Computational Physics · Cross-listed from quant-ph
Sampling two-dimensional isometric tensor network states
Alec Dektor, Eugene Dumitrescu and Chao Yang
Sampling a
quantum system's underlying probability distributions is an important computational task, e.g., for
quantum advantage experiments and
quantum Monte Carlo
algorithms. Tensor networks are an invaluable tool for efficiently representing states of large
quantum systems with limited
entanglement.
Algorithms for sampling one-dimensional (1D) tensor networks are well-established and utilized in several 1D tensor network methods. In this paper we introduce two novel sampling
algorithms for two-dimensional (2D) isometric tensor network states (isoTNS) that generalize existing 1D tensor networks sampling
algorithms. Our first proposed
algorithm performs independent sampling and yields a single configuration together with its associated probability. The second
algorithm employs a greedy search strategy to identify $K$ high-probability configurations and their corresponding probabilities. Numerical results demonstrate the effectiveness of these
algorithms across
quantum states with varying
entanglement and system size.
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Chemical Physics · Cross-listed from cs.DC
Enabling AI Deep Potentials for Ab Initio-quality Molecular Dynamics Simulations in GROMACS
Andong Hu, Luca Pennati, Stefano Markidis et al. (+1 more)
State-of-the-art AI deep potentials provide ab initio-quality results, but at a fraction of the computational cost of first-principles
quantum mechanical calculations, such as density functional theory. In this work, we bring AI deep potentials into GROMACS, a production-level Molecular Dynamics (MD) code, by integrating with DeePMD-kit that provides domain-specific deep learning (DL) models of interatomic potential energy and force fields. In particular, we enable AI deep potentials inference across multiple DP model families and DL backends by coupling GROMACS Neural Network Potentials with the C++/CUDA backend in DeePMD-kit. We evaluate two recent large-atom-model architectures, DPA2 that is based on the
attention mechanism and DPA3 that is based on GNN, in GROMACS using four ab initio-quality
protein-in-water
benchmarks (1YRF, 1UBQ, 3LZM, 2PTC) on NVIDIA A100 and GH200 GPUs. Our results show that DPA2 delivers up to 4.23x and 3.18x higher throughput than DPA3 on A100 and GH200 GPUs, respectively. We also provide a characterization study to further contrast DPA2 and DPA3 in throughput,
memory usage, and kernel-level execution on GPUs. Our findings identify kernel-launch overhead and domain-decomposed inference as the main optimization priorities for AI deep potentials in production MD simulations.
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All authors Andong Hu, Luca Pennati, Stefano Markidis, Ivy Peng
Atomic Physics
Intrinsic atomic calibration of oscillating magnetic fields in ULF and VLF bands
Zak Johnston, Paul F. Griffin, Erling Riis et al. (+3 more)
We present a method for absolute calibration of received
radio-frequency in the ultra low frequency (ULF), and very low frequency (VLF) range. This is achieved with the use of a
radio frequency optically pumped magnetometer (RF-OPM). We describe a method using an optically pumped sample where the RF broadening of the Cs
magnetic resonance allows the magnitude of the received field to be calibrated against the ground-state gyromagnetic ratio of the Cs atoms. This frequency-based calibration avoids the
geometric and electrostatic response functions that affect inductive sensors, such as fluxgates, search coils, and SQUID magnetometers. We demonstrate calibration of
magnetic measurement using oscillating
magnetic fields in the 300 Hz - 20 kHz range and a sensor noise floor of 15 fT.Hz-1/2. This
radio-frequency sensor may be used as a widely tunable narrowband receiver for communication, ranging, or penetrative conductivity imaging.
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All authors Zak Johnston, Paul F. Griffin, Erling Riis, Dominic Hunter, Marcin Mrozowski, Stuart J. Ingleby
Biological Physics · Cross-listed from cs.LG
Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics
Nima Shoghi, Yuxuan Liu, Yuning Shen et al. (+3 more)
Molecular dynamics (MD) simulations remain the gold standard for studying
protein dynamics, but their computational cost limits access to biologically relevant timescales. Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation due to architectural constraints, error accumulation, and inadequate modeling of spatio-temporal dynamics. We present
STAR-MD (Spatio-Temporal Autoregressive Rollout for Molecular Dynamics), a scalable SE(3)-equivariant
diffusion model that generates physically plausible
protein trajectories over microsecond timescales. Our key innovation is a causal
diffusion transformer with joint spatio-temporal
attention that efficiently captures complex space-time dependencies while avoiding the
memory bottlenecks of existing methods. On the standard ATLAS
benchmark,
STAR-MD achieves state-of-the-art performance across all metrics–substantially improving conformational coverage, structural validity, and dynamic fidelity compared to previous methods.
STAR-MD successfully extrapolates to generate stable microsecond-scale trajectories where baseline methods fail catastrophically, maintaining high structural quality throughout the extended rollout. Our comprehensive evaluation reveals severe limitations in current models for long-horizon generation, while demonstrating that
STAR-MD's joint spatio-temporal modeling enables robust dynamics simulation at biologically relevant timescales, paving the way for accelerated exploration of
protein function.
