Medical Physics
Spectral micro-CT for quantitative analysis of calcification in fibrocartilage
Vittoria Mazzini, Paolo Cardarelli, Andrew L. Coathup et al. (+6 more)
This work introduces a quantitative method for assessing calcification in fibrocartilage using spectral micro-computed
tomography ($\mu$CT). Tissue samples of hip acetabular labrum from patients with osteoarthritis and femoroacetabular impingement were imaged with a laboratory-based spectral $\mu$CT system equipped with a small-pixel
photon-counting detector. The detector operated with two energy thresholds, allowing the simultaneous acquisition of two CT
datasets at different X-ray energies. A material decomposition
algorithm accounting for the system's spectral response was applied to separate calcium- and water-like components, yielding three-dimensional visualization and quantification of calcified regions within intact paraffin-embedded samples. Unlike the conventional method for calcification assessment based on histology, this spectral \mu CT approach offers volumetric quantification of calcium structures without physical sectioning or staining. The method achieved a voxel size of 20 $\mu$m for samples up to ~3 cm, with a calcium detection threshold of ~0.3 g/cm$^3$ for structures down to 50 $\mu$m. Quantification accuracy was estimated to be 5% by using a calibration phantom. Further comparison with histology demonstrated the correct localization of calcium spatial distributions and a match in the calcium
crystal deposition score by providing non-destructive, quantitative 3D calcium maps of preserved tissue samples. This technique complements histology and could enhance the characterization of pathological fibrocartilage calcification in hip joint disorders.
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All authors Vittoria Mazzini, Paolo Cardarelli, Andrew L. Coathup, Eleonora Olivotto, Francesco Grassi, Enrico Tassinari, Simone Velardita, Angelo Taibi, Luca Brombal
Applied Physics
Investigation of the Physical Mechanism behind Retention Loss in FeFETs with MIFIFIS Gate Structure
Tao Hu, Zeqi Chen, Runhao Han et al. (+19 more)
A Metal-Gate Blocking Layer (GBL)- Ferroelectric-Tunnel Dielectric Layer (TDL)-Ferroelectric -Channel
Insulator (Ch.IL)-Si (MIFIFIS) structure is proposed to achieve a larger MW for applications in Fe-NAND. However, the large retention loss (RL) in the MIFIFIS structure restricts its application. In this work, we vary the physical thickness of the GBL and TDL, and conduct an in-depth analysis of the energy bands of the gate structure to investigate the physical mechanism behind the RL in FeFETs with the MIFIFIS structure. The physical origin of the RL is that the electric field direction across the TDL reduces the potential barrier provided by the ferroelectric near the silicon substrate. Based on the above physical mechanism, the RL can be reduced to 12% and 0.2% by redesigning the gate structure or reducing the pulse amplitude, respectively. Our work contributes to a deeper understanding of the physical mechanism behind the RL in FeFETs with the MIFIFIS gate structure. It provides guidance for enhancing the reliability of FeFETs.
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All authors Tao Hu, Zeqi Chen, Runhao Han, Xinpei Jia, Jia Yang, Mingkai Bai, Ruoyao Ji, Yajing Ding, Mengwei Zhao, Yuhan Li, Kaiyi Li, Wenbo Fan, Xianzhou Shao, Xiaoqing Sun, Kai Han, Jing Zhang, Yanrong Wang, Junshuai Chai, Hao Xu, Xiaolei Wang, Wenwu Wang, Tianchun Ye
Applied Physics · Cross-listed from cond-mat.stat-mech
Persson's Theory of Purely Normal Elastic Rough Surface Contact: A Tutorial Based on Stochastic Process Theory
Yang Xu, Xiaobao Li, Qi Chen et al. (+1 more)
Persson's theory of purely normal rough surface contact was developed two decades ago during the study of tire-road interaction, and gradually became one of the dominant approaches to study the solid-solid interaction between rough surfaces. Contrary to its popular applications in various cross-disciplinary fields, the fundamental study of Persson's theory of contact attracted little
attention from the tribology and contact mechanics communities. As far as the authors know, many researchers struggle to understand the derivation of the theory. Few attempts have been made to clarify the oversimplified derivation provided by Persson (Persson, 2001). The present work provides a detailed tutorial on Persson's theory, which does not simply follow the commonly adopted derivation initiated by Persson. A new derivation is given based on
stochastic process theory, assuming that the variation of the random contact pressure with respect to scale is a Markov process. We revisit the essential assumptions utilized to derive the
diffusion equation, boundary conditions, drift and
diffusion coefficients, and closed-form results. This tutorial can serve as a self-consistent introduction for solid mechanicians, tribologists, and postgraduate students who are not familiar with Persson's theory, or who struggle to understand it.
