Applied Physics · Cross-listed from cond-mat.soft
Suppressed Rupture of Thin Metal Films via van der Waals Epitaxy
Wenxiang Wang, Jiaxing Wang, Guotong Wang et al. (+9 more)
Ultrathin metal films exhibit
liquid-like instabilities, rupturing via surface
diffusion far below their melting points. This behavior constrains thermal budgets for advanced integrated
circuits and emerging 2D-
crystal devices. Here, we demonstrate that these instabilities can be fundamentally suppressed using
graphene as a van der Waals (vdW) template. While conventional 20-nm-thick gold films break up into islands below 300 {\textdegree}C, templated films not only remain stable but also become structurally refined after annealing above 600 {\textdegree}C. This exceptional stability stems from a vdW-mediated crystallographic texture that reorganizes grain boundaries into a mechanically robust network. This mechanism significantly widens the processing window for nanoscale interconnects and enables high-temperature integration of metals with 2D-
crystal technologies.
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All authors Wenxiang Wang, Jiaxing Wang, Guotong Wang, Zhichao Yan, Chenxiao Jiang, Siqin Zhou, Chuanli Yu, Jianhao Chen, Kun Zheng, Thomas Salez, Xiaoding Wei, Zhaohe Dai
Atomic Physics
High-resolution spectroscopy of 162Dy Rydberg levels
G. Ferioli, P. Lombardi, P. Sekhar et al. (+9 more)
Highly excited Rydberg states of lanthanides are a promising, yet largely unexplored, playground for
quantum studies. Here, we report on the first high-resolution spectroscopy of 162Dy obtained by two-color trap depletion spectroscopy in a magneto-optical trap. The absolute excitation frequency of over 700 states with effective principal
quantum number n between 21 and 130 is measured with an accuracy of 20 MHz. Most states are assigned to the 8 different series converging to the first 4f10(5I8)6s(2S1/2) J = 17/2 ionization potential. This energy is measured at EIP = 47901.8265 +/- 0.0008 cm-1, improving the precision of the literature value by over an order of magnitude. A multichannel
quantum defect theory approach is used to
benchmark and refine the assignments and to characterize six observed perturbing states belonging to higher ionization limits. These results pave the way for using dysprosium in Rydberg-based
quantum architectures, leveraging the unique properties arising from its complex electronic structure. They also represent a compelling
benchmark for ab-initio calculations of open-shell atomic systems.
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All authors G. Ferioli, P. Lombardi, P. Sekhar, E. Sole Cardona, N. Preti, C. Drevon, N. Antolini, L. Tanzi, G. Modugno, C. Gabbanini, F. Robicheaux, A. Fioretti
Biological Physics · Cross-listed from cond-mat.soft
A label-free method to quantify early-stage amyloid aggregation via intrinsic phenylalanine fluorescence
Gaëlle Audéoud, Louis Moine, Laura Bonnecaze et al. (+5 more)
The aggregation of amyloid-forming peptides is a dynamic, complex process that underlies their diverse biological activities, from physiological functions to disease-associated dysfunctions. While the structure of fibrillar end-products is well-characterized for most amyloids, the heterogeneous and often transient oligomers, likely key in cytotoxicity, remain poorly investigated, especially for peptides with low-yield aromatic residues. Here, by exploiting and developing flow induced dispersion analysis in both peak and front modes, we demonstrate that intrinsic phenylalanine fluorescence can be harnessed to quantify the conversion of diffusing monomers into non-diffusing oligomers and fibrils. This approach is validated using PSM$\alpha$3, a tryptophan- and tyrosine-free peptide from S. aureus, known for its fast amyloid fibrillation and key roles in bacterial virulence. We indeed characterize its low-molecular-weight oligomers, and size
evolution from 2 to 10 nm over time. Importantly, to further validate the robustness of our front-mode approach, we apply it to hIAPP, a tyrosine-containing, tryptophan-free peptide, with critical outcome in type 2 diabetes. Our results overcome the limitations of traditional biochemical and biophysical amyloid assays by extending analysis from large oligomers and fibrils to small heterogeneous oligomers, under near-physiological conditions. This study thus offers a new analytical framework, thereby filling a critical gap in amyloid research, to probe the early stages of aggregation, key in the design of alternative therapeutics for amyloid-diseases.
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All authors Gaëlle Audéoud, Louis Moine, Laura Bonnecaze, Maxime Lavaud, Lucie Khemtemourian, Yacine Amarouchene, Thomas Salez, Marion Mathelié-Guinlet
Accelerator Physics
Conventional Accelerator Magnets
Stephane Sanfilippo
This course introduces conventional magnets used in particle accelerators, focusing on both normal-conducting copper coil magnets and permanent magnets (PMs). It covers magnet classification, design principles, material selection, and mechanical constraints. Advantages and limitations of PMs compared to copper coil magnets are discussed. Key construction steps and cooling methods are presented. The course also includes
magnetic field measurement techniques and quality control. Practical examples from PSI and CERN illustrate the concepts.
