Computational Physics · Cross-listed from cond-mat.str-el
Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
Ali Rayat, Yunhao Fan and Gia-Wei Chern
Metallic magnets exhibit complex
spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically require repeated solutions of an underlying electronic problem throughout the time
evolution, creating a major computational bottleneck. Here we introduce a graph neural network (GNN)
magnetic force-field framework that learns the effective
magnetic energy functional governing itinerant
spin dynamics directly from electronic calculations. Conceptually analogous to machine-learned interatomic potentials, the proposed framework enables efficient evaluation of
spin torques while capturing the nonlinear and spatially extended interactions generated by itinerant electrons. We
benchmark the method on representative metallic
magnetic systems exhibiting collinear, noncollinear, and noncoplanar
magnetic order. The learned force fields accurately reproduce electronically generated
spin torques and yield nonequilibrium
spin dynamics in excellent agreement with direct electronic simulations. Our results establish graph neural networks as a powerful framework for machine-learned
magnetic force fields, providing a pathway toward predictive large-scale simulations of nonequilibrium magnetism across multiple length and time scales.
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Geophysics
Reliability-calibrated deep residual full-waveform inversion using geometry-invariant physics encoding: synthetic validation and zero-shot Marmousi-2 testing
Deepak Kumar, Jayant Nath Tripathi and Laxmidhar Behera
Neural networks that map seismic data directly to velocity models tend to memorize the acquisition geometry they were trained on, and their uncertainty estimates are rarely trustworthy once the data drift away from the training distribution. We describe a laptop-scale pipeline that addresses both problems. The network never sees shot gathers. Instead, variable acquisition geometries are mapped into a fixed model-space representation computed by classical physics operators: the starting model, a regularized classical inversion, two misfit-gradient images, and six illumination and wavenumber-coverage maps from fast-marching traveltimes. The learned component is not a replacement for FWI; it is a calibrated residual corrector applied to this physics-derived prior. A six-member heterogeneous ensemble with per-pixel variance heads is calibrated by physics-conditioned conformal prediction, giving finite-sample pixel-wise marginal coverage on the calibration distribution. Beyond that distribution, a held-out-shot physics audit simulates shots the inversion never used through samples of the predictive distribution and rescales interval width where their data-space coverage peaks; no ground truth is involved. On a corpus of 1000 synthetic models spanning six acquisition families, the ensemble reduces error by 38% relative to its classical prior and transfers to never-seen geometries without measurable degradation. Applied zero-shot to the full 17 km Marmousi-2 line, it lowers the error from 354 to 304 m/s while raw coverage collapses to 0.42; the audit restores coverage to 0.89-0.91 across eleven corruption conditions covering noise, wavelet error, and shot decimation, and its peak height cleanly separates physics mismatch from benign corruptions. Budget-matched gather-based baselines underperform substantially off their training acquisition. Code and checkpoints will be archived on Zenodo.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Hubbard physics with ultracold polar molecules: on-site interaction energies for shielded molecules
Carlin Stewart-Wiese, Joy Dutta and Jeremy M. Hutson
We explore on-site interaction energies $U$ for pairs of shielded ultracold molecules on the same
lattice site. We use 2-dimensional effective potentials appropriate for microwave shielding, which have dipole-dipole character at long range but feature a very large repulsive core when the two molecules come close together. This causes very strong correlation between the motions of two molecules on the same site. We find behavior very different from that for ultracold atoms: in particular, there are states for which $U$ is negative for weak
lattices but crosses zero to positive values as the
lattice strength increases. This behavior is found for both unbound pairs and 2-body bound states. The latter will give access to previously unexplored types of strongly dipolar Hubbard physics with multiple site occupancy.
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Plasma Physics · Cross-listed from astro-ph.SR
Recovering Electron-Distribution Information from the Quiet-Sun Temperature Discrepancy
Victor Edmonds
Temperature diagnostics compress an electron distribution into a scalar. If two diagnostics weight different velocity ranges, their disagreement can retain information either discards. We develop this measurement for the quiet Sun, where
radio brightness and scale-height/ionization diagnostics read about 0.6 and 1.5 MK, a ratio of $2.4 \pm 0.3$ stable across eight years. For specified projections, an exact relative-
entropy identity partitions the discrepancy: the ratio fixes a family-independent temperature component; residual shape requires a family. Under the $\kappa$ family and stated projection assignments, the ratio gives $\kappa \approx 2.5$ and a free-energy equivalent of 10–20% of the electron thermal energy. An independent EIS within-ion Fe IX test is consistent with $\kappa = 2.5$–3, not confirmed; its confirm condition fired under neither calibration treatment. Under narrow-DEM conditioning, the Maxwellian residual is 2.8 times the conservative systematic floor. A published broad Maxwellian DEM restores spectroscopic consistency, but its material gives a class-level
radio-to-EUV cap of 1.21 against the measured class value $2.4 \pm 0.3$. Across all stated treatments, the Maxwellian fails at least one constraint in this class-level joint comparison; the records are neither co-temporal nor co-spatial. Conditional tests, not further evidence, find that the local Coulomb/runaway channel falls 39–56 times short and that a 1.8–3.5 keV stopping-column scale overlaps the inferred 1.7–3 keV sharp-edge bracket. Termination there remains a working hypothesis. The central result is the measurement construction: information lost to either temperature alone becomes recoverable from their disagreement. Direct shape confirmation requires a cross-class or distribution-resolving measurement.