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All authors Yang Xu, Xiaobao Li, Qi Chen, Yunong Zhou
Atomic Physics · Cross-listed from quant-ph
Probing False Vacuum Decay and Bubble Nucleation in a Rydberg Atom Array
Yu-Xin Chao, Peiyun Ge, Zhen-Xing Hua et al. (+8 more)
In
quantum field theory (QFT), the "
vacuum" is not just empty space but the lowest-energy state of a
quantum field. If the energy landscape has multiple local minima, the local ground states are the false
vacuum (FV) which can tunnel towards the global ground state (true
vacuum, TV). This process exhibits signature akin to classical supercooled gas transitions and many-body tunneling in discrete
quantum systems. Here, we study the FV decay and bubble nucleation in a Rydberg atom ring. The $1/r^6$ van-der-Waals interactions and individual-site addressability allow us to explore physics beyond the standard Ising model. We observe that the FV decay rate decreases exponentially with the inverse of the
symmetry-breaking field, directly mirroring QFT predictions. Moreover, we demonstrate that even minor deviations from the ideal metastable state can cause a stark departure from this universal scaling law. Extending beyond short-time decay dynamics, we also examine resonant bubble nucleation, a feature distinctive to systems with discrete energy spectra. Our findings and methods open avenues for future studies of many-body tunneling in higher dimensions or more complex geometries.
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All authors Yu-Xin Chao, Peiyun Ge, Zhen-Xing Hua, Chen Jia, Xiao Wang, Xinhui Liang, Zongpei Yue, Rong Lu, Meng Khoon Tey, Xiao Wang, Li You
Applied Physics
Evaluating Power-to-Heat-to-Power Storage Potential in Wind- and Solar-Dominated Energy Markets
Alicia López-Ceballos, Alejandro Datas, Ignacio Antón et al. (+2 more)
This study assesses the role of Power-to-Heat-to-Power Storage (PHPS) systems, also known as Carnot batteries, in the national energy system of a wind-dominated (Denmark) and a
solar-dominated (Spain) region. Using the open-source PyPSA framework, we model sector-coupled electricity and heating systems, and evaluate individual and district heating configurations for heat provision at residential level, with and without PHPS waste heat recovery. Our results show that PHPS is most viable in individual heating systems, and in wind-dominated locations, where its low cost per energy capacity enables it to balance long-duration fluctuations. Moreover, waste heat recovery is essential to enhance PHPS competitiveness, allowing it to displace lithium-ion batteries. Conversely, PHPS is largely outcompeted in district heating systems, which favour low-cost centralized energy storage. Sensitivity analysis highlights that PHPS viability depends primarily on achieving low energy capacity costs and maintaining reasonable heat-to-power conversion efficiencies. Overall, while PHPS complements existing storage technologies by providing dispatchable electricity and heat, its potential adoption is highly context-dependent, influenced by
climate, heating system configuration, and competing storage costs.
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All authors Alicia López-Ceballos, Alejandro Datas, Ignacio Antón, Carlos Cañizo, Marta Victoria
Atomic Physics
QED nuclear recoil effect in helium isotope shift
Krzysztof Pachucki, Vojtěch Patkóš and Vladimir A. Yerokhin
We present a detailed investigation of the leading-order $m\alpha^5$ QED correction with inclusion of the finite-
nuclear-mass effects. Previously, this correction had been calculated within an expansion in the electron-
nucleus mass ratio $m/M$ up to the first order. In this work, we derive formulas for the $m\alpha^5$ QED contribution that are valid up to the second order in $m/M$, and perform its calculation for the $^3$He$-$$^4$He isotope shift, leading to an improved determination of the
nuclear charge-radius difference.