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Atmospheric and Oceanic Physics · Cross-listed from math.AP
Local well-posedness of strong solutions to the non-isentropic compressible primitive equations with vertical diffusion
Rupert Klein, Jinkai Li, Xin Liu et al. (+1 more)
Due to the absence of dynamical equation in the vertical momentum component of the primitive equations (PEs) of atmospheric dynamics, the vertical component of the velocity can be recovered only from the information on the other physical quantities, while utilizing the hydrostatic balance. This causes one spatial derivative loss while leads to a stronger nonlinearity, comparing to the classic compressible Navier-Stokes equations. As a result, the mathematical analysis on the compressible primitive equations is mathematically more challenging than that on the compressible Navier-Stokes equations. In this paper, we consider the initial-boundary value problem to the non-isentropic compressible primitive equations with only vertical
diffusion for the temperature, but without
gravity. Local existence and uniqueness as well as the continuous dependence on the initial data of strong solutions are established for any suitably regular initial data. The initial velocity and pressure are assumed here to be of one order derivative higher regularity than that of the initial density.
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All authors Rupert Klein, Jinkai Li, Xin Liu, Edriss S. Titi
Optics
Composition-Dependent Plasmon-Enhanced Emission in Lead-Free Cs$_3$Cu$_2$X$_5$ Halides: A DFT–FDTD Study
Shoumik Debnath, Sudipta Saha, Khondokar Zahin et al. (+2 more)
Lead-free Cs$_3$Cu$_2$X$_5$ (X = Cl, Br, I) halides exhibit high photoluminescence
quantum yields and excellent ambient stability, yet light-emitting devices based on these materials remain limited by poor optical outcoupling. In this work, we develop an integrated density functional theory (DFT) and finite-difference time-domain (FDTD) framework to establish quantitative links between halide composition, wavelength-dependent optical constants, and plasmonic enhancement. First-principles calculations are used to obtain composition-specific refractive index (n) and extinction coefficient (k) spectra, which are directly implemented into three-dimensional FDTD simulations of a complete PeLED stack incorporating Ag/SiO$_2$ core–shell nanostructures. Among the investigated compositions, Cs$_3$Cu$_2$Cl$_5$ demonstrates the strongest plasmonic response, achieving a 4.4$\times$ Purcell enhancement and 30% light extraction efficiency (LEE) using optimized nanorods. The superior performance originates from its lower refractive index, which reduces dielectric screening and improves near-field coupling. Cs$_3$Cu$_2$Br$_5$ exhibits the highest spectral overlap ($J_{\mathrm{cos}} = 0.955$) but yields moderate extraction (26%) due to increased optical confinement. Cs$_3$Cu$_2$I$_5$ requires a nanosphere geometry and shows limited enhancement, with LEE restricted to 10%. Distance-ependent analysis reveals composition-specific optimal emitter–plasmon separations, ranging from 8–12 nm for Cs$_3$Cu$_2$Br$_5$ to approximately 15 nm for Cs$_3$Cu$_2$Cl$_5$. These results provide composition-dependent design guidelines for plasmon-enhanced lead-free PeLEDs and highlight the critical role of accurate optical constants in predictive device optimization.
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All authors Shoumik Debnath, Sudipta Saha, Khondokar Zahin, Ying Yin Tsui, Md. Zahurul Islam
Computational Physics · Cross-listed from q-bio.QM
Exact Discrete Stochastic Simulation with Deep-Learning-Scale Gradient Optimization
Jose M. G. Vilar and Leonor Saiz
Exact
stochastic simulation of continuous-time Markov chains (CTMCs) is essential when discreteness and noise drive system behavior, but the hard categorical event selection in Gillespie-type
algorithms blocks gradient-based learning. We eliminate this constraint by decoupling forward simulation from backward differentiation, with hard categorical sampling generating exact trajectories and gradients propagating through a continuous massively-parallel Gumbel-Softmax straight-through surrogate. Our approach enables accurate optimization at parameter scales over four orders of magnitude beyond existing simulators. We validate for accuracy, scalability, and reliability on a reversible dimerization model (0.09% error), a genetic oscillator (1.2% error), a 203,796-parameter gene regulatory network achieving 98.4% MNIST accuracy (a prototypical deep-learning multilayer perceptron
benchmark), and experimental patch-clamp recordings of ion channel gating (R^2 = 0.987) in the single-channel regime. Our GPU implementation delivers 1.9 billion steps per second, matching the scale of non-differentiable simulators. By making exact
stochastic simulation massively parallel and autodiff-compatible, our results enable high-dimensional parameter inference and inverse design across systems biology, chemical kinetics, physics, and related CTMC-governed domains.