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All authors Nima Shoghi, Yuxuan Liu, Yuning Shen, Rob Brekelmans, Pan Li, Quanquan Gu
Atomic Physics
Theoretical investigation of transition data of astrophysical importance in neutral sulphur
W. Li, A. M. Amarsi and P. Jönsson
Accurate and comprehensive atomic data are essential for the modelling of
stellar spectra. Uncertainties in the oscillator strengths of specific lines used for abundance analyses directly translate into uncertainties in the derived elemental abundances; incomplete or biased atomic data sets can impart significant errors in non-local thermodynamic
equilibrium (non-LTE) modelling. Theoretical calculations of atomic data are therefore crucial to supplement the limited experimental results. In this work, we present extensive atomic data, including oscillator strengths, transition rates, and lifetimes for 1730 electric-dipole (E1) transitions among 107 levels in neutral sulphur (S I) using the multi-configuration Dirac-Hartree-Fock (MCDHF) and relativistic-configuration-interaction (RCI) methods. These levels belong to the configurations $\mathrm{3p^3np (n=3-7)}$, $\mathrm{3p^3nf (n=4,5)}$, $\mathrm{3s3p^5}$, $\mathrm{3p^3ns (n=4-7)}$, and $\mathrm{3p^3nd (n=3-6)}$. The accuracy of the computed transition rates is assessed by combining the comparison of the differences in transition rates between the Babushkin and Coulomb gauges with a cancellation-factor (CF) analysis. Approximately 16% of the ab initio results achieved an accuracy classification of A-B, corresponding to uncertainties within 10%, as defined by the Atomic Spectra Database of the National Institute of Standards and Technology (NIST ASD). Applying a fine-tuning technique was found to significantly improve the accuracy of the results in the Coulomb gauge, thereby improving the consistency between the Babushkin and Coulomb gauges; about 24% of the fine-tuned transition data are assigned to the accuracy classes A-B.
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Chemical Physics
The Entropic Barrier around the Conical Intersection Seam
Johannes C. B. Dietschreit, Sebastian Mai and Leticia González
Conical intersections (CIs) are seen as the main mediators of nonadiabatic transitions; yet, mixed
quantum-classical (MQC) simulations that treat
nuclei as classical point particles rarely, if ever, sample geometries with exactly degenerate electronic energies. Here we show that this behavior arises from a fundamental statistical-mechanical constraint on classical
nuclear motion. Using a linear vibronic coupling model, we derive the free energy along the adiabatic energy gap and demonstrate analytically that as the gap approaches zero, the free energy diverges around the CI seam. Molecular dynamics simulations of the methaniminium cation on the S$_1$ surface confirm this prediction: trajectories can approach regions with small adiabatic gaps, but never reach the CI seam, even if the CI corresponds to a region of lowest potential energy. These results clarify why MQC methods successfully capture nonadiabatic behavior without sampling exact degeneracies and agree with recent findings that classical trajectories can sense the presence of CIs without visiting them.
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Optics
A T-matrix database to promote information-driven research in nanophotonics
Nigar Asadova, Kaoutar Boussaoud, Jörg Meyer et al. (+2 more)
Information-driven methods from machine learning and artificial intelligence for exploring the optical response of metasurfaces and, more generally, photonic systems rely on well-annotated
datasets for training. For metasurfaces made from a periodic or aperiodic arrangement of scatterers, the primary information encoding their response is the optical properties of these individual scatterers. In the linear regime, that response is entirely contained in the transition or T-
matrix of the individual scatterer. However, despite the widespread use of these T-
matrices in exploring advanced photonic materials within the larger community, there is no common infrastructure for distributing them with consistent metadata and a standard representation. That would be important to avoid the repetitive, resource-intensive computation of these T-
matrices by researchers worldwide and to enable data-driven research. To overcome this limitation, we introduce the Daphona T-
matrix portal at https://tmatrix.scc.kit.edu/, a web-based platform for interactive searching, filtering, and exporting standardized data containing structure-property relations for a wide range of scatterers, as expressed by their T-
matrices. Besides introducing this infrastructure, we demonstrate how the available data enables addressing scientific questions in the broader context of information-driven research. The multiple illustrative examples in our contribution cover both surrogate forward models and inverse design models, and operate either directly on the T-
matrix or alternatively on optical observables of metasurfaces made from these scatterers.
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All authors Nigar Asadova, Kaoutar Boussaoud, Jörg Meyer, Frank Tristram, Carsten Rockstuhl
Atmospheric and Oceanic Physics
WADEPre: A Wavelet-based Decomposition Model for Extreme Precipitation Nowcasting with Multi-Scale Learning
Baitian Liu, Haiping Zhang, Huiling Yuan et al. (+4 more)
The heavy-tailed nature of precipitation intensity impedes precise precipitation nowcasting. Standard models that optimize pixel-wise losses are prone to regression-to-the-mean bias, which blurs extreme values. Existing
Fourier-based methods also lack the spatial localization needed to resolve transient convective
cells. To overcome these intrinsic limitations, we propose WADEPre, a wavelet-based decomposition model for extreme precipitation that transitions the modeling into the wavelet domain. By leveraging the Discrete Wavelet Transform for explicit decomposition, WADEPre employs a dual-branch architecture: an Approximation Network to model stable, low-frequency advection, isolating deterministic trends from statistical bias, and a spatially localized Detail Network to capture high-frequency
stochastic convection, resolving transient singularities and preserving sharp boundaries. A subsequent Refiner module then dynamically reconstructs these decoupled multi-scale components into the final high-fidelity forecast. To address optimization instability, we introduce a multi-scale curriculum learning strategy that progressively shifts supervision from coarse scales to fine-grained details. Extensive experiments on the SEVIR and Shanghai Radar
datasets demonstrate that WADEPre achieves state-of-the-art performance, yielding significant improvements in capturing extreme thresholds and maintaining structural fidelity. Our code is available at https://github.com/sonderlau/WADEPre.