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History and Philosophy of Physics
De mora luminis: Roemer's discovery 350 years later
Fabio Falchi, Riccardo Furgoni, Paolo Gattillo et al. (+1 more)
350 years from the 1676 announcement of the Roemer's discovery that light propagates with finite speed, we present our observations using eyes with
telescopes having similar resolution compared to those in the late 17th century. We confirmed that Roemer's method is valid and gives reasonable values for the speed of light c, within about 10 per cent of the modern value for our measurements, even with the simplest modelling technique, using uniform circular motions. We found that increasing the complexity of the model, e.g., by taking into account the elliptical orbit of Jupiter, does not necessarily bring the results closer to the value of c due to the influence of other perturbations. Using modern ephemerides yields a noticeably accurate result of c=(298200+-1900) km/s. This experience can have great didactic value by showing the interconnections between formulation of hypotheses and the consequent predictions, making observations, reducing data, and searching for alternative explanations for the same phenomenon. Lastly, we also found, in the correspondence between Roemer and Huygens, that Roemer in 1677 searched for an independent confirmation of what he found during previous years observing Io's eclipses by making observations and reducing the data of the meridian transits of the Great Red Spot on Jupiter.
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All authors Fabio Falchi, Riccardo Furgoni, Paolo Gattillo, Maurizio Francesio
Physics and Society
Predictability of Human Movements across Industry Sectors using Multilayer Networks
Maisha Islam Sejunti, Melissa Butler, Yingjie Hu et al. (+1 more)
Predicting the patterns of human movement is important to applications from urban design to disease control and emergency response. In this work, we leverage machine learning (ML) to study the predictability of movements to different types of locations (e.g., movements to grocery stores, to schools, or to hospitals). Our approach relies on developing multilayer mobility networks encoding movements between census tracts with network layers associated with different industry sectors. We study the predictability of node degrees across layers, studying which industries are easier/harder to predict. Rather than treat prediction as an end in itself, we propose ML predictability as an exploratory tool to understand multilayer network organization. Our results suggest that node's out-degrees are easy to predict, since they are driven by census tract populations; whereas in-degrees are harder to predict, since persons can be drawn to diverse type of locations in a census tract. We also find that movements to certain industry types (e.g., restaurants) are easier to predict than others, and the different industrie's predictability are positively correlated with the number of movements to those types of locations. These findings provide a practical step toward using ML for multilayer mobility modeling and the many downstream application.
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All authors Maisha Islam Sejunti, Melissa Butler, Yingjie Hu, Dane Taylor
Applied Physics
Matching Trace Element Distribution to Mineralogical Phases in Ancient Biotechnology-Derived Metallic Salts: a Multimodal Analysis
Sumera Rehman, Spyridon Panagiotopoulos, Georgios E Christidis et al. (+4 more)
Conventional X-ray fluorescence (XRF) and X-ray diffraction (XRD) analysis applied to the investigation of ancient metal salts used as pigments and/or therapeutics provide bulk average compositions in two stand-alone data sets; however, major elements aside, these two sets cannot inform on the spatial distribution of one with respect to the other. To address this issue, we present here a
multimodal approach incorporating spatially resolved XRF, XRD and nanoscale X-ray imaging applied to the analysis of archaeological and experimental samples of synthetic lead carbonate (PbCO3 - Greek psimythion); psimythion was used in antiquity as a cosmetic and/or a therapeutic for external applications. The experimental sample was produced according to a well-documented recipe dated to the 4th century BCE. In this paper we demonstrate that by using a
multimodal approach we can confidently assign trace elements to individual crystalline or to infer the existence of non-crystalline phases. Although the assignment of an element to a phase (i.e. the location) is now possible, the origin underlying it (i.e. the mechanism) is not always clear. Trace elements do not 'control' chemical/mineralogical composition, but they can influence it. Our approach is particularly suited to following changes in the artefact's chemical/mineralogical profile, from its manufacture, to use and burial, to excavation and conservation.
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All authors Sumera Rehman, Spyridon Panagiotopoulos, Georgios E Christidis, Athena Athanassiadou, Alessandro Olivo, Effie Photos-Jones, Silvia Cipiccia
Optics
Large-Aperture All-Solid-State Cascaded Liquid-Crystal Beam Steering for High-Resolution Wide-Field Imaging
Chenxi Liu, Yongxiang Qu, Xiaoxin Wang et al. (+10 more)
High-resolution wide-field imaging is essential for applications requiring simultaneous global coverage and local detail, yet conventional approaches face a fundamental trade-off: wide-FOV cameras sacrifice spatial sampling density by distributing finite detector pixels over a broad angular range, while telephoto systems resolve fine features at the cost of scene coverage. Beam-steering devices can mitigate this trade-off but are currently limited in achieving simultaneously all-solid-state, large aperture, and high-speed operation. Here, we report an all-solid-state large-aperture cascaded
liquid-
crystal beam-steering (CaLiBS) imaging system that extends the effective angular range of a high-resolution narrow-FOV camera by electrically steering sub-FOVs. The CaLiBS module comprises cascaded
liquid crystal waveplates and
liquid crystal Pancharatnam-Berry phase gratings; a theoretical voltage-prediction model with a hierarchical search
algorithm enables efficient calibration under oblique incidence and 10 times faster calibration speed compared with conventional methods. The calibrated system addresses sub-FOVs across 30.3{\deg} * 30.3{\deg} at 2{\deg} intervals with diffraction efficiency above 60%. Sequential sub-FOV acquisition reconstructs a 34.7 * 34.7 composite image, an 8.6-fold enhancement in spatial-bandwidth product over a single-shot wide-FOV camera using the same detector. Combined with object tracking methods, sub-FOV switching further enables high-resolution tracking of moving vehicles within the wide-area scene. This cascaded LC architecture offers a scalable pathway toward compact, vibration-free, and high-resolution wide-field observation.
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All authors Chenxi Liu, Yongxiang Qu, Xiaoxin Wang, Leixin Meng, Xiaohua Feng, Qidong Wang, Guohao Zhang, Xuejun Zhang, Yubing Han, Qing Yang, Xu Liu, Mingwei Tang, Kai Wei
Instrumentation and Detectors
Design and beam-test characterization of the CRILIN semi-homogeneous crystal calorimeter
S. Ceravolo, M. Cavallina, V. Ciccarella et al. (+17 more)
CRILIN is a high-granularity semi-homogeneous electromagnetic calorimeter based on longitudinally segmented PbF$_2$
crystal matrices read out by UV-extended silicon photomultipliers. The concept combines fast Cherenkov response, fine transverse granularity, longitudinal shower information, and radiation tolerance for future
lepton-
collider experiments. This paper reports the construction of a large-area prototype and its performance measured in beam tests at the CERN SPS. The detector comprises five $7\times7$ PbF$_2$
crystal matrices, has a depth of about $22X_0$, and is read out by four $3\times3~\mathrm{mm}^2$ SiPMs per
crystal integrated in a single electronic channel. Electron data between 10 and 120~GeV and dedicated 150~GeV
muon data were used to characterize the detector response. A time resolution below 50~ps is achieved for electron energies above 10~GeV, reaching values below 20~ps above 60~GeV. The energy resolution is described by a
stochastic term of $(6.58\pm0.04)\%/\sqrt{E/\mathrm{GeV}}$ and a constant term of $(0.23\pm0.02)\%$, with an additional noise contribution fixed from pedestal data. The longitudinal
segmentation enables event-by-event corrections based on the reconstructed shower development, resulting in a significant improvement of the energy resolution. A light yield of approximately 0.54~photoelectrons/MeV is measured consistently using both electron showers and minimum-ionizing particles. A Geant4-based simulation incorporating the relevant experimental effects reproduces the measured energy resolution. These results validate the CRILIN architecture as a compact, fast, and longitudinally segmented electromagnetic calorimeter for future
collider experiments.
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All authors S. Ceravolo, M. Cavallina, V. Ciccarella, E. Di Meco, E. Diociaiuti, R. Gargiulo, Q. Han, E. Leonardi, D. Lucchesi, M. Moulson, L. Palombini, N. Pastrone, I. Raghib, A. Russo, A. Saputi, I. Sarra, S. Salamino, L. Sestini, S. Squerzanti, D. Zuliani
Atmospheric and Oceanic Physics
Memory compression and physical state augmentation favor different AMOC prediction tasks
Mauricio Herrera-Marín
The Atlantic Meridional Overturning Circulation is monitored and emulated through reduced indices, but such projections discard thermohaline structure and may require either explicit physical state or
memory of the observed index. We compare these strategies in 30 branch-consistent CMIP6 trajectories from eight model families using leave-one-family-out validation. Salinity, temperature and density information improves direct 20-year forecasts, whereas compact scalar
memory is top-ranked at every recursive horizon and yields the lowest case-averaged Brier score. A matched ablation confirms that feedback from
memory improves long-horizon prediction. Physical state and recent trends also predict future ocean-state changes beyond the emissions pathway, most robustly at five years. NorESM under SSP5–8.5 identifies a forcing-dependent limit of scalar compression, while MIROC shows negative long-horizon transfer. A resolvent analysis explains why stable
memory components do not guarantee stability of the complete learned model. Physical augmentation and
memory compression therefore serve different AMOC prediction tasks.
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Physics Education
Trajectories into Careers in the Quantum Industry: Beyond Knowledge and Skills
Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl et al. (+1 more)
Career preparation for participation in the
quantum industry is often framed in terms of formal educational pipelines, workforce projections, and knowledge and skills needed for various roles. Less is known about how
quantum industry professionals themselves characterize their preparation for participation in the field. In this paper, we analyze interviews with
quantum industry professionals working across a range of positions and company types to examine the experiences that enable entry into the
quantum industry. Using thematic analysis, we identify four trajectories: (1) continuity of research practice from academia to industry, (2) reframing of prior expertise for
quantum applications, (3) incremental engagement through various professional opportunities, and (4) network-enabled entry. These trajectories often co-occur within individual narratives, showing that preparation emerges from a combination of educational, professional, and relational experiences. Our findings demonstrate that supporting preparation for
quantum industry careers requires more than the design of formal coursework and degree programs. Our results highlight the importance of experiential learning opportunities that allow students to apply their knowledge and skills and develop professional connections that facilitate entry into
quantum careers.