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Applied Physics
AC loss modelling in a 2 MW-class REBCO high temperature superconducting motor for hydrogen-electric aircraft
Enric Pardo, Alexandre Colle, Remi Dorget et al. (+1 more)
High temperature superconducting motors are very promising for hydrogen-electric aircraft thanks to their high specific power, specific torque, and efficiency. High temperature
superconductor REBCO offer high cryogenic flexibility, but a stator made of REBCO tapes could present high AC loss. Although stacking effect reduce AC loss, it could be compromised by imperfections, such as winding misalignment and tape inhomogeneity. Therefore, it is needed to know whether the AC loss is acceptable in realistic REBCO stators. This article analyses the AC loss in a REBCO propulsion motor for aviation that takes these imperfections into account. For this purpose, we developed our own fast and accurate numerical model, which considers the highly nonlinear screening currents in the
superconductor into account. This work studies a motor of around 2 MW power with REBCO stator coils with 27 parallel tapes as conductor and a permanent-magnet rotor. We consider several electric coupling scenarios of the multi-tape conductor. We also analyze the effect of finite tape-to-tape resistances at the terminals. We have found that the AC loss for the whole motor in the most realistic coupling scenario represents less than 0.018 % of the rated power. Misalignments and tape degradation at the edges of up to 100 $\mu$m only increase AC loss by up to around 20 %. Therefore, motors with REBCO
superconductors in the stator are feasible for aircraft propulsion.
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All authors Enric Pardo, Alexandre Colle, Remi Dorget, Mohamed Essam Ahmed
Instrumentation and Detectors
Designing electrostatic MEMS-based electron optics: the case of the spiral phase plate
Payam Habibdazeh Kavkani, Amir H. Tavabi, Paolo Rosi et al. (+8 more)
A new generation of microfabricated MEMS for electron optics is changing electron microscopy for the better. These devices allow operations on the electron beam that are impossible with conventional electron optics. Unprecedented phase landscapes like tunable spiral phase plates and localized strong phase gradients are just some examples of what can be achieved. This work establishes the methodological foundation to design and control MEMS based phase plates. The design strategy is rooted on a novel analytical and numerical modeling of thin electrodes with accurate account of the fringing fields having a major role in the thin-MEMS geometry. We designed, fabricated and characterized experimentally a spiral phase plate, and assessed the quality of the generated
vortex beam while discussing the most relevant control parameters and design approaches.
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All authors Payam Habibdazeh Kavkani, Amir H. Tavabi, Paolo Rosi, Alberto Roncaglia, Enzo Rotunno, Luca Belsito, Sergio Sapienza, Stefano Frabboni, Rafal E. Dunin-Borkowski, Marco Beleggia, Vincenzo Grillo
Classical Physics
Damped harmonic oscillator revisited: a new approach to energy decay in the case of Coulomb, Stokes, and Newton damping
Robert Pezer and Karlo Lelas
Approximate formulas are derived to describe energy loss in a harmonic oscillator that experiences three distinct damping mechanisms: constant-magnitude (Coulomb), velocity-proportional (Stokes), and velocity-squared (Newton), using fundamental mathematical methods and physical insight. Our methodology leverages an understanding of the free harmonic oscillator and the inherent link between energy dissipation rates and the power exerted by damping forces. We establish a direct analytical framework for assessing the energy of a damped harmonic oscillator, obviating the need for amplitude-based equations. The simplicity of our findings is accompanied by their remarkable accuracy when validated against exact or computational simulations. In addition to an excellent approximate description of the energy decay, we also show how to derive an exact solution in the case of Stokes damping without relying on the standard procedure for solving second-order differential equations. The theoretical underpinnings and mathematical strategies employed are well-suited for undergraduate-level or advanced high school physics instruction.
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Medical Physics
SHARP: A compact focusing system for medical applications using a diverging plasma lens
Kyrre Ness Sjobak, Elisabeth Rød-Lindberg, Abélia Ellingsen et al. (+5 more)
Cancer therapy for deep-seated tumors requires precise irradiation of a small target deep within the patient while minimizing radiation exposure to surrounding tissues. This can be accomplished with a round beam sharply converging towards a single spot, requiring a large beam size in both planes at the exit of the focusing system. Achieving this over a short distance using only quadrupole lenses is challenging; but by using a linear active
plasma lens (APL) in defocusing mode, the beam can be quickly and non-destructively enlarged before focusing using quadrupoles. The position of the irradiation spot can also be scanned in three dimensions by changing magnet settings. The SHARP project will develop and test this concept. Such a system can be used with very high energy electrons (hundreds of MeV), creating a Bragg-peak-like spot using novel accelerator technology. This could lead to more compact radiotherapy facilities, not requiring a bulky infrastructure typically associated with
proton radiotherapy machines. If successful, SHARP will enable precision conformal radiotherapy, spatial fractionation, and potentially be useful for FLASH radiotherapy.
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All authors Kyrre Ness Sjobak, Elisabeth Rød-Lindberg, Abélia Ellingsen, Pierre Drobniak, Vilde Flognfeldt Rieker, Fardous Reaz, Carl Andreas Lindstrøm, Erik Adli
Physics and Society
Group adaptation drives opinion dynamics in higher-order networks
Cosimo Agostinelli, Marco Mancastroppa and Alain Barrat
In modern interconnected societies, opinions and beliefs can quickly spread across large populations, giving rise to collective behaviors such as the adoption of social norms or polarization. These phenomena have motivated many models aimed at reproducing emergent properties from simple interaction mechanisms. In particular, opinion dynamics models describe how individual opinions evolve through interactions and study the conditions for global consensus or polarization. Most models assume that these interactions occur between pairs of
agents, typically on a fixed network structure. However, opinion changes can occur in groups, which may also undergo adaptive changes if disagreement arises. Here, we propose a bounded confidence model that incorporates both mechanisms: group discussions can lead to global agreement among members, while strong internal disagreement causes groups to split, with resulting subgroups merging with others. We systematically study the model outcomes as a function of
agents' tolerance for agreement. Strikingly, adaptivity suppresses key effects of group interactions, restoring a phenomenology close to that of pairwise interactions. In particular, adaptivity enables the formation of large groups and prevents fragmentation at small tolerance. It also restores a phase transition from polarization to consensus, which would otherwise disappear in a non-adaptive group-based model. Overall, our work shows that both adaptivity and group interactions shape the structure of social ties and global opinion dynamics in a population.