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All authors Baitian Liu, Haiping Zhang, Huiling Yuan, Dongjing Wang, Ying Li, Feng Chen, Hao Wu
Plasma Physics
Statistical theory of electronic degrees of freedom in wave packet molecular dynamics
Daniel Plummer, Pontus Svensson, Wiktor Jasniak et al. (+3 more)
We derive statistical distributions for the degrees of freedom in wave packet molecular dynamics models. Specifically, a theory is developed for the width distributions of Gaussian wavepackets in both isotropic and anisotropic formulations. The resulting distribution functions show good agreement with molecular dynamics data under warm dense matter conditions, providing practical guidance for constraining the confining potential, an empirical parameter in the model. We also discuss how these distributions influence the resulting effective Coulomb interactions.
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All authors Daniel Plummer, Pontus Svensson, Wiktor Jasniak, Patrick Hollebon, Sam M. Vinko, Gianluca Gregori
Optics
Mie Voids as broadband directional light sources
Benjamin Reichel, Adrià Canós Valero, Mario Hentschel et al. (+2 more)
The Kerker effect arises from the interference between electric and
magnetic multipoles, enabling directional light scattering in nanophotonics. However, conventional dielectric and plasmonic nanoparticles can only act as Kerker sources in narrow spectral regions, limiting their applicability. Here, we show that the recently discovered Mie voids overcome this limitation by supporting a broadband generalized Kerker effect spanning the whole visible range. We investigate the optical response of Mie voids under both plane-wave and dipolar excitation. For plane waves, the voids preferentially scatter light in the forward direction. Under dipolar excitation, the resulting radiation emission towards the void and beyond is suppressed due to destructive interference between the dipole field with the directional scattered field of the void. These findings identify Mie voids as versatile broadband directional sources, opening pathways for
antenna design and energy harvesting at the nanoscale.
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All authors Benjamin Reichel, Adrià Canós Valero, Mario Hentschel, Harald Giessen, Thomas Weiss
Optics
Meta-optical processors for broadband complex-field image operations
Linzhi Yu, Jesse Pietila, Haobijam J. Singh et al. (+2 more)
All-optical image processing provides a fast and energy-efficient alternative to conventional electronic systems by directly manipulating optical wavefronts. However, metasurface-based optical processors reported to date are often limited in functionality, operating bandwidth, or input modality, which restricts their adaptability across different image processing tasks. Here, we demonstrate a broadband metasurface platform capable of performing diverse analog image processing operations on both amplitude- and phase-encoded inputs. This platform is realized using a single-layer dielectric metasurface designed through an end-to-end, task-driven inverse design framework. By tailoring the spatial-frequency components of incident image wavefronts, the metasurface implements analog operations such as edge detection and pattern recognition across a 200nm wavelength bandwidth in the visible spectrum. Furthermore, we develop a compact processor architecture that integrates imaging and computation within a reduced optical footprint. These results establish a flexible and compact metasurface-based optical processor with strong potential for integration into practical imaging and optical computing systems.
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All authors Linzhi Yu, Jesse Pietila, Haobijam J. Singh, Arttu Nieminen, Humeyra Caglayan
Accelerator Physics
Heat load measurements for the PIP-II pHB650 cryomodule
D. Porwisiak, M.J. White, V. Roger et al. (+11 more)
Phase-3 testing of the pHB650 cryomodule at the PIP-II Injector Test Facility was conducted to evaluate the effectiveness of heat load mitigations performed after earlier phases of testing and to continue pinpointing any sources of unexpectedly high heat loads.. The programme measured HTTS, LTTS, and 2 K isothermal/non-isothermal loads under "standard", "linac", and "simulated dynamic" operating modes, recording data both inside the cryomodule and across the bayonet can
circuits. Thermal-acoustic oscillations were eliminated by replacing the original G10 cooldown-valve stem with a stainless-steel stem fitted with wipers. A newly developed Python script automated acquisition of ACNET data, performed real-time heat-load calculations, and generated plots and tables that were posted to the electronic logbook within minutes, vastly reducing manual effort and accelerating feedback between SRF and cryogenics teams. Analysis showed that JT heat-exchanger effectiveness and temperature stratification in the two-phase and relief piping strongly influence the observed loads and helped isolate sources of excess heat. The campaign demonstrates that rigorous pre-test planning, real-time diagnostics, and automated reporting can improve both accuracy and efficiency, providing a template for future PIP-II cryomodule tests and for implementing targeted heat-load mitigations.