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All authors Shams El-Adawy, A.R. Piña, Benjamin M. Zwickl, H.J. Lewandowski
Computational Physics
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang et al. (+12 more)
Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large and diverse
datasets now routinely deployed as 'foundation models' for downstream fine-tuning in targeted chemical spaces. Many scientific applications of the resulting models, such as molecular dynamics (MD), require high inference and training speeds as well as accuracy. In this work, we examine the limits of equivariant MLIPs, which directly encode physical
symmetries in model architectures, to achieve these competing targets – particularly in the regime of extremely large
datasets where data efficiency is less critical. We show how this trade-off can be addressed, and present a family of foundation potentials in the NequIP and Allegro equivariant MLIP architectures which achieve leading inference speeds and strong scalability as well as excellent accuracies across a range of community
benchmarks – spanning materials discovery, thermal conductivity prediction, and near-
equilibrium mechanical and thermodynamic properties. Accelerations implemented within the NequIP infrastructure now permit training of high-accuracy foundation potentials on ultra-large
datasets with dramatically reduced computational cost. Alongside, we show that efforts to improve model accuracy for materials discovery should focus on
dataset diversity and improved, consistent descriptions of transition metal compound energy surfaces.
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All authors Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang, Marc L. Descoteaux, Gabriel de Miranda Nascimento, Ulrik Unneberg, Laura Zichi, Francesco Libbi, Norma Rivano, Austin Glover, Vivek Bharadwaj, Anders Johansson, William C. Witt, Albert Musaelian, Boris Kozinsky
Medical Physics
A reduced viscoelastic FDTD formulation for ultrasound-driven shear wave propagation in soft tissue
Gianmarco Pinton
Ultrasound-driven shear wave propagation in soft tissue underlies shear wave elastography (SWE) and emerging elastomechanical hypotheses of ultrasonic neuromodulation, both of which require accurate, efficient modeling of radiation-force–induced tissue motion. General-purpose finite-element elastodynamic solvers are often computationally expensive and unnecessarily broad for shear-dominant applications. We derive a reduced viscoelastic formulation by applying near-incompressibility, small-strain linearization, Helmholtz decomposition, and solenoidal force projection to the full Navier equations, yielding a Kelvin–Voigt shear wave equation that retains only the transverse dynamics relevant to radiation-force–induced motion. An explicit finite-difference time-domain (FDTD) implementation with second-order spatial and temporal accuracy enforces the solenoidal body-force constraint via a
matrix-free conjugate-gradient Poisson solve. For separable radiation-force sources, a pre-computed projection reduces this cost by one to two orders of magnitude. In homogeneous media the solver recovers the theoretical shear wavespeed to within $<$0.1%, exhibits clear second-order grid convergence (trace-$L_2$ self-convergence, $p\gtrsim2$), and matches analytical Kelvin–Voigt attenuation and phase speed to within ${\sim}3\%$ and $<$1% over a 16-point viscosity sweep ($\eta\in[0,1.5]$~Pa$\cdot$s). An end-to-end RSNA QIBA phantom
benchmark recovers shear wave speeds to within ${\sim}1\%$ across a tenfold shear-modulus range ($G=1$–$10$~kPa). The framework accommodates spatial heterogeneity in shear modulus, density, and viscosity, and integrates with acoustic simulators. Transcranial demonstrations through micro-CT skull geometries produce shear displacements of 1.7–5~$\mu$m consistent with clinical ARFI.
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Atomic Physics
Optical Dipole Trap for Hg Atoms
I. Nandi, A. Sahu, M. Veis et al. (+3 more)
We report the first realization of an optical dipole trap (ODT) for ultracold mercury atoms, overcoming the challenge posed by the exceptionally low polarizability of Hg and the resulting weak optical trapping potential. We demonstrate the transfer of six naturally abundant Hg isotopes from a magneto-optical trap (MOT) into the ODT and characterize its loading dynamics, trap depth, and lifetime. Confinement of dense samples of ultracold Hg opens new opportunities for studies of isotope-dependent collisions and photoassociation and provides a route toward
quantum degeneracy.