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Plasma Physics
Gyrokinetic simulation of the effect of transient fueling on plasma turbulence in ADITYA-U tokamak
Jaya Kumar Alageshan, Suman Dolui, Joydeep Ghosh et al. (+8 more)
The gradient-driven microturbulence in ADITYA-U tokamak
plasmas has been suppressed by injecting short gas puffs. The suppression of microturbulence increases the core temperature and subsequently the energy confinement time following the gas puff. The gas injection modifies the radial density profile, making it relatively flatter near the mid-radius. Global electrostatic gyrokinetic simulations show that this modification to the radial density profile due to gas injection suppresses the existing trapped electron mode (TEM). Simulation results show that the TEM-dominated
turbulence suppression reduces the
turbulence-driven heat transport, leading to an increase in core temperature. Applying multiple periodic gas-puffs leads to multiple periodic events of TEM suppression, improving the overall energy confinement time, and is used as an active control mechanism to influence microturbulence in ADITYA-U tokamak.
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All authors Jaya Kumar Alageshan, Suman Dolui, Joydeep Ghosh, Kishore Mishra, Sarveshwar Sharma, Abhijit Sen, Manjunatha Valmiki, Sandeep Agrawal, Sanjay Wandhekar, Zhihong Lin, Animesh Kuley
Optics
Circular V-grooves on single-crystal gold: optical properties and sensing feasibility
Amos Sospeter Kiyumbi
Single-
crystal Au(111) microplates provide an ultra-smooth, low-defect platform for reproducible plasmonic nanocavities. Here we realize reflection-mode whispering-gallery metasurfaces comprising periodic arrays of circular V-groove cavities milled into optically thick Au microplates and characterize their visible–near-IR response. The measured spectra exhibit narrow, depth-tunable Fano-like reflectance minima with weak azimuthal dependence, reproduced by quarter-
cell finite-element modeling consistent with strong gap-surface-plasmon confinement. For refractometric sensing, simulations yield bulk sensitivities up to $\sim 598$ nm RIU$^{-1}$ and figures of merit up to $\sim 33$ (hexagonal
lattice). As a model-based illustration, a functionalized architecture targeting Plasmodium falciparum lactate dehydrogenase (PfLDH), a common malaria target of rapid diagnostic tests, gives an estimated limit of detection of $0.016$ nM ($\sim 0.56$ ng mL$^{-1}$) under the adopted noise floor.
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Physics Education
Curiosity Over Hype: Modeling Motivation Language to Understand Early Outcomes in a Selective Quantum Track
Daniella Alexandra Crysti Vargas Saldana and Freddy Herrera Cueva
We study whether latent motivation signals in short Spanish admission responses predict engagement and performance in an early
quantum computing pathway run by QuantumHub Peru. We analyze N=241 applicants' open responses and link them to outcomes from two selective modules: Module 1 (secondary; mathematics and computing foundations; n=23) and Module 2 (secondary + early undergraduate;
quantum fundamentals; n=36, including M1 continuers). To ensure baseline comparability, the M2 university entrance exam matched the difficulty of the M1 final. Final grades followed the program's official cohort-specific weightings (attendance/assignments/exam), which we retain to preserve ecological validity. Methodologically, we model text with Latent Dirichlet Allocation (LDA, k=8) and, for robustness, with sentence
embeddings from a small
multilingual language model, EmbeddingGemma-300M, projected via UMAP and clustered with HDBSCAN. This combination leverages the transparency of bag-of-words topics and the semantic richness of small
language model
embeddings. Descriptively, curiosity/learning topics show higher grades and attendance than technology/career-oriented topics; inferential tests are underpowered (e.g., linear R2 ~ 0.03; logistic pseudo-R2 ~ 0.04) so effect-size estimates should be viewed as preliminary rather than confirmatory.
Embedding-based clustering yields seven clusters with 11.2% noise and modest agreement with LDA (ARI=0.068; NMI=0.163). Results suggest that brief motivation responses encode promising signals that could support early mentoring in rigorous STEM pipelines, while highlighting the need for larger, pre-registered studies.
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Computational Physics · Cross-listed from cond-mat.str-el
Data-Driven Bath Fitting for Hamiltonian-Diagonalization Dynamical Mean-Field Theory
Taeung Kim, Jeongmoo Lee and Ara Go
We propose a machine-learning-based initialization method to overcome the nonlinear bath-fitting bottleneck in
Hamiltonian-diagonalization-based dynamical mean-field theory (HD-DMFT). In HD-DMFT, the continuous hybridization function is approximated by a finite set of bath-site energies and hybridization amplitudes, determined by minimizing a highly non-convex multivariable cost function. As the number of bath sites increases, the optimization becomes more sensitive to the initial guess and more prone to suboptimal local minima, which can slow or destabilize the DMFT self-consistency loop. We reformulate bath fitting as a supervised regression problem and train a kernel ridge regression model to predict near-optimal discrete bath parameters directly from the target hybridization function on the Matsubara axis. To ensure physical relevance and data diversity, we construct the training
dataset from tight-binding
Hamiltonians of layered-perovskite-like ruthenate models across systematically deformed structures, instead of relying on naive random parameter sampling, and obtain high-quality labels through fully converged conventional bath fitting. Time-reversal
symmetry is explicitly incorporated in both feature and target representations to reduce effective dimensionality and enforce physical consistency.
Benchmarks in the non-interacting limit show that the learned initialization systematically reduces the initial fitting error, decreases the number of conjugate-gradient iterations, and improves robustness against local minima over a wide range of bath sizes. We further demonstrate transferability to interacting DMFT calculations for $\mathrm{Sr_{2}RuO_{4}}$ solved with an adaptive-truncation
impurity solver, where the ML initialization yields consistently faster convergence than a
symmetry-preserving heuristic baseline while preserving the final fitted solution.
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Optics
Profiling THz Beams With Off-Label Use of Infrared Microbolometric Cameras
Gabriel Nagamine, Carlo Vicario, Tariq Leinen et al. (+6 more)
Visualizing the spatial profile of light beams is essential for evaluating irradiance, characterizing beam quality, and achieving precise
alignment. In the optical spectral range, this is readily performed using silicon-based CCD and CMOS cameras. In the terahertz (THz) range, however, it typically requires specialized detectors with prohibitive costs. Here, we show that an infrared (IR) camera can be used outside of its labeled specifications to achieve similar performance as a dedicated microbolometric THz camera, at under 1% of the THz camera's cost. We compared the cameras by characterizing THz beam profiles from two sources: a pulsed broadband THz beam produced through optical rectification in organic
crystals, and a narrowband quasi-continuous-wave (quasi-CW) THz beam emitted by a
quantum cascade
laser. For the broadband THz radiation, the beam width measured by the two cameras differed by only ~ 6%, well within the pixel resolution limit, and in the narrowband quasi-CW case by just ~ 1.3%. Additionally, the IR camera exhibits a lower minimum detectable power (down to 1.5 THz) than the THz camera, while also maintaining a linear and polarization-independent responsivity. These results expand the applicability of conventional IR cameras to the THz range, suggesting that they will become routine tools for high-fidelity THz beam diagnostics and imaging in scientific and industrial applications.
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All authors Gabriel Nagamine, Carlo Vicario, Tariq Leinen, Guy Matmon, Marco Raffa, Mattias Beck, Giacomo Scalari, Adrian L. Cavalieri, Flavio Giorgianni
Atmospheric and Oceanic Physics
Deep Learning Based Monthly Temperature Prediction for Jilin Province: A Multi Model Comparative Study 2000 2026
Xingyue Deng and Xuechen Liang
Jilin Province, a core commercial grain production base in China with a mid-temperate continental monsoon
climate and significant temperature fluctuations, relies heavily on temperature for agricultural production and ecological security. Existing temperature prediction studies focus mostly on national/southeastern coastal regions, with few targeting Jilin's specific climatic characteristics, and most models fail to integrate local temperature's spatiotemporal differentiation and seasonal periodicity, limiting prediction accuracy. Using 1 km $\times$ 1 km monthly mean temperature raster data (2000–2024) of Jilin Province, we analyzed regional temperature's spatiotemporal variation and constructed a multi-model comparison system including four deep learning models (LSTM, GRU, BiLSTM,
Transformer) and five traditional machine learning models (Ridge/Lasso Regression, SVR, Random Forest, Gradient Boosting). Model performance was evaluated via RMSE, MAE, and $R^2$. Results show Jilin's temperature has obvious latitudinal zonal distribution, significant warming trend, strong seasonal periodicity, and high temporal autocorrelation. The LSTM model achieved optimal performance (test set RMSE=2.26 $^\circ$C, MAE=1.83 $^\circ$C, $R^2$=0.9655), outperforming traditional models and
Transformer. Predictions for 2025–2026 indicate stable seasonal temperature fluctuations with an annual mean of ~4.9 $^\circ$C. This study enriches mid-latitude cold region temperature prediction research, verifies LSTM's applicability for Jilin's monthly temperature prediction, and provides scientific support for agricultural planning, frost disaster warning, and extreme temperature risk prevention.
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Plasma Physics
Friction-induced scale-selection in the extended Cahn-Hilliard model for zonal staircase
M. Leconte and T.S. Hahm
In this work, we describe a possible mechanism to set the radial scale of zonal flows, which may be applicable to the $E \times B$ staircase found in the global full-f simulations such as [G. Dif-Pradalier et al. Phys. Rev. Lett. 114, 085004 (2015)]. 1D numerical simulation results of the Cahn-Hilliard model - extended to include zonal flow friction - can be understood from a heuristic nonlinear analysis. The staircase step-size $\Delta$ is found to decrease as the dimensionless zonal flow friction $\mu$ increases. It scales like $\Delta \sim \log \mu^\alpha$, with $\alpha \simeq -0.41$, up to a constant offset.