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All authors D. Porwisiak, M.J. White, V. Roger, J. Bernardini, B.J. Hansen, J.P. Holzbauer, J. Makara, J. Ozelis, D. Passarelli, S. Yoon, J. Subedi, S. Ranpariya, V. Patel, J. Dong
Computational Physics · Cross-listed from cond-mat.mtrl-sci
FluxNet: Learning Capacity-Constrained Local Transport Operators for Conservative and Bounded PDE Surrogates
Zishuo Lan, Junjie Li, Lei Wang et al. (+1 more)
Autoregressive learning of time-stepping operators provides an effective approach to data-driven partial differential equation (PDE) simulation, yet for conservation laws, they face a fundamental challenge: learned updates may violate global conservation over long rollouts. For the important subclass of mass-conservation-type equations, the problem is compounded by inherent physical bounds (e.g., nonnegativity or concentrations in [0,1]) whose violation further destabilizes predictions. We introduce FluxNet, which learns cumulative transport amounts representing the total conserved quantity redistributed between each
cell and a configurable neighborhood over the full surrogate interval. A conservative update guarantees exact discrete conservation by construction; modular capacity-constrained transport heads (L, U, and D) enforce lower bounds, upper bounds, or near-zero dual-bound violations through architectural design. Unlike flux-rate surrogates that require temporal integration and thus inherit CFL constraints, FluxNet involves no such integration; configurable transport neighborhoods enable large-timestep prediction at full spatial resolution. Ghost
cells extend the framework to non-periodic boundaries. Experiments on four
benchmarks (1D convection–
diffusion, 2D shallow water, 1D traffic flow, 2D Cahn–Hilliard) demonstrate exact conservation, structural bound preservation, architecture modularity, and superior stability over flux-rate surrogates at large temporal strides. The code is publicly available at: https://github.com/Lan-zs/FluxNet.
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All authors Zishuo Lan, Junjie Li, Lei Wang, Jincheng Wang
Computational Physics · Cross-listed from math.NA
A Flux-Correction Form of the Third-Order Edge-Based Scheme for a General Numerical Flux Function
Hiroaki Nishikawa
In this short note, we present a flux-correction form of the third-order edge-based scheme for the Euler equations that enables the direct use of a general flux function. The core idea is to replace, without loss of accuracy, the arithmetic average of the flux extrapolations by a general numerical flux evaluated at the edge midpoint, together with a correction term. We show that the proposed flux-correction form preserves third-order accuracy, provided that the general numerical flux is evaluated with the left and right states that are computed exactly for a quadratic function, which can be achieved effectively by the U-MUSCL scheme with {\kappa} = 1/2. Numerical results are presented to verify third-order accuracy with the HLLC and LDFSS flux functions on irregular tetrahedral grids.
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Chemical Physics · Cross-listed from cond-mat.stat-mech
Internal Trajectories and Observation Effects in Langevin Splitting Schemes
Bettina G. Keller
Langevin integrators based on operator splitting are widely used in molecular dynamics. This work examines Langevin splitting schemes from the perspective of their internal trajectories and observation points, complementing existing generator-based analyses. By exploiting merging, splitting, and cyclic permutation of elementary update operators, formally distinct schemes can be grouped according to identical or closely related trajectories. Accuracy differences arising from momentum updates and observation points are quantified for configurational sampling, free-energy estimates, and transition rates. While modern Langevin integrators are remarkably stable under standard simulation conditions, subtle but systematic biases emerge at large friction coefficients and time steps. These results clarify when accuracy differences between splitting schemes matter in practice and provide an intuitive framework for understanding observation effects.
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Geophysics
Propagating the prior from far to near offset: A self-supervised diffusion framework for progressively recovering near-offsets of towed-streamer data
Shijun Cheng and Tariq Alkhalifah
In marine towed-streamer seismic acquisition, the nearest hydrophone is often two hundred meter away from the source resulting in missing near-offset traces, which degrades critical processing workflows such as surface-related multiple elimination, velocity analysis, and full-waveform inversion. Existing reconstruction methods, like transform-domain interpolation, often produce kinematic inconsistencies and amplitude distortions, while supervised deep learning approaches require complete ground-truth near-offset data that are unavailable in realistic acquisition scenarios. To address these limitations, we propose a self-supervised
diffusion-based framework that reconstructs missing near-offset traces without requiring near-offset reference data. Our method leverages overlapping patch extraction with single-trace shifts from the available far-offset section to train a conditional
diffusion model, which learns offset-dependent statistical patterns governing event
curvature, amplitude variation, and wavelet characteristics. At inference, we perform trace-by-trace recursive extrapolation from the nearest recorded offset toward zero offset, progressively propagating learned prior information from far to near offsets. The generative formulation further provides uncertainty estimates via ensemble sampling, quantifying prediction confidence where validation data are absent. Controlled validation experiments on synthetic and field
datasets show substantial performance gains over conventional
parabolic Radon transform baselines. Operational deployment on actual near-offset gaps demonstrates practical viability where ground-truth validation is impossible. Notably, the reconstructed waveforms preserve realistic amplitude-versus-offset trends despite training exclusively on far-offset observations, and uncertainty maps accurately identify challenging extrapolation regions.