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All authors I. Nandi, A. Sahu, M. Veis, A. Linek, R. Ciurylo, M. Witkowski
Optics
Extreme sensitivity of nonlinear trajectories enhances optical spectral broadening
Jiachen Wang, Koorosh Sadri and Mikael C. Rechtsman
The generation of a wide spectrum of light in highly nonlinear optical fibers has broad application in spectroscopy, microscopy and medical imaging. To generate such a 'supercontinuum', an ultrashort pulse of light is injected into a highly nonlinear fiber. Then, in a process called self-phase modulation, the nonlinearity of the fiber causes the spectrum to start broadening as the pulse becomes chirped during propagation, seeding further cascaded nonlinear processes. The dynamics associated with supercontinuum generation are captured mathematically as a pulse profile evolving in time and occupying a single spatial mode. Here, we theoretically and experimentally demonstrate that a waveguide composed of multiple coupled cores - a photonic molecule' - rather than just a single core, gives rise to greater self-phase modulation for a given input power. This is perhaps counterintuitive because it may be naively expected that the strongest nonlinear effects would be achieved by concentrating all optical power in one waveguide. The increased broadening arises due to the extreme sensitivity of trajectories near a separatrix of the nonlinear dynamics. This sensitivity leads to a distortion of the temporal shape of the pulse, resulting in a broader spectrum. The effect is reminiscent of the sensitivity associated with exceptional points in coupled-resonator systems, but does not suffer in the same way from the parasitic effects of noise. This suggests that by including multiple cores, a straightforward modification of conventional nonlinear fiber design, supercontinuum sources seeded by self-phase modulation can generate a significantly wider spectrum. More broadly, this demonstrates that the sensitivity to initial conditions associated with nonlinear dynamics may be utilized to generate stronger nonlinear effects in optics.
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Accelerator Physics
Fast optics-based modeling enabling large-scale optimization of the H4 and M2 beamlines in the CERN SPS North Area
Giovanni Dal Maso, Dipanwita Banerjee and Nikolaos Charitonidis
The CERN multi-purpose secondary beamlines are invaluable facilities offering a rich and diverse physics program to the international particle physics community, providing many different particle species and beam characteristics to fixed-target experiments and test-beam users. A number of
magnetic elements are required to enable such flexibility, posing a significant challenge in the design and optimization of the beamline optics. In this work, we present the optimization of the H4 and M2 beamlines of the CERN SPS North Area using genetic
algorithms. The aim of this study is to demonstrate the effectiveness of fast optics-based tracking models (here Xsuite), to enable large-scale, multi-objective optimization on standard computing hardware. The model is benchmarked and validated with high-fidelity Monte Carlo simulations using BDSIM and cross-validation of the results with experimental measurements. The optimized optics, applied to the H4 electron configuration for NA64, achieved a 30% increase in available electron rate and a five-fold reduction in beam-related background. In the M2 line, the optimized configurations yielded a nearly two-fold increase in electron rate and a 67% improvement in
muon transmission.
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Computational Physics
Performance Benchmarking: Software for the Density Matrix Renormalization Group
Per Sehlstedt, Paolo Bientinesi and Lars Karlsson
The performance of scientific software often determines the scale of problems that can be solved in practice. As multiple implementations of the same
algorithm emerge, systematic evaluation is needed to compare their strengths and limitations. The density
matrix renormalization group (DMRG)
algorithm, widely used to study
quantum systems, has over 50 software implementations. These implementations vary in multiple aspects that can strongly affect performance. However, despite the need, performance evaluations of these implementations are scarce and lack a consistent standard; many existing evaluations are either too incomplete to enable meaningful comparisons or focus on objectives other than direct performance comparisons, thereby limiting understanding of how the implementations compare. Here, we present a performance-oriented benchmarking framework to facilitate meaningful comparisons of DMRG implementations, and we apply it to quantify the performance of eight implementations, highlighting similarities and differences among them. Furthermore, we examine multiple parameter settings, optimization strategies, and implementation-specific features to demonstrate how parameter configuration can affect performance and how systematic evaluation can reveal non-obvious trade-offs. The results show significant performance differences, up to two orders of magnitude in some cases, not only between different implementations when aligning parameters, but also within the same implementation when comparing different parameter configurations. Hence, our results demonstrate the significant value and insight that can be gained from conducting rigorous performance evaluations. Using our results and framework as a starting point, more rigorous benchmarking will ultimately help users and developers make informed decisions and support future development efforts to build better, more efficient software.
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Instrumentation and Detectors
On a Liquid Krypton TPC for double positron decay searches
S. Torelli, A. Simón Estévez and F. Monrabal Capilla
Double positron decay features the emission of four 511 keV gamma rays from positron alongside the well-known 'double blob' signature which can be tracked with a TPC. Successfully detecting these gammas creates a unique signature that cannot be replicated by any backgroun. This enables a virtually background-free search if the four gammas are efficiently tagged. In this work, we propose the concept of a
liquid Krypton Time Projection Chamber designed to search for double positron decay using this four-gamma tagging concept, and we'll show how already a small version of this prototype could set the world best limit on the search of this decay towards a sensitivity of $10^{29}-10^{30}$ y with a ton scale detector.
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Atmospheric and Oceanic Physics
An unstructured-grid, nonhydrostatic, GVC ocean model Part I: Model description and application of vertical hybrid coordinates to internal solitary waves
B. J. Pauken, L. Yue, Y. Zhang et al. (+2 more)
We present a nonhydrostatic ocean model with a horizontally unstructured, C-grid and a moving, generalized vertical coordinate (GVC) designed for the simulation of nonhydrostatic processes in realistic ocean domains. The GVC system can represent any of the well-known z-level, terrain-following, or isopycnal coordinates while also being able to employ hybrid vertical coordinates. In this paper we outline the specific steps needed to incorporate the GVC system into the unstructured, C-grid, nonhydrostatic, z-coordinate SUNTANS model of Fringer et al. (2006). The approach is adapted from the nonhydrostatic, isopycnal-coordinate method of Vitousek and Fringer (2014), yet our model differs from that implementation through the development of a conservative momentum advection scheme and a positivity-preserving layer height scheme for horizontally unstructured grids. We validate the momentum advection implementation with simulations of a turbulent channel flow and demonstrate the advantages of the hybrid vertical coordinate approach over z- or terrain-following coordinates through simulations of internal solitary waves and the associated bottom boundary layer instability.
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All authors B. J. Pauken, L. Yue, Y. Zhang, S. Vitousek, O. B. Fringer
Physics Education
Uniform and Accelerated Circular Motion with a Smartphone: A No-Code, AI-Generated Browser Laboratory
Josep Ll. Suñer, Francisco M. Muñoz-Pérez, Juan C. Castro-Palacio et al. (+4 more)
Smartphones have become a standard measurement instrument in the physics laboratory. Their built-in accelerometers, gyroscopes, magnetometers, and cameras have been used to investigate a wide range of phenomena in mechanics, and rotational motion in particular has proven especially well-suited to smartphone-based experiments. A recurring limitation, however, is that most experiments rely on precompiled sensor apps whose interfaces cannot be tailored to a specific activity, and until recently creating customized smartphone laboratories required programming knowledge beyond what most physics teachers possess. This paper makes two connected contributions. First, we present a validated smartphone experiment for uniform circular motion (UCM) and uniformly accelerated circular motion (UACM), in which the angular velocity and the angular acceleration are obtained from two independent smartphone channels and checked against independent
video analysis with Tracker.10,14 Second, we show that generative AI can serve as a no-code development tool for the physics laboratory: the browser-based application used here was produced entirely through natural-
language prompting, with no manual coding, extending to rotational motion the approach we recently introduced for acoustic experiments. The emphasis is therefore not only on what is measured, but also on how the measuring instrument itself was built.
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All authors Josep Ll. Suñer, Francisco M. Muñoz-Pérez, Juan C. Castro-Palacio, Juan A. Monsoriu, Martín Monteiro, Cecilia Stari, Arturo C. Martí
Chemical Physics
Wavelength-Resolved Photoinduced Spin Polarization in a Broad Optical Range for a Porphyrin-Quinone System
Liubov Chuchkova, Oleg Tretiak, Kirill F. Sheberstov et al. (+3 more)
Photochemically induced dynamic
nuclear polarization (photo-CIDNP) in
liquid-state donor-acceptor systems is typically studied at a limited number of excitation wavelengths, leaving its spectral dependence incompletely characterized. Understanding the wavelength dependence of photo-CIDNP is important both for elucidating the underlying
spin-chemical mechanisms and for optimizing hyperpolarization strategies in chemically and biologically relevant molecular systems. Here, we investigate wavelength-resolved photo-CIDNP in a tetraphenylporphyrin-1,4-benzoquinone donor-acceptor system over the 350-800 nm spectral range.
Photon-flux-normalized CIDNP amplitudes were measured using both a tunable
laser system and a broadband xenon lamp equipped with interchangeable 10 nm interference filters. The CIDNP response exhibits a non-monotonic dependence on excitation wavelength. Pronounced hyperpolarization is observed near 350 nm and in the 500-550 nm region, whereas excitation within the strongly absorbing 400-450 nm range results in a substantially reduced CIDNP response. Comparison with the UV-Vis absorption spectrum demonstrates that photo-CIDNP efficiency is not governed solely by optical absorption and reflects wavelength-dependent photophysical processes. After normalization to the excitation
photon flux, lamp- and
laser-based measurements yield consistent CIDNP results, validating broadband filtered excitation as a reliable and experimentally accessible approach for wavelength-resolved photo-CIDNP studies. These results establish excitation wavelength as an independent control parameter for
liquid-state photo-CIDNP and provide a framework for systematic investigations of wavelength-dependent
spin hyperpolarization.
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All authors Liubov Chuchkova, Oleg Tretiak, Kirill F. Sheberstov, Danila A. Barskiy, Raphael Kircher, Dmitry Budker
Fluid Dynamics
Compressible solved-volatility stochastic fluid thermodynamics: source-consistent energy, finite-correlation reservoirs, entropy admissibility and boundary conditions
Hsieh-Chen Tsai
A variable-density thermodynamic extension is developed for the solved-
volatility stochastic-fluid formulation of arXiv:2607.25536. Source-inclusive
stochastic transport separates mass, momentum and total-energy conservation into time-
evolution partial differential equations and martingale compatibility constraints. Density and temperature are the primitive thermodynamic fields: mass conservation determines density, internal energy determines temperature, and the equation of state determines pressure
evolution along
stochastic particle paths. The resolved kinetic-energy identity is combined with a finite-correlation reservoir, Green–Kubo calibration, an
equilibrium counterterm and adjoint resolved-unresolved exchange. A
stochastic Gibbs identity and Gaussian relative
entropy yield a conditional
entropy-admissibility result for a Hencky-reservoir formulation. Equation-of-state pressure fluctuations are distinguished from mechanical stress impulses; regular finite-Mach fluctuations produce no independent white-noise bulk pressure impulse, while fast mechanical pressure is represented by a causal finite-correlation carrier. Conservative boundary conditions and a calorically perfect ideal-gas specialization are given. In the zero-
volatility limit, the classical compressible Navier–Stokes–
Fourier equations are recovered. A frozen descriptor analysis identifies a mixed
hyperbolic–
parabolic drift subsystem coupled to algebraic martingale constraints, with closure-dependent
elliptic blocks and a singular low-Mach pressure limit. Canonical calculations verify the pressure carrier, acoustic dispersion, viscous-thermal energy balance and low-Mach scaling. Nonlinear well-posedness, shock admissibility and developed
turbulence are not claimed.
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Optics
Large-Time-Step Operation in a Volume Integral Equation for Dielectric Scattering
Sushil Kumar, Giampiero Gerini and M. C. van Beurden
In transient electromagnetic analysis, explicit time-domain solvers are restricted by the Courant-Friedrichs-Lewy (CFL) condition, making finely discretized dielectric scattering problems computationally expensive. This work investigates large-time-step operation in a marching-on-in-time time-domain current-density volume integral equation (MOT-JVIE) solver for dielectric scattering. For the considered band-limited excitations, accurate transient analysis is demonstrated for time steps up to 16 times larger than the reference CFL-limited time step associated with the voxel discretization. The study reveals a fundamental computational shift in the large-time-step regime. As the time-step size increases, the present-time causal interaction region expands, increasing the number of nonzero entries in the present-time interaction
matrix and causing the dominant computational cost to transition from history-term evaluations to repeated
matrix–vector products involving this
matrix. Consequently, the present-time interaction
matrix emerges as the principal scalability bottleneck in the large-time-step regime. To address this bottleneck, a
matrix-free FFT-based
matrix–vector-product strategy that exploits the multilevel Toeplitz structure of the Green-function-related volume-integral operator is employed for the present-time interaction
matrix. The proposed framework is evaluated through an inhomogeneous dielectric cube and an 8 X 8 array of inhomogeneous dielectric nanopillars representative of multiscale metasurface structures, demonstrating more than an order-of-magnitude reduction in computational cost. In single-threaded execution, the method is demonstrated for 15.6 million unknowns, providing a large-scale MOT-JVIE demonstration beyond 15 million unknowns on one CPU thread.
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Geophysics
A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro et al. (+7 more)
Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have underscored their vulnerability and the need for continuous monitoring solutions. Distributed acoustic sensing (DAS) applied to submarine optical-fiber cables enables wide-area monitoring of
underwater acoustic activity. We present the Marlinks-NS DAS
dataset, comprising processed submarine DAS measurements and AIS-derived vessel information curated for cable-protection research. The
dataset defines two machine-learning tasks (vessel detection and vessel-to-cable distance estimation) allowing reproducible research under realistic marine conditions. The
dataset contains 74,771 labeled data instances from ten days of continuous recording along a 2,554 m segment in a 28 km buried fiber-optic cable in the North Sea. Each instance includes spectral-energy features from 250 sensing channels, together with anonymized distance measurements and metadata from AIS information. The released HDF5 data, documentation, processing description, and example code support reproducible development and evaluation of DAS-based vessel-monitoring methods for submarine cable protection.
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All authors Erick Eduardo Ramirez-Torres, Javier Macias-Guarasa, Daniel Pizarro, Javier Tejedor, Sira Elena Palazuelos-Cagigas, Pedro J. Vidal-Moreno, María R. Fernández-Ruiz, Sonia Martin-Lopez, Miguel Gonzalez-Herraez, Roel Vanthillo
Plasma Physics
Variational Formulation of a Hybrid Kinetic and Gyrokinetic Model for Astrophysical and Laboratory Plasmas
F. N. deOliveira-Lopes, S. C. Thatikonda, D. Told et al. (+5 more)
We present a Lagrangian model that encapsulates fully kinetic ions and gyrokinetic electrons for
solar wind electromagnetic
turbulence. Using a consistent method, where both electrons and
protons are treated with the same mathematical formalism, we derive and implement a model in which high-frequency waves and kinetic electron effects are described in a computationally cost-efficient way. Higher-order Lie-transform perturbation methods applied to
Hamiltonian formulation of guiding center motion are used to describe the dynamics of particles and fields. We use a variational approach to derive field equations for closure of the system.