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Medical Physics · Cross-listed from cs.SD
Standard audiogram classification from loudness scaling data using unsupervised, supervised, and explainable machine learning techniques
Chen Xu, Lena Schell-Majoor and Birger Kollmeier
To address the calibration and procedural challenges inherent in remote audiogram assessment for rehabilitative audiology, this study investigated whether calibration-independent adaptive categorical loudness scaling (ACALOS) data can be used to approximate individual audiograms by classifying listeners into standard Bisgaard audiogram types using machine learning. Three classes of machine learning approaches - unsupervised, supervised, and explainable - were evaluated. Principal component analysis (PCA) was performed to extract the first two principal components, which together explained more than 50 percent of the variance. Seven supervised multi-class classifiers were trained and compared, alongside unsupervised and explainable methods. Model development and evaluation used a large auditory reference database containing ACALOS data (N = 847). The PCA factor map showed substantial overlap between listeners, indicating that cleanly separating participants into six Bisgaard classes based solely on their loudness patterns is challenging. Nevertheless, the models demonstrated reasonable classification performance, with logistic regression achieving the highest accuracy among supervised approaches. These findings demonstrate that machine learning models can predict standard Bisgaard audiogram types, within certain limits, from calibration-independent loudness perception data, supporting potential applications in remote or resource-limited settings without requiring a traditional audiogram.
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Optics
Exceptionally high Verdet constant in gold nanodisc arrays
Gajendra Mulay, Shraddha Choudhary, Ashwin A Tulapurkar et al. (+4 more)
Magneto-optical effects in non-
magnetic noble metals can be greatly enhanced by leveraging the in-plane Lorentz force at engineered plasmonic resonances. We demonstrate a 2D array of gold nanodiscs designed to host a hybrid resonance of localized plasmon and surface
lattice modes. The structure exhibits a Verdet constant of 1.98e6 deg/T.m, corresponding to a Faraday rotation of -0.15 deg at a 1 T
magnetic field. This Verdet constant represents a 15-fold enhancement over unpatterned gold and is highly competitive with many plasmon-enhanced diamagnetic nanostructures. These findings offer new opportunities for harnessing strong magneto-plasmonic effects in optoelectronic devices by patterning common non-
magnetic metals.
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All authors Gajendra Mulay, Shraddha Choudhary, Ashwin A Tulapurkar, Daria O Ignatyeva, Vladimir I Belotelov, Shriganesh S Prabhu, Venu Gopal Achanta
Classical Physics · Cross-listed from astro-ph.GA
The dynamical memory of tidal stellar streams: Joint inference of the Galactic potential and the progenitor of GD-1 with flow matching
Giuseppe Viterbo and Tobias Buck
Stellar streams offer one of the most sensitive probes of the Milky Way`s gravitational potential, as their phase-space morphology encodes both the
tidal field of the host
galaxy and the internal structure of their progenitors. In this work, we introduce a framework that leverages Flow Matching and Simulation-Based Inference (SBI) to jointly infer the parameters of the GD-1 progenitor and the global properties of the Milky Way potential. Our aim is to move beyond traditional techniques (e.g. orbit-fitting and action-angle methods) by constructing a fully
Bayesian, likelihood-free posterior over both host-
galaxy parameters and progenitor properties, thereby capturing the intrinsic coupling between
tidal stripping dynamics and the underlying potential. To achieve this, we generate a large suite of mock GD-1-like streams using our differentiable N-body code \textsc{Odisseo}, sampling self-consistent initial conditions from a Plummer sphere and evolving them in a flexible Milky Way potential model. We then apply conditional Flow Matching to learn the vector field that transports a base Gaussian distribution into the posterior, enabling efficient, amortized inference directly from stream phase-space data. We demonstrate that our method successfully recovers the true parameters of a fiducial GD-1 simulation, producing well-calibrated posteriors and accurately reproducing parameter degeneracies arising from progenitor-host interactions. Flow Matching provides a powerful, flexible framework for
Galactic Archaeology. Our approach enables joint inference on progenitor and
Galactic parameters, capturing complex dependencies that are difficult to model with classical likelihood-based methods.
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Computational Physics · Cross-listed from math.NA
Stable self-adaptive timestepping for Reduced Order Models for incompressible flows
Josep Plana-Riu, Henrik Rosenberger, Benjamin Sanderse et al. (+1 more)
This work introduces RedEigCD, the first self-adaptive timestepping technique specifically tailored for reduced-order models (ROMs) of the incompressible Navier-Stokes equations. Building upon linear stability concepts, the method adapts the timestep by directly bounding the stability function of the employed time integration scheme using exact spectral information of
matrices related to the reduced operators. Unlike traditional error-based adaptive methods, RedEigCD relies on the eigenbounds of the convective and diffusive ROM operators, whose computation is feasible at reduced scale and fully preserves the online efficiency of the ROM. A central theoretical contribution of this work is the proof, based on the combined
theorems of Bendixson and Rao, that, under linearized assumptions, the maximum stable timestep for projection-based ROMs is shown to be larger than or equal to that of their corresponding full-order models (FOMs). Numerical experiments for both periodic and non-homogeneous boundary conditions demonstrate that RedEigCD yields stable timestep increases up to a factor 40 compared to the FOM, without compromising accuracy. The methodology thus establishes a new link between linear stability theory and reduced-order modeling, offering a systematic path towards efficient, self-regulating ROM integration in incompressible flow simulations.
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All authors Josep Plana-Riu, Henrik Rosenberger, Benjamin Sanderse, F.Xavier Trias
Chemical Physics · Cross-listed from quant-ph
Supramolecular approach-based intermolecular interaction energy calculations using quantum phase estimation algorithm
Yuhei Tachi, Akihiko Arakawa, Taisei Osawa et al. (+2 more)
Accurate computation of non-covalent, intermolecular interaction energies is important to understand various chemical phenomena, and
quantum computers are anticipated to accelerate it. Although the state-of-the-art
quantum computers are still noisy and intermediate-scale ones, development of theoretical frameworks those are expected to work on a fault-tolerant
quantum computer is an urgent issue. In this work, we explore resource-efficient implementation of the
quantum phase estimation-based complete active space configuration interaction (QPE-CASCI) calculations, with the aid of the second-order Møller–Plesset perturbation theory (MP2)-based active space selection with Boys localized orbitals. We performed numerical simulations of QPE for the supramolecular approach-based intermolecular interaction energy calculations of the hydrogen-bonded water dimer, using 6 system and 6 ancilla
qubits. With the aid of algorithmic error mitigation, the QPE-CASCI simulations achieved interaction energy predictions with an error of 0.02 kcal mol$^{-1}$ relative to the CASCI result, demonstrating the accuracy and efficiency of the proposed methodology. Preliminary results on
quantum circuit compression for QPE are also presented to reduce the number of two-
qubit gates and depth.
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All authors Yuhei Tachi, Akihiko Arakawa, Taisei Osawa, Masayoshi Terabe, Kenji Sugisaki
Fluid Dynamics
Can Explicit Subgrid Models Enhance Implicit LES Simulations? A Very High-Order Solver Perspective
Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena et al. (+5 more)
High-order discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent-flow simulations and are increasingly attractive on GPU-oriented architectures, where high
polynomial orders can improve arithmetic intensity. However, very high-order under-resolved simulations remain sensitive to the balance between numerical and modeled dissipation. We investigate how explicit Vreman subgrid-scale (SGS) modeling interacts with dissipation from split-form stabilization and Riemann solvers in a DGSEM framework. Using the three-dimensional Taylor-Green
vortex at Re=1600 and in the inviscid limit, we assess kinetic-energy dissipation, spectral accuracy, and stability across well-resolved, under-resolved viscous, and strongly under-resolved regimes, comparing lower- and very high-order configurations at similar degrees of freedom. The usefulness of explicit SGS modeling depends strongly on resolution,
polynomial order, and the numerical dissipation already present. In the well-resolved cases considered, Vreman modeling does not improve accuracy because its active wavenumber range overlaps with the scheme's inherent dissipation. At similar degrees of freedom, lower-order simulations introduce stronger damping near the smallest resolved scales, whereas very high-order simulations preserve more spectral content but are more susceptible to high-wavenumber energy accumulation when dissipation is insufficient. Under stronger under-resolution, a weak SGS contribution can control this accumulation, while excessive SGS dissipation degrades intermediate scales. These results identify regimes in which explicit SGS modeling is beneficial, neutral, or detrimental, and provide practical guidance for selecting dissipation mechanisms in very high-order DG
turbulence simulations suited to modern GPU architectures.
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All authors Gonzalo Rubio, Gerasimos Ntoukas, Miguel Chávez-Módena, Oscar Mariño, Bernat Font, Oriol Lehmkuhl, Eusebio Valero, Esteban Ferrer
Fluid Dynamics
Secondary flows drive triboelectric powder charging in pneumatic conveying
Gizem Ozler and Holger Grosshans
Highly resolved simulations reveal the fundamental influence of a carrier fluid's flow dynamics on triboelectric powder charging. We found that particles transported through a square-shaped duct charge faster than in a channel flow caused by secondary flows that led to more severe particle-wall collisions. Specifically, particles with a Stokes number of 4.69 achieve 85 % of their
equilibrium charge approximately 1.5 times faster in duct flow than in channel flow. Also, charge distribution is more uniform in a duct cross-section compared with a channel cross-section. In channel flow, particles are trapped near the walls and collide frequently due to limited movement in the wall-normal direction, causing localized charge buildup. In contrast, duct flow promotes better mixing through secondary flows, reducing repeating collisions and providing uniform charge distribution across the cross-section. Upon charging, electrostatic forces significantly reshape particle behaviour and distribution. Once the powder achieves half of its
equilibrium charge, particles increasingly accumulate at the wall, leading to a reduced concentration in the central region. These changes in particle distribution have a noticeable impact on the surrounding fluid phase and alter the overall flow dynamics. These findings open the possibility for a new measure to control powder charging by imposing a specific pattern.
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Optics
An all-optical convolutional neural network for image identification
Wei-Wei Fu, Dong Zhao, Qing-Hong Rao et al. (+5 more)
In modern artificial intelligence, convolutional neural networks (CNNs) have become a cornerstone for visual and perceptual tasks. However, their implementation on conventional electronic hardware faces fundamental bottlenecks in speed and energy efficiency due to resistive and capacitive losses. Photonic alternatives offer a promising route, yet the difficulty of realizing optical nonlinearities has prevented the realization of all-optical CNNs capable of end-to-end image classification. Here, we demonstrate an all-optical CNN that bypasses the need for explicit optical nonlinear activations. Our architecture comprises a single spatial-differentiation convolutional stage–using 24 directional kernels spanning 360{\deg}, along with a mean-filtering kernel–followed by a diffractive fully-connected layer. The directional convolution enhances feature selectivity, suppresses noise and crosstalk, and simplifies the classification task, allowing the weak nonlinearity inherent in optical diffraction to achieve high accuracy. We report experimentally classification accuracies of 86.8% on handwritten digits (MNIST) and 94.8% on a ten-class gesture
dataset. The system delivers a computational throughput of 1.13X10^5 tera-operations per second (TOPS) and an energy efficiency of 1.51X10^3 TOPS/W–the highest reported among CNN hardware–with the potential to improve by a further 5-6 orders of magnitude using nanosecond-scale detectors. This work establishes a scalable pathway toward ultralow-latency, ultralow-energy
vision processing for real-time intelligent systems.
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All authors Wei-Wei Fu, Dong Zhao, Qing-Hong Rao, Heng-Yi Wang, Ben-Li Yu, Zhi-Jia Hu, Fang-Wen Sun, Kun Huang
Data Analysis and Statistics · Cross-listed from cs.LG
Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
Miguel Sánchez-Domínguez, Lucas Lacasa, Javier de Vicente et al. (+2 more)
Surrogate models (including deep neural networks and other machine learning
algorithms in supervised learning) are capable of approximating arbitrarily complex, high-dimensional input-output problems in science and engineering, but require a thorough data-agnostic uncertainty quantification analysis before these can be deployed for any safety-critical application. The standard approach for data-agnostic uncertainty quantification is to use conformal prediction (CP), a well-established framework to build uncertainty models with proven statistical guarantees that do not assume any shape for the error distribution of the surrogate model. However, since the classic statistical guarantee offered by CP is given in terms of bounds for the marginal coverage, for small calibration set sizes (which are frequent in realistic surrogate modelling that aims to quantify error at different regions), the potentially strong dispersion of the coverage distribution around its average negatively impacts the relevance of the uncertainty model's statistical guarantee, often obtaining coverages below the expected value, resulting in a less applicable framework. After providing a gentle presentation of uncertainty quantification for surrogate models for machine learning practitioners, in this paper we bridge the gap by proposing a new statistical guarantee that offers probabilistic information for the coverage of a single conformal predictor. We show that the proposed framework converges to the standard solution offered by CP for large calibration set sizes and, unlike the classic guarantee, still offers relevant information about the coverage of a conformal predictor for small data sizes. We validate the methodology in a suite of examples, and implement an open access software solution that can be used alongside common conformal prediction libraries to obtain uncertainty models that fulfil the new guarantee.
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All authors Miguel Sánchez-Domínguez, Lucas Lacasa, Javier de Vicente, Gonzalo Rubio, Eusebio Valero
Computational Physics · Cross-listed from cond-mat.soft
A hybrid Green-Kubo (hGK) framework for calculating viscosity from short MD simulations
Akash K. Meel and Santosh Mogurampelly
Viscosity calculation from
equilibrium molecular dynamics (MD) simulations relies on the traditional Green-Kubo (GK) framework, which integrates the stress autocorrelation function (SACF) over time. While the formalism is exact in the linear response regime, the traditional approach often suffers from poor convergence and requires extensive phase space sampling, which is computationally demanding for soft matter and
polymer systems. In this Letter, we introduce a hybrid Green-Kubo (hGK) framework that alleviates these limitations by partitioning the SACF into two physically meaningful regimes: (i) a short time ballistic component extracted directly from short MD simulations, and (ii) a long time relaxation tail represented using analytically motivated functions, $\phi(\tau)$, fitted only to short trajectories. This strategy bypasses the need for extensive sampling while preserving the exact GK framework at short times. Benchmarking against SPC/E water confirms excellent agreement with established results, and we further demonstrate the efficacy of the method for challenging electrolyte systems (EC-LiTFSI and PEO-LiTFSI), for which the GK framework fails to converge. The computational savings are substantial, with reductions of several orders of magnitude in required sampling, achieved without compromising predictive accuracy. We also discuss the limitations of the hGK framework and outline clear avenues for refinement, including optimal tail selection and robust identification of relaxation regimes in noisy stress data. The hGK framework presented in this Letter provides a conceptually simple, broadly applicable, and computationally efficient route for viscosity prediction in molecular
liquids,
polymer melts, and ionically conducting soft materials.
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Atomic Physics
Continuously tunable single-photon level nonlinearity with Rydberg state wave-function engineering
Biao Xu, Gen-Sheng Ye, Yue Chang et al. (+2 more)
Extending optical nonlinearity into the extremely weak light regime is at the heart of
quantum optics, since it enables the efficient generation of photonic
entanglement and implementation of photonic
quantum logic gate. Here, we demonstrate the capability for continuously tunable single-
photon level nonlinearity, enabled by precise control of Rydberg interaction over two orders of magnitude, through the use of microwave-assisted wave-function engineering. To characterize this nonlinearity, light storage and
retrieval protocol utilizing Rydberg electromagnetically induced transparency is employed, and the
quantum statistics of the retrieved
photons are analyzed. As a first application, we demonstrate our protocol can speed up the preparation of single
photons in low-lying Rydberg states by a factor of up to ~ 40. Our work holds the potential to accelerate
quantum operations and to improve the
circuit depth and connectivity in Rydberg systems, representing a crucial step towards scalable
quantum information processing with Rydberg atoms.
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All authors Biao Xu, Gen-Sheng Ye, Yue Chang, Tao Shi, Lin Li
Optics
Sub-cycle pulse control of holographic and non-holographic electron interferences
Rambabu Rajpoot and Eiji J. Takahashi
We investigate the influence of sub-cycle
laser pulses on
holographic and non-
holographic intracycle interferences by analyzing the photoelectron momentum distributions of helium using TDSE simulations supported by classical trajectory calculations. The results show that the forward-scattering
holographic (FSH), backward-scattering
holographic (BSH), and time double-slit (TDS) structures are found to be highly sensitive to the pulse duration, carrier-envelope phase (CEP), and temporal envelope in the sub-cycle regime. Sub-cycle pulses with CEP values of $0^\circ$ and $90^\circ$ selectively enhance or suppress distinct features, isolating
holographic patterns and enhancing BSH fringes. Classical analysis reveals that the intrinsic chirp inherent to sub-cycle fields shortens the recollision time for scattering trajectories, thereby increasing the fringe spacing in FSH and BSH patterns, while simultaneously enlarging the ATI peak spacing associated with TDS interference. Pulse envelope variations, even at fixed FWHM duration, further reshape the fringe spacings by modifying the instantaneous frequency and vector potential slope near ionization times. These results demonstrate that sub-cycle pulses enable precise temporal control of
holographic interference, offering new opportunities for probing and manipulating attosecond electron dynamics.
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Plasma Physics
Numerical model for pellet rocket acceleration in PELOTON
J. Corbett, R. Samulyak, F.J. Artola et al. (+3 more)
A direct numerical simulation model for the rocket acceleration of pellets in thermonuclear fusion devices has been developed for PELOTON, a 3D Lagrangian particle pellet code [R. Samulyak et al,
Nuclear Fusion 61 (4), 046007 (2021)], and validated using shattered pellet injection (SPI) experiments in JET. The pellet rocket acceleration is driven by grad-B drift of the ablation cloud that creates asymmetry and non-uniform heating of the cloud. The model accounts for non-uniform charging of the ablation cloud by hot
plasma electrons as well as local
plasma gradients. The increased pressure on the high-field-side compared to the low-field-side leads to pellet (fragment) rocket acceleration. Pure deuterium and deuterium-neon mixture models have been implemented. The background
plasma states have been obtained by using a new
plasma cooling model for PELOTON. The cooling model distributes the ablated material within the corresponding flux volumes and accounts for ionization and other energy losses, Ohmic heating by toroidal currents, and the energy exchange between ions and electrons.
Plasma profiles predicted by PELOTON cooling model have been compared with JOREK and INDEX simulations. PELOTON simulations of rocket acceleration and the corresponding trajectories of deuterium fragments are consistent with experimentally measured trajectories in JET. We show that composite deuterium-neon pellets containing 0.5% of neon experienced smaller deviation of their trajectories compared to the pure deuterium case. We simulate various spatial configurations of pellet fragments and demonstrate the cloud overlap impact on rocket acceleration. Additionally, we demonstrate the effect of
plasma state gradients on the rocket acceleration. Future work will focus on the rocket acceleration of SPI in projected ITER
plasmas and the development of the corresponding scaling law for the rocket acceleration.
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All authors J. Corbett, R. Samulyak, F.J. Artola, S. Jachmich, M. Kong, E. Nardon
Applied Physics · Cross-listed from math-ph
Rotation angles of a rotating disc as the holonomy of the Hopf fibration
Takuya Matsumoto
We address the fundamental question of how rotation angles arise in classical rigid body motion from a
geometric viewpoint. As a fundamental kinematical setting, we consider a rotating-disc model. We show that the rotation angle accumulated in a cyclic motion can be naturally decomposed into a dynamical phase and a
geometric phase, and that the latter is obtained from the $U(1)$ holonomy of the
Hopf fibration equipped with its canonical connection through a natural weight-two representation of the fiber group. By introducing a Gauss map based on a normal vector fixed at the center of the rotating disc, the physical periodic motion is mapped to a closed curve on the two-sphere $S^2$, whose horizontal lift in the
Hopf bundle $S^3 \to S^2$ determines the
geometric phase through the weight-two representation. This framework provides a conceptually clear interpretation of rotation angles as
geometric phases and clarifies the
geometric origin of their decomposition.
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Optics
Relative Wavefront Error Correction Over a 2.4 km Free-Space Optical Link via Machine Learning
Nathan K. Long, Benjamin P. Dix-Matthews, Alex Frost et al. (+4 more)
In coherent optical communication across turbulent atmospheric channels, reference beacons can be multiplexed with information-encoded signals during transmission. In this case, it is commonly assumed that the wavefront distortion of the two is equivalent. In contrast to this assumption, we present experimental evidence of relative wavefront errors (WFEs) between polarization-multiplexed reference beacons and signals, after passing through a 2.4 km atmospheric link. We develop machine learning-based wavefront correction
algorithms to compensate for observed WFEs, via phase
retrieval, resulting in up to a 2/3 reduction in the relative phase error variance. Further, we analyze the excess noise contributions from relative WFEs in the context of continuous-variable
quantum key distribution (CV-QKD), where our findings suggest that if future CV-QKD implementations employ wavefront correction
algorithms similar to those reported here, an order of magnitude increase in secure key rates may be forthcoming.
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All authors Nathan K. Long, Benjamin P. Dix-Matthews, Alex Frost, John Wallis, Ziqing Wang, Kenneth J. Grant, Robert Malaney
Applied Physics
Jingyi Li, Mengwen Qiao, Minghui Guo et al. (+7 more)
Gallium-based
liquid metals, as a broad category of emerging functional materials with unique physical, chemical, and biological properties, offer numerous possibilities for advancing intelligent systems. However, a basic query persistently remains for the complex
liquid metal system: Is there a minimal functional unit that can fully capture its diversity of morphology and function?
Cells, as the most basic structural and functional units of life, are small in scale but have complex structures, functions, and life activities. Analogous to nature, this article proposes the concept of
liquid metal
cells, and systematically explores their construction routes, sensing capabilities, motion behaviors, and potential applications. We first construct a multi-phase composite structure with
liquid metal as the
nucleus, ionic solution as the cytoplasm, and
polymer as the
cell membrane by developing a layered cryogenic molding method. Furthermore, we reveal that
liquid metal
cells exhibit inherently versatile responsive characteristics and self-adaptive behaviors to thermal, pressure, chemical, electrical, and
magnetic fields, indicating "small world, vast potential". Based on these fundamental findings, we finally demonstrate the feasibility of utilizing
liquid metal
cells as sensors, fluidic valves, and material transport carriers in flow channels through dynamic control.
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All authors Jingyi Li, Mengwen Qiao, Minghui Guo, Zerong Xing, Yunlong Bai, Ju Wang, Yujia Song, Ren Xu, Xi Zhao, Jing Liu
Atmospheric and Oceanic Physics
NORi: An ML-Augmented Ocean Boundary Layer Parameterization
Xin Kai Lee, Ali Ramadhan, Andre Souza et al. (+4 more)
NORi is a machine learning (ML) parameterization of ocean boundary layer
turbulence that is physics-based and augmented with neural networks. NORi stands for neural ordinary differential equations (NODEs) Richardson number (Ri) closure. The physical parameterization is controlled by a Richardson number-dependent diffusivity and viscosity. The neural ODEