cosmic rays (CRs). For instance, the efficient acceleration of charged particles at supernova remnant shocks requires rapidly growing instabilities, so much so that none of the proposed processes seem sufficient to warrant acceleration to PeV energies. In this work, we investigate whether an acoustic instability triggered by the presence of a CR pressure gradient can lead to significant self-confinement of charged particles in the vicinity of shocks. We validate the expected growth rates and obtain the scale and energy of magnetic field perturbations induced by such system using magnetohydrodynamical simulations. Our results suggest a strong suppression of the diffusion coefficient for particles with Larmor radius around a thousandth of the precursor scale length.
This study enhances the application of Physics-Informed Neural Networks (PINNs) for modeling discontinuous solutions in both hydrodynamics and relativistic hydrodynamics. Conventional PINNs, trained with partial differential equation residuals, frequently face convergence issues and lower accuracy near discontinuities. To address these issues, we build on the recently proposed locally linearized PINNs (LLPINNs), which improve shock detection by modifying the Jacobian matrix resulting from the linearization of the equations, only in regions where the velocity field exhibits strong compression. However, the original LLPINN framework required a priori knowledge of shock velocities, limiting its practical utility. We present a generalized LLPINN method that dynamically computes shock speeds using neighboring states and applies jump conditions through entropy constraints. Additionally, we introduce locally Roe PINNs (LRPINNs), which incorporate an approximate Roe Riemann solver to improve shock resolution and conservation properties across discontinuities. These methods are adapted to two-dimensional Riemann problems by using a divergence-based shock detection combined with dimensional splitting, delivering precise solutions. Compared to a high-order weighted essentially non-oscillatory solver, our method produces sharper shock transitions but smoother solutions in areas with small-scale vortex structures. Future research will aim to improve the resolution of these small-scale features without compromising the model's ability to accurately capture shocks.
For more than half a century, dualities have been at the heart of modern physics. From quantum mechanics to statistical mechanics, condensed matter physics, quantum field theory and quantum gravity, dualities have proven useful in solving problems that are otherwise quite intractable. Being surprising and unexpected, dualities have been taken to raise philosophical questions about the nature and formulation of scientific theories, scientific realism, emergence, symmetries, explanation, understanding, and theory construction. This book discusses what dualities are, gives a selection of examples, explores the themes and roles that make dualities interesting, and highlights their most salient types. It aims to be an entry point into discussions of dualities in both physics and philosophy. The philosophical discussion emphasises three main topics: whether duals are theoretically equivalent, the view of scientific theories that is suggested by dualities (namely, a geometric view of theories), and the compatibility between duality and emergence.
The surface array of the IceCube Neutrino Observatory, IceTop, measures cosmic rays in the PeV-EeV primary energy range. Stations comprising radio antennas and scintillation detectors will be added to enhance the existing surface detectors. A prototype station, consisting of eight scintillation detectors and three radio antennas, has been in operation in with the instrumentation in final design since the beginning of 2023. Radio signals from air showers are measured by antennas that are read-out when the trigger condition from the scintillation detectors is met. This contribution reports on air-shower coincidence measurements of these radio antennas with IceTop. Geometric shower parameters reconstructed from the radio antennas are compared with those from IceTop to determine the angular resolution. We also present details on the two new stations that were tested, deployed and commissioned with their respective data acquisition systems during the latest field season at the South Pole.