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All authors F. N. deOliveira-Lopes, S. C. Thatikonda, D. Told, A. Mustonen, K. Pommois, K. Hagiwara, F. Jenko, R. Grauer
Biological Physics · Cross-listed from cond-mat.stat-mech
Synchronization, Kinematic Waves and Spike-Phase-Separation in Feedback Ising Neural Networks on Heterogeneous Graphs
Anna Poggialini, Irem Topal, Fabrizio Lombardi et al. (+1 more)
Structural heterogeneity constrains collective dynamics in complex systems. However, its analytical tractability out of
equilibrium remains limited. In this work, we study a class of kinetic Ising neural networks driven out of
equilibrium by a homeostatic feedback loop between the neuronal excitability and the population firing rate. Using a Curie-Weiss heterogeneous mean-field approximation validated by Monte Carlo simulations, we provide an analytical characterization of how a macroscopic synchronized limit cycle emerges via an Andronov-
Hopf bifurcation on heterogeneous networks. We derive closed-form phase boundaries and show that the onset of oscillations is explicitly controlled by network heterogeneity through the degree moment ratio. Degree heterogeneity decouples the spiking rate per
neuron m from the spiking rate per synapse u, generating physical phenomena absent in homogeneous systems. These include (i) kinematic waves of sequential, degree-ordered activations propagating from the network periphery to the hubs, and (ii) a low-temperature phase-separated state emerging via a pitchfork bifurcation. We prove that for highly heterogeneous
topologies, this phase-separated fixed point stabilizes and dynamically destroys the synchronized limit cycle. These results provide a mathematical framework for understanding how heterogeneity regulates macroscopic oscillations and out-of-
equilibrium transitions in neural networks
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All authors Anna Poggialini, Irem Topal, Fabrizio Lombardi, Daniele De Martino
Data Analysis and Statistics
Unveiling Amplitude Distributions via the Ordinal Language of Random Walks
Alberto Mateos Roig, Luciano Zunino and Felipe Olivares
Ordinal patterns are widely used to characterize temporal organization in time series, yet they are often considered insensitive to the amplitude distribution of the data. In this work, we show that this limitation can be overcome by considering the ordinal structure of integrated time series. We investigate random walks generated from independent non-Gaussian increments and derive analytical expressions for the ordinal pattern probabilities associated with them. We show that for symmetric distributions, the probabilities of some ordinal patterns are fully determined by
symmetry arguments, while those for the remaining patterns depend explicitly on the shape of the increments distribution. Numerical simulations based on $q$-Gaussian increments validate the theoretical predictions. We further show that the construction of the random walk itself plays a fundamental role in the accurate characterization of non-Gaussian fluctuations, as different centering procedures may significantly affect the resulting ordinal statistics. Finally, we validate the proposed framework using financial time series, showing that the ordinal distributions of integrated logarithmic returns capture non-Gaussian features consistent with a cubic law.
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Physics and Society
High-fidelity inference of power grid frequency distributions
Alessandro Lonardi, Benjamin Schäfer and Christian Beck
Precision measurements of power grid frequency track energy supply-demand imbalances, with persistent fluctuations indicating strain that can lead to outages. Characterizing the statistical properties of grid frequency fluctuations is therefore essential to achieve efficient control and stable operations. Existing models had limited success in reconstructing the complex non-Gaussian features of observed frequency distributions. Here, we introduce a method for statistical inference of an interpretable
stochastic process governing frequency fluctuations, modeling frequency through a coarse-grained power imbalance signal combined with nonlinear generator control and Gaussian white noise. We develop an efficient
algorithm to infer these latent variables via maximum likelihood estimation, combined with superstatistics. We test our method on a large
dataset of new measurements from Great Britain and South Africa. Although these grids have markedly different properties, our predictions for frequency distributions match measurements excellently. Our method uses only frequency data for inference, thus offering an alternative to data-intensive approaches.
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Optics · Cross-listed from quant-ph
Displaced photon helicity states: a quantum realization of Arago-Fresnel interference
Yan Zhang, Xiang Liu and Hou Ian
The laws of Arago and Fresnel relate the polarization of light to its capacity for interference. We investigate their
quantum counterpart at the level of the fundamental helicity states of a single
photon. To this end, we introduce displaced
photon helicity states (DPHS), constructed by coherently displacing single-
photon helicity eigenstates. The displacement bridges the microscopic
spin degree of freedom and macroscopic polarization observables, enabling interference effects that would otherwise be inaccessible to isolated
photons. We derive analytical expressions for the Stokes parameters associated with the interference between DPHS and coherent optical fields and show that opposite helicities generate distinct parameter dependences in the resulting fringe structure. The interference constitutes a
quantum realization of Arago-Fresnel interference and permits the helicity of an unknown
photon to be inferred from the Stokes response of the combined field. Beyond this application, DPHS generalize the framework of displaced nonclassical states from
photon-number excitations to the helicity degree of freedom of light.
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Atmospheric and Oceanic Physics
Weather Emulators at the Frontier of Heat Extremes Predictability
Cas Decancq, Thomas Mortier, Jessica Keune et al. (+1 more)
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge. Here we evaluate six state-of-the-art deep learning weather emulators - Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora - alongside leading dynamical systems and statistical baselines in
forecasting global near-surface temperature and extreme heat at lead times of 10-15 days. We find that several emulators rival or even surpass physics-based forecasts in deterministic temperature skill, but do so at the cost of reduced spectral fidelity, in a process widely known as blurring. While all models show some degree of predictive skill for extreme heat, most emulators under-represent peak intensities, and IFS recall is greater than that of any of the emulators. These results highlight both the emerging potential of AI to enhance extended range temperature prediction, and the remaining challenges in delivering reliable, actionable early warnings in a changing
climate.
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All authors Cas Decancq, Thomas Mortier, Jessica Keune, Diego G. Miralles