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Atmospheric and Oceanic Physics
Koopman Analysis of Sea Surface Temperature with a Signature Kernel
Nozomi Sugiura, Satoshi Osafune and Shinya Kouketsu
We develop a trajectory-based Koopman method for sea surface temperature (SST) that lifts annual SST segments using a signature kernel – a reproducing kernel
Hilbert space (RKHS) kernel that compares paths via iterated-integral features – and learns the one-year shift operator. By operating on annual trajectory segments rather than instantaneous fields, the method encodes finite-time history, which helps capture
memory effects in SST-only
evolution. The resulting operator improves out-of-sample multi-year forecast skill relative to a climatology baseline and reveals coherent spectral modes. We implement the approach via kernel extended dynamic mode decomposition (EDMD) on signature-kernel Gram
matrices, yielding a single pipeline for
forecasting and spectral diagnostics of high-dimensional SST dynamics.
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Applied Physics
Exact Solutions to Acoustoelectric Interactions in Arbitrary Geometries
William W. Roberts and Matt Eichenfield
Acoustoelectric interactions occur when free carriers in a
semiconductor interact with the fields of an acoustic wave in a piezoelectric medium. These interactions can amplify acoustic waves, as well as give rise to extremely large phononic nonlinearities and strong non-reciprocal effects. The field of acoustoelectric devices is currently dependent on analytical and perturbative solutions for the two simplest arrangements of piezoelectric-
semiconductor materials. While these canonical models have allowed the field to advance substantially, new geometries are arising that do not satisfy assumptions integral to these models. These assumptions include the treatment of the interactions between the acoustic fields and free carriers as weak, the neglect of the tensorial nature of the material properties, the omission of the spatial variations in the
phonons' electric field profiles, and the disregard of elastic coupling across material boundaries, among others. We develop, for the first time, a finite element method (FEM) model to solve for acoustoelectric interactions in arbitrary geometries that avoids making the assumptions of the canonical models. We verify the FEM model using results for amplification, dispersion, and non-reciprocity obtained from the canonical models in their regime of validity. We then examine the acoustoelectric effect in two geometries not covered by the canonical models: a thin piezoelectric film placed on a
semiconductor substrate and a fully 2D waveguide under a thin
semiconductor layer. This work lays the foundation for accurate modeling of arbitrary acoustoelectric geometries such as those currently being developed for all-acoustic
radio frequency (RF) signal processing, acoustoelectrically enhanced photonic devices, and
quantum acoustoelectric devices.
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Data Analysis and Statistics · Cross-listed from cs.IT
Physics-Aware, Shannon-Optimal Compression via Arithmetic Coding for Distributional Fidelity
Cristiano Fanelli
Assessing whether two
datasets are distributionally consistent is central to modern scientific analysis, particularly as generative artificial intelligence produces synthetic data whose fidelity must be validated against real observations in increasingly high-dimensional settings. Existing approaches are typically relative: they determine whether one
dataset is more consistent with a reference than another, but do not provide a physically grounded absolute standard for fidelity. We propose an information-theoretic approach in which lossless compression via arithmetic coding provides an operational measure of
dataset fidelity under a physics-informed probabilistic representation.
Datasets sharing the same underlying physical correlations admit comparable optimal descriptions, while discrepancies-arising from miscalibration, mismodeling, or bias-manifest as an irreducible excess in codelength relative to the Shannon-optimal limit defined by the physics itself. This excess codelength defines an absolute fidelity metric, quantified directly in bits. Unlike conventional measures, which lack an intrinsic scale, zero excess provides a well-defined and physically meaningful target corresponding to consistency with the underlying distribution. We show that this metric is global, interpretable, additive across components, and asymptotically optimal, with differences in codelength corresponding to differences in expected negative log-likelihood under a common reference model. As a byproduct, our approach achieves improved compression relative to standard general-purpose
algorithms such as gzip. These results establish arithmetic coding not merely as a compression tool, but as a measurement instrument for absolute, physics-grounded assessment of distributional fidelity.
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Computational Physics · Cross-listed from cs.CE
Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi et al. (+3 more)
Physics-informed neural networks (PINNs) have emerged as a promising mesh-free paradigm for solving partial differential equations, yet adoption in science and engineering is limited by slow training and modest accuracy relative to modern numerical solvers. We introduce the Sequential Correction
Algorithm for Learning Efficient PINN (Scale-PINN), a learning strategy that bridges modern physics-informed learning with numerical
algorithms. Scale-PINN incorporates the iterative residual-correction principle, a cornerstone of numerical solvers, directly into the loss formulation, marking a paradigm shift in how PINN losses can be conceived and constructed. This integration enables Scale-PINN to achieve unprecedented convergence speed across PDE problems from different physics domain, including reducing training time on a challenging fluid-dynamics problem for state-of-the-art PINN from hours to sub-2 minutes while maintaining superior accuracy, and enabling application to representative problems in aerodynamics and urban science. By uniting the rigor of numerical methods with the flexibility of deep learning, Scale-PINN marks a significant leap toward the practical adoption of PINNs in science and engineering through scalable, physics-informed learning. Codes are available at https://github.com/chiuph/SCALE-PINN.