<p>Due to a large number of automatic auroral camera systems on the ground, image data analysis requires more efficiency than what human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by an unsupervised learning method on an image set that contains both nightside and dayside aurora. We used 6 months of full-colour auroral all-sky images captured at a high-Arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input into a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological clusters. In the clustering of auroral image data with two different time resolutions, we found that the occurrence of 8 clusters strongly increased when the image cadence was high (24 s), while the occurrence of 14 clusters experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of a group of eight “active aurora” clusters. Time periods for which this active aurora persisted for longer than two consecutive images with a maximum cadence of 6 min coincided with ground-magnetic deflections, and their occurrence was found to maximize around magnetic midnight. The active aurora onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image clustering method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.</p>
<p>One of the most widely used approaches for measuring the earth's subsurface resistivity is the transient electromagnetic (TEM) method. However, noise and interference from different sources, e.g., radio communication, the instrument, the atmosphere, and power lines, severely taint these types of signals. In particular, radio transmission in the very low-frequency (VLF) range between 3 and 30 kHz is one of the most prominent sources of noise. Transient electromagnetic signals are normally gated to increase the signal-to-noise ratio. A precise selection of gate shapes is required to suppress undesired noise while allowing the TEM signal to pass unaltered. We employ the multi-objective particle swarm optimization technique to choose optimal gate shapes and placements by minimizing an objective function composed of standard error bars, the covariance between gates, and the distortion of the gated signal. The proposed method is applied to both fully sampled synthetic TEM data and to boxcar-gated field data. The best output from the search space of gate shapes was found to be a hybrid combination of boxcar and Hamming gates. The effectiveness of hybrid gating over traditional boxcar and semi-tapered gating is confirmed by an analysis of covariance matrices and error bars. The results show that the developed method effectively suppresses VLF noise in the middle gates, which are gates with center times spanning 30 to 200 <span class="inline-formula">µ</span>s , and in the late gates, which are gates with center times spanning 200 to 1130 <span class="inline-formula">µ</span>s. The analysis shows that the average improvement in standard errors obtained for the hybrid gating scheme over boxcar gating is 1.719 and 1.717 for synthetic and field data, respectively.</p>
Imaging methods based on the absorption or scattering of atmospheric muons, collectively named under the neologism "muography", exploit the abundant natural flux of muons produced from cosmic-ray interactions in the atmosphere. Recent years have seen a steep rise in the development of muography methods in a variety of innovative multidisciplinary approaches to study the interior of natural or man-made structures, establishing synergies between usually disconnected academic disciplines such as particle physics, geology, and archaeology. Muography also bears promise of immediate societal impact through geotechnical investigations, nuclear waste surveys, homeland security, and natural hazard monitoring. Our aim is to provide an introduction to this vibrant research area, starting from the physical principles at the basis of the methods and reviewing several recent developments in the application of muography methods to specific use cases, without any pretence of exhaustiveness. We then describe the main detector technologies and imaging methods, including their combination with conventional techniques from other disciplines, where appropriate. Finally, we discuss critically some outstanding issues that affect a broad variety of applications, and the current state of the art in addressing them.
Recently, the IceCube collaboration observed a neutrino excess in the direction of NGC 1068 with high statistical significance. This constitutes the second detection of an astrophysical neutrino point source after the discovery of a variable emission originating from the blazar TXS 0506+056. Neutrinos emitted by these sources traverse huge, well-determined distances on their way to Earth. This makes them a promising tool to test new physics in the neutrino sector. We consider secret interactions with the cosmic neutrino background and discuss their impact on the flux of neutrino point sources. The observation of emission from NGC 1068 and TXS 0506+056 can then be used to put limits on the strength of the interaction. We find that our ignorance of the absolute neutrino masses has a strong impact and, therefore, we present limits in two benchmark scenarios with the sum of the neutrino masses around their lower and upper limits.
Abstract Interactions between whistler mode chorus waves and electrons are a dominant mechanism for particle acceleration and loss in the outer radiation belt. One form of this loss is electron microburst precipitation: a sub‐second intense burst of electrons. Despite previous investigations, details regarding the microburst‐chorus scattering mechanism—such as dominant resonance harmonic—are largely unconstrained. One way to observationally probe this is via the time‐of‐flight energy dispersion. If a single cyclotron resonance is dominant, then higher energy electrons will resonate at higher magnetic latitudes: sometimes resulting in an inverse time‐of‐flight dispersion with lower‐energy electrons leading. Here we present a clear example of this phenomena, observed by a FIREBIRD‐II CubeSat on 27 August 2015, that shows good agreement with the Miyoshi‐Saito time‐of‐flight model. When constrained by this observation, the Miyoshi‐Saito model predicts that a relatively narrowband chorus wave with a ∼0.2 of the equatorial electron gyrofrequency scattered the microburst.
Abstract The three leading modes of the North Atlantic atmospheric circulation explain about 70% of the winter climate variability. Although climate models generally can capture these modes, biases may induce large uncertainties in regional climate predictions. Here, we evaluate the leading winter modes simulated by CMIP5‐PMIP3 and CMIP6‐PMIP4 models from the last millennium to future scenarios in comparison with historical reanalysis and paleo‐reconstructions. The models generally have a good representation of the average spatial pattern of the North Atlantic Oscillation (NAO) while showing a larger spread in performance for the East Atlantic and Scandinavian patterns. In contrast to historical reanalysis, the simulated NAO pattern tends to be rather stationary under various climate states over the years 861–2100. Such underestimated spatial variability in the simulated NAO is directly related to the biased spatial shifts in NAO‐related regional temperature and precipitation changes, inducing uncertainties in climate projections over the North Atlantic sector.