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Atmospheric and Oceanic Physics
Role of the ocean for fast atmospheric evolution revealed by machine learning
Bobby Antonio, Kristian Strommen and Hannah M. Christensen
There have recently been many efforts to create machine learnt atmospheric emulators designed to replace physical models. So far these have mainly focused on medium-range weather
forecasting, where these `Machine Learnt Weather Prediction' (MLWP) models can outperform leading operational
forecasting centres. However, because of this focus on shorter timescales, many of these emulators ignore the effects of the ocean, and take no ocean variables as inputs. We hypothesise that such MLWP models have learnt a best-guess of the
evolution of the atmosphere, by implicitly inferring ocean conditions from atmospheric states, with no access to ocean data. Turning this limitation into a strength, we use it as a means to study the role of the oceans on the
evolution of the atmosphere. By exploring how model forecast errors relate to properties of the air-sea interface, we infer what ocean information these atmospheric emulators are able to derive from atmospheric data alone, and what they cannot. This highlights the regions and processes through which the ocean independently influences the atmosphere on fast timescales. We perform this analysis for GraphCast, finding clear relationships between air-sea properties and the forecast errors over the ocean, including clear seasonal effects. We then explore what this reveals about GraphCast's internal representation of the ocean. In addition to understanding real-world ocean-atmosphere interactions, this analysis provides guidance for improving forecast skill and physical realism in MLWP models, and for informing how future machine learning models should use ocean information on short timescales.
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Physics and Society
Effect of higher-order interactions on noisy majority-rule dynamics with random group sizes
Roni Muslim, Jong-Min Park, Jihye Kim et al. (+1 more)
We study opinion dynamics with higher-order interactions, motivated by the fact that social influence often takes place in groups rather than only through pairwise contacts. We introduce a noisy majority-rule model on annealed hypergraphs with heterogeneous group sizes and investigate how the distribution of interaction sizes affects collective ordering and relaxation. Using analytical theory and Monte Carlo simulations, we show that group-size heterogeneity strongly shapes both the transition between ordered and mixed states and the associated time scales. In particular, broader and heavier-tailed distributions make ordering more robust by enhancing the effect of rare large-group events. They also modify the finite-size scaling of relaxation, producing a crossover from the standard logarithmic behavior to faster ordering in sufficiently broad ensembles. In the pure majority-rule limit, we further show that the exit probability near coexistence obeys a universal error-function scaling form controlled by a single structural parameter. Our results demonstrate that the full distribution of group sizes is a key determinant of nonequilibrium ordering in higher-order opinion dynamics.
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All authors Roni Muslim, Jong-Min Park, Jihye Kim, Rinto Anugraha NQZ
Optics
Wavefront Control and Intensity Modulation of Third Harmonic Generation in Nonlocal Metasurfaces
Yu Tian, Nuo Wang, Qi Liu et al. (+4 more)
Metasurfaces have emerged as a promising platform for integrated nonlinear optics. Nonlocal metasurfaces enable high nonlinear conversion efficiency, while the local ones can offer versatile wavefront control, yet achieving both within a single metasurface remains challenging. Here, using a nonlocal phase gradient metasurface, we firstly demonstrate efficient third harmonic generation (THG) with polarization-dependent wavefront control. Leveraging nonlocal nonlinear
geometric phase existing at resonance, the third harmonic light with distinct polarizations is deflected into $\pm$ 2nd and $\pm$ 4th diffraction orders, simultaneously achieving a conversion efficiency up to $1.45\times 10^{-4}$ under a pump intensity of $1 GW/cm^{2}$. Then, by introducing a secondary fundamental beam, whose generated third harmonic light overlaps with that of the first beam, the intensity modulation of THG is obtained. The THG efficiency can be tuned from $3.9 \times 10^{-9}$ to $5.5 \times 10^{-3}$ by varying the relative phase, polarization and intensity of two fundamental beams. Through utilizing the advantages of both local and nonlocal metasurfaces, our results effectively pave the way to on-chip nonlinear photonic devices and signal processing.