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All authors Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi, Chang Wei, Yuchen Fan, Yew-Soon Ong
Plasma Physics
Three Dimensional Multiphysics Modelling of Helicon Wave Heating and Antenna Plasma Coupling for Boundary Density Control in Toroidal Fusion Plasmas
Hua Zhou, Lei Chang, GuoSheng Xu et al. (+5 more)
Active control of scrape off layer density is emerging as a critical requirement for improving ion cyclotron resonance heating and enabling high performance steady state operation in future
magnetic confinement fusion devices. Helicon wave excitation offers a promising physics based approach to generating high density boundary
plasmas with high ionization efficiency and low
impurity release. In this work, we develop THEMIS code, a fully three dimensional (3D) multiphysics model of helicon wave propagation and power deposition in a toroidal fusion relevant configuration, employing a finite temperature thermal dielectric tensor. The code quantifies the relative contributions of Doppler shifted cyclotron damping, anomalous Doppler damping, collisional damping, and Landau damping, and demonstrates that slow wave propagation and electron Landau damping dominate the accessible heating regime in Helimak device. A comparative study of four planar
antenna geometries under the present protruding window configuration shows that
geometric cutoffs and SOL density gradients severely limit power penetration into the core accessible region. To address this constraint, we introduce a recessed window launch scheme that positions the dielectric window inside the
vacuum vessel and perform systematic parameter scans of window position,
antenna geometry, and installation orientation. From these analyses, we identify the key physics driven principles governing efficient helicon wave coupling: the importance of open
circuit termination, maximized strap length and width, controlled inter turn spacing, and sufficient clearance from metallic walls to avoid near field suppression. Guided by these principles, we designed an optimized racetrack spiral
antenna that increases coupling efficiency by more than an order of magnitude compared with conventional short circuited rectangular spiral
antenna.
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All authors Hua Zhou, Lei Chang, GuoSheng Xu, YiWei Zhang, Matthew Hole, Dan Du, ZhiSong Qu, MuQuan Wu
Optics · Cross-listed from cs.CV
Physics-informed Active Polarimetric 3D Imaging for Specular Surfaces
Jiazhang Wang, Hyelim Yang, Tianyi Wang et al. (+1 more)
3D imaging of specular surfaces remains challenging in real-world scenarios, such as in-line inspection or hand-held scanning, requiring fast and accurate measurement of complex geometries. Optical metrology techniques such as deflectometry achieve high accuracy but typically rely on multi-shot acquisition, making them unsuitable for dynamic environments.
Fourier-based single-shot approaches alleviate this constraint, yet their performance deteriorates when measuring surfaces with high spatial frequency structure or large
curvature. Alternatively, polarimetric 3D imaging in computer
vision operates in a single-shot fashion and exhibits robustness to
geometric complexity. However, its accuracy is fundamentally limited by the orthographic imaging assumption. In this paper, we propose a physics-informed deep learning framework for single-shot 3D imaging of complex specular surfaces. Polarization cues provide orientation priors that assist in interpreting
geometric information encoded by structured illumination. These complementary cues are processed through a dual-encoder architecture with mutual feature modulation, allowing the network to resolve their nonlinear coupling and directly infer surface normals. The proposed method achieves accurate and robust normal estimation in single-shot with fast inference, enabling practical 3D imaging of complex specular surfaces.
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All authors Jiazhang Wang, Hyelim Yang, Tianyi Wang, Florian Willomitzer
Instrumentation and Detectors
Cold Neutron Imaging and Efficiency Measurements with a Boron-10 Coated Double-GEM Detector
WooJong Kim, DongHyun Kim, Minjae Kwon et al. (+6 more)
A ${}^{10}\mathrm{B}$-coated double-GEM
neutron detector (BGEM) was developed as a ${}^{3}\mathrm{He}$-free cold-
neutron beamline detector using a single $\mathrm{B}_{4}\mathrm{C}$ converter cathode and a 512-channel APV25 orthogonal-strip readout over an active area of $10 \times 10~\mathrm{cm}^{2}$. The detector was tested at the HANARO Bio-REF beamline with a monochromatic $4.5~\mathring{\mathrm{A}}$ beam ($E_{n}=4.03~\mathrm{meV}$). The absolute detection efficiency relative to a ${}^{6}\mathrm{Li}$-based Ce:LiCAF reference detector was $\varepsilon_{\mathrm{BGEM}}=(8.69 \pm 0.20)\%$ (stat.). The pulse-height spectrum was qualitatively consistent with Geant4 energy-deposition simulations, and Cd-mask imaging yielded a Gaussian-equivalent edge-spread width of $\sigma = 555 \oplus 102~\mu\mathrm{m}$. These results establish a cold-
neutron beamline
benchmark for a single-converter BGEM detector with full-strip APV25 readout.
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All authors WooJong Kim, DongHyun Kim, Minjae Kwon, Jason Sang Hun Lee, Hyupwoo Lee, Inkyu Park, Donghyun Song, Inseok Yoon, Myeonghun Choi
Physics and Society
Emergence of opinion splits in the Sznajd model with latency
Ryan W. Salatti and André M. Timpanaro
In the modelling of social systems, opinion latency is the idea that once an
agent changes its opinion, there will be a period of time where it is immune to other changes. When added to the voter model this leads to a situation where no matter how low the latency is or how many opinions are considered, all opinions end up in a coexistence where they are equally represented. In this work, we examine what happens when latency is added to the Sznajd model. What we find is that for low latency, the model behaves roughly like it does in the absence of latency, where one opinion will always eventually dominate. For high latency, the possibility for a symmetric coexistence of 2 opinions arises, but contrary to the voter model, a coexistence of more than 2 opinions is never stable. We provide evidence of this phenomenon with computer simulations of the model in Barabási-Albert networks, together with a mean field treatment that is able to capture the observed behavior. We argue that this could hint at an explanation for the prevalence of two opinion splits in the real world.
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Atomic Physics
Optimized Phase Masks for Absorption of Ultra-Broadband Pulses by Narrowband Atomic Ensembles
L. B. A. Mélo, Daniel Felinto and Marcio H. G. de Miranda
By combining genetic
algorithm and a spatial light modulator we theoretically analyse how to improve a two-
photon cascade absorption in atomic ensembles, inspecting the impact of various configurations and parameters in the optimized phase mask. At low atomic densities, we compare the cases of sequential transitions with the two
photons coming from the same pulse or from two different pulses. For the former, we predict an enhancement by a factor of $9.5$, similar to what was previously reported in the literature [Phys. Rev. Lett. 86, 47 (2002)]. For the later, on the other hand, we obtain an enhancement factor of $26$ times. This absorption of two
photons by different pulses is of particular interest for the storage of ultra-broadband single
photons by atomic ensembles, in which case the second
photon would come from a control pulse. We investigate this process as a function of the atomic density, demonstrating enhancements by factors up to 3 for the two-
photon absorption after propagating through large optical depths. However, for the experimental conditions considered in the previous work by Carvalho {et al.} [Phys. Rev. A 101, 053426 (2020)], in terms of control power and optical depths, we show that this enhancement in two-
photon absorption would still result in just a modest increase of the absorption of a weak probe pulse.
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Fluid Dynamics
A unified SPH framework for shell-related interactions
Dong Wu, Shuaihao Zhang, Weiyi Kong et al. (+1 more)
A unified Smoothed Particle Hydrodynamics (SPH) framework is proposed to simulate interaction dynamics involving thin shells modeled by a reduced-dimensional, single-layer particle discretization, as opposed to full-dimensional SPH solids. The framework encompasses one-sided fluid-shell interactions, with the fluid present on only one side of the shell, as well as solid-shell, shell-shell, and shell-self interactions The study introduces a novel concept of imaginary shell contact particles, generated by projecting real shell particles along the local normal direction within the cut-off radius of the fluid particle, thereby mapping this reduced-dimensional shell model into a full-dimensional representation. With the volume of the imaginary particles defined based on the local shell
curvature, the projection preserves kernel completeness for fluid-shell interactions while leaving the fluid-structure interaction (FSI) dynamics unchanged, such that the fluid-shell coupling
algorithm is the same as in standard fluid-solid coupling. In addition, a particle-to-particle contact model for solid-solid interactions is developed by analogy to fluid dynamics: a contact density is computed using a fluid-style density initialization, and the resulting contact forces follow a momentum-equation-inspired formulation. Combined with the projection strategy, this contact formulation is directly extended to efficiently handle shell-related contact problems. The proposed method is validated using a series of
benchmark tests, demonstrating stable and accurate performance across diverse interaction scenarios.
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All authors Dong Wu, Shuaihao Zhang, Weiyi Kong, Xiangyu Hu
Optics
Superresolution technique beyond the diffraction limit under a structured beam via different optical nanostructures
Subhankar Roy, Jianping Hu and M Ummal Momeen
To overcome the limit of diffraction while achieving the superresolution technique, solid immersion lenses are the key optical elements for data storage and nanophotonics applications. Recent demonstrations have shown how different nanostructures (such as elliptical solid immersion lenses) are used in diverse fields of increasing resolution in the presence of a structured Gaussian beam. By applying twisted beams such as angular momentum beams (Laguerre- Gaussian) and spatial higher-order Gaussian beams (Hermite- Gauss), we can attain a sharp near-field focal spot pattern, which is considerably better than the conventional solid immersion lens structure in ~mm scale specifically for imaging beyond diffraction limit. Our computation results present a resolution of ~27 nm under a specific Hermite -Gauss mode illumination on a pyramidal shape nanolens structure. By numerical simulations, tolerance has been confirmed with a slight variation in beam size and geometrical modification to make the model compatible with fabrication errors. This narrow bandwidth intensity distribution can be utilized for scanning the sample with higher resolution, especially in the field of
quantum technology.
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