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All authors Yu Tian, Nuo Wang, Qi Liu, Shuyuan Xiao, Tingting Liu, Olivier J. F. Martin, Ying Gu
Fluid Dynamics
On Large Deformations of Oldroyd-B Drops in a Steady Electric Field
Sarika Shivaji Bangar and Gaurav Tomar
The deformation of viscoelastic drops under electric fields is central to applications in microfluidics, inkjet printing, and electrohydrodynamic manipulation of complex fluids. This study investigates the dynamics of an Oldroyd-B drop subjected to a uniform electric field using numerical simulations performed with the open-source solver Basilisk. Representative pairs of conductivity ratio ($\sigma_r$) and permittivity ratio ($\epsilon_r$) are selected from six regions ($PR_A^+$, $PR_B^+$, $PR_A^-$, $PR_B^-$, $OB^+$, and $OB^-$) of the $(\sigma_r, \epsilon_r)$ phase space. In regions where the first- and second-order deformation coefficients share the same sign ($PR_A^-$, $PR_B^-$, $OB^+$), deviations from Newtonian behavior are negligible. In $PR_A^+$, drops develop multi-lobed shapes above a critical electric capillary number, with elasticity reducing deformation and increasing the critical $Ca_E$ with Deborah number ($De$). In $PR_B^+$, drops form shapes with conical ends above the critical $Ca_E$, while steady-state deformation decreases with $De$ below this threshold, and critical $Ca_E$ shows non-monotonic variation. At high $Ca_E$ and $De$, transient deformation exhibits maxima and minima before reaching steady state, with occasional oscillations between spheroidal and pointed shapes. In $OB^-$, drops deform to oblate shapes and breakup above a critical $Ca_E$, with deformation magnitude increasing and critical $Ca_E$ decreasing with $De$; at low $Ca_E$ and high $De$, dimpling and positional oscillations are observed. These results elucidate viscoelastic-electric interactions and provide guidance for controlling drop behavior in practical applications.
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Computational Physics · Cross-listed from math.NA
Multigrid Poisson Solver for Complex Geometries Using Finite Difference Method
Deepak Gautam and Bhooshan Paradkar
We present an efficient numerical method, inspired by transformation optics, for solving the Poisson equation in complex and arbitrarily shaped geometries. The approach operates by mapping the physical domain to a uniform computational domain through coordinate transformations, which can be applied either to the entire domain or selectively to specific boundaries inside the domain. This flexibility allows both homogeneous (Laplace equation) and inhomogeneous (Poisson equation) problems to be solved efficiently using iterative or fast direct solvers, with only the material parameters and source terms modified according to the transformation. The method is formulated within a finite difference framework, where the modified material properties are computed from the coordinate transformation equations, either analytically or numerically. This enables accurate treatment of arbitrary
geometric shapes while retaining the simplicity of a uniform grid solver. Numerical experiments confirm that the method achieves second-order accuracy , and offers a straightforward pathway to integrate fast solvers such as multigrid methods on the uniform computational grid.
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Instrumentation and Detectors
Development and characterization of hybrid MCP-PMT with embedded Timepix4 ASIC used as pixelated anode
Riccardo Bolzonella, Jerome Alozy, Rafael Ballabriga et al. (+10 more)
We present a novel single-
photon detector based on a
vacuum tube incorporating a photocathode, a microchannel plate (MCP), and a Timepix4 CMOS ASIC functioning as a pixelated anode. Designed to handle
photon rates up to 1 billion per second across a 7 cm$^2$ active area, the detector achieves outstanding spatial and temporal resolutions of 5-10 $\mu$m and below 50 ps r.m.s., respectively. The Timepix4 ASIC comprises approximately 230,000 pixels, each integrating analog and digital front-end electronics. This enables data-driven acquisition and supports data transmission rates up to 160 Gb/s. External FPGA-based electronics manage both configuration and readout. In order to test the timing performance of the Timepix4 ASIC we performed preliminary characterization of an assembly bonded to a 100 $\mu$m thick n-on-p silicon sensor using a pulsed infrared
laser, which demonstrated a per-pixel timing resolution of 110 ps, with cluster-based averaging methods improving to below 50 ps. Six prototype detectors incorporating different MCP stack configurations and end-spoiling depths were produced by Hamamatsu Photonics. We report on their characterization, including dark count rates, gain, and spatial and timing resolutions, assessed both in laboratory conditions and during a test-beam campaign at CERN's SPS facility.
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All authors Riccardo Bolzonella, Jerome Alozy, Rafael Ballabriga, Nicolò Vladi Biesuz, Michael Campbell, Viola Cavallini, Angelo Cotta Ramusino, Massimiliano Fiorini, Edoardo Franzoso, Marco Guarise, Xavi Llopart Cudie, Gabriele Romolini, Alessandro Saputi
Applied Physics
Spin splitting torque enabled artificial neuron with self-reset via synthetic antiferromagnetic coupling
Badsha Sekh, Hasibur Rahaman, Ravi Shankar Verma et al. (+3 more)
Spintronic artificial
neurons are intriguing building blocks for energy efficient Neuromorphic Computing (NC). Nevertheless, most contemporary implementations rely on
symmetry breaking external in plane
magnetic fields (H_X) for
neuron operation, which limits scalability and hardware practicality. We experimentally demonstrate an altermagnet/Synthetic
Antiferromagnetic Coupling (SAF) based spintronic
neuron that uses out of plane
spin ({\sigma}_Z) polarized
spin-splitting torque to eliminate the necessity of an external H_X. The
neuron device also features intrinsic self-reset function facilitated by built-in exchange coupling. Furthermore, the proposed device is validated for Spiking Neural Network (SNN) applications by achieving test accuracies of 95.99% and 94.36% on the MNIST and N-MNIST
datasets, respectively. These results demonstrate the hardware feasibility and compatibility of the proposed spintronic
neuron, highlighting its potential for compact, scalable and energy-efficient neuromorphic computing systems.
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All authors Badsha Sekh, Hasibur Rahaman, Ravi Shankar Verma, Ramu Maddu, Kesavan Jawahar, S.N. Piramanayagam
Atomic Physics
Spin-orbit-dependent lifetimes of long-range Rydberg molecules
Michael Peper, Jakob Skrotzki, Martin Trautmann et al. (+2 more)
Long-range Rydberg molecules (LRMs) form when a highly excited Rydberg electron scatters from ground-state atoms inside its orbit, creating oscillatory, long-range potentials. We present a combined theoretical and experimental study of caesium dimers correlated to 402P3/2 Rydberg states, with an emphasis on decay via autoionisation (associative ionisation). Our model includes a relativistic treatment of electron-atom scattering with
spin-orbit coupling, the perturber's hyperfine structure, and coupling of vibrational levels to a continuum of short-range decay channels. Calculated potential-energy curves predict two families of wells: outer wells near the classical outer turning point supporting long-lived states, and inner wells at shorter range whose lifetimes are limited by tunneling and subsequent vibronic decay. Using photoassociation in an ultracold Cs gas and an analysis of pulsed-field-ionisation signals which are highly selective for the detection of molecules, we assign resonances by binding energy and measure lifetimes. The measured lifetimes of inner-well states increase systematically with increasing detuning and agree with calculated lifetimes; detection of Cs2+ product ions supports autoionisation as a dominant channel. We show that the lifetimes are strongly reduced by
spin-orbit interactions in the transient Cs-collision complex, which lift the near-degeneracy in Omega observed for states in the outer well and control the inner-well binding. The identified states also provide promising pathways to create ultracold molecules in ion-pair states.
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All authors Michael Peper, Jakob Skrotzki, Martin Trautmann, Ajith Sanjai C. V. Sivakumar, Johannes Deiglmayr
Optics
Tilt-to-length coupling metrology in the LISA mission
Frederic Cleva, Jean-Pierre Coulon and Marco Nardello
This paper describes a setup aimed at measuring the so-called Tilt-To-Length (TTL) coupling in the optical benches of the LISA mission. The TTL is the coupling of the angular jitter of any optical setup into the optical path length between its input and output pupils. This might be deleterious in
laser ranging experiments and must be evaluated for further compensation. The setup is made of two
laser beams, one features an angular jitter that mimics the input beam as seen from the jittering bench under test (BUT), the other is aligned to the optical axis of the BUT and provides a phase reference for the jittering beam. The induced phase variations between both beams detected at the BUT's output pupil gives access to the TTL coupling. The ”TTL probe” must feature a negligible residual TTL coupling which implies a micrometric accuracy in the centering of the setup pupil, the beams and the angular jitter associated pivot point. The setup integrates optical masks as a link between the setup optical reference frame to its mechanical reference frame, together with position
memories and servo-loops for the beam's
alignment. We show that the stability, the accuracy, and the noise floor of the setup is compliant with the LISA specifications for the TTL mitigation, although it makes use of off-the-shelf components and is operated in a standard environment laboratory.
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Physics and Society
Coordinated planning of European charging infrastructure and energy system for optimal V1G and V2G deployment
Francesco Sanvito, Francesco Lombardi and Stefan Pfenninger-Lee
Vehicle charging infrastructure targets in Europe currently rely on uniform
benchmarks and overlook the flexibility that could be provided by future smart charging (V1G) and vehicle to grid operation (V2G). To address this gap, we explicitly represent charging infrastructure and its costs in a cost minimizing European energy system model, allowing uncontrolled charging, V1G, and V2G to compete. We find that V1G captures the majority of system cost savings, amounting to 19 to 42 billion euros per year, or 2.2 to 4.5 percent, and substantially reduces infrastructure requirements. V2G provides more limited system cost savings of up to 2.5 billion euros per year, but generates substantial balancing
market revenues of around 6.4 billion euros per year. V2G deployment is most cost effective in photovoltaic dominated systems and in scenarios with limited grid expansion, where combined
solar and wind generation is relatively scarce. Charging infrastructure requirements vary across countries, reflecting either utilization maximization or flexibility maximization. This indicates that uniform EU targets risk overestimating infrastructure needs in some regions while constraining the benefits of smart charging in others.
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Optics
TF-UNet: Resolving Complex Speckles for Single-Shot Reconstruction of 512^2-Matrix Images Using a Micron-Sized Optical Fiber
Mingliang Xu, Fangyuan Li, Yuxin Leng et al. (+2 more)
Tapered optical fibers (TFs), with diameters gradually reduced from hundreds of microns to the micron scale, offer key advantages over conventional flat optical fibers (FFs), including uniform illumination, efficient long-range signal collection, and minimal invasiveness for applications in high-sensitivity biosensing, optogenetics, and photodynamic therapy. However, high-fidelity, single-shot imaging through a single TF remains underexplored due to intermodal coupling from the tapering geometry, which distorts output speckle patterns and poses challenges for image reconstruction using existing deep learning methods. Here, we propose a physics-inspired TF-UNet architecture that augments skip connections with hierarchical grouped-MLP fusion to effectively capture non-local, cross-scale dependencies caused by intermodal coupling in TFs. We experimentally validate our method on both FFs and TFs, demonstrating that TF-UNet outperforms standard U-Net variants in structural and perceptual fidelity while maintaining competitive PSNR at quadratic complexity. Our study offers a promising approach for deep learning-based imaging through micron-sized, ultrafine optical fibers, enabling scanning-free single-shot reconstruction on a 512x512 reconstruction
matrix, and further validating the framework on biologically meaningful neuronal and vascular
datasets for physically interpretable characterization.
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All authors Mingliang Xu, Fangyuan Li, Yuxin Leng, Ruxin Li, Fei He
Instrumentation and Detectors
Proton Energy Dependence of Radiation Induced Low Gain Avalanche Detector Degradation
Veronika Kraus, Marcos Fernandez Garcia, Luca Menzio et al. (+1 more)
Low Gain Avalanche Detectors (LGADs) are key components for precise timing measurements in high-energy physics experiments, including the High Luminosity upgrades of the current LHC detectors. Their performance is, however, limited by radiation induced degradation of the gain layer, primarily driven by acceptor removal. This study presents a systematic comparison of how the degradation evolves with different incident
proton energies, using LGADs from Hamamatsu Photonics (HPK) and The Institute of Microelectronics of Barcelona (IMB-CNM) irradiated with 18 MeV, 24 MeV, 400 MeV and 23 GeV
protons and fluences up to 2.5x10^15 p/cm2. Electrical characterization is used to extract the acceptor removal coefficients for different
proton energies, whereas IR TCT measurements offer complementary insight into the gain
evolution in LGADs after irradiation. Across all devices, lower energy
protons induce stronger gain layer degradation, confirming expectations. However, 400 MeV
protons consistently appear less damaging than both lower and higher energy
protons, an unexpected deviation from a monotonic energy trend. Conversion of
proton fluences to 1 MeV
neutron-equivalent fluences reduces but does not eliminate these differences, indicating that the standard Non-Ionizing Energy Loss (NIEL) scaling does not fully account for the underlying defect formation mechanisms at different energies and requires revision when considering irradiation fields that contain a broader spectrum of particle types and energies.
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All authors Veronika Kraus, Marcos Fernandez Garcia, Luca Menzio, Michael Moll
Optics
Full-span reversible space-time birefringence
Chenhui Yu, Guanyi Zhu, Mingliang Xu et al. (+5 more)
Birefringence, the polarization-dependent splitting of light in anisotropic
crystals, enables diverse optical phenomena and advanced functionalities such as optical communication, nonlinear optics, and
quantum optics. However, conventional methods for controlling birefringence typically rely on engineering the optical
crystal structure or applying external stimuli such as electric fields, mechanical stress or thermal variations, which are often constrained by limited tunability, challenges in integration with compact photonic devices or slow response time. Here, we introduce a new degree of freedom to manipulate the birefringence of light propagation in optical
crystals through programming the spatiotemporal spectral phase of the incident light wave. We demonstrate this approach achieves continuous tuning of birefringence across a spectrum more than 100 times broader than that achievable with conventional birefringence tuning, spanning from positive through zero to negative values, irrespective of the
crystal's optical sign and without inherent physical limitations. This unique optical behavior provides a versatile platform for investigating the complex dynamics of wave flow in anisotropic media, while the broad tunability of this space-time birefringence will spur innovations in ultrafast optical manipulation, optical computation, and
quantum information processing-applications that demand rapid and flexible device reconfiguration.
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All authors Chenhui Yu, Guanyi Zhu, Mingliang Xu, Fei He, Liwei Song, Ye Tian, Yuxin Leng, Ruxin Li
Biological Physics · Cross-listed from eess.AS
Short-wave admittance correction for a time-domain cochlear transmission line model
François Deloche, Morgan Thienpont and Sarah Verhulst
Transmission line (TL) models implemented in the time domain can efficiently simulate basilar-membrane (BM) displacement in response to transient or non-stationary sounds. By design, a TL model is well-suited for an one-dimensional (1-D) characterization of the traveling wave, but the real configuration of the cochlea also introduces higher-dimensional effects. Such effects include the focusing of the pressure around the BM and transverse viscous damping, both of which are magnified in the short-wave region. The two effects depend on the wavelength and are more readily expressed in the frequency domain. In this paper, we introduce a numerical correction for the BM admittance to account for 2-D effects in the time domain using autoregressive filtering and regression techniques. The correction was required for the implementation of a TL model tailored to the gerbil cochlear physiology. The model, which includes instantaneous nonlinearities in the form of variable damping, initially presented insufficient compression with increasing sound levels. This limitation was explained by the strong coupling between gain and frequency selectivity assumed in the 1-D nonlinear TL model, whereas cochlear frequency selectivity shows only a moderate dependence on sound level in small mammals. The correction factor was implemented in the gerbil model and made level-dependent using a feedback loop. The updated model achieved some decoupling between frequency selectivity and gain, providing 5 dB of additional gain and extending the range of sound levels of the compressive regime by 10 dB. We discuss the relevance of this work through two key features: the integration of both analytical and regression methods for characterizing BM admittance, and the combination of instantaneous and non-instantaneous nonlinearities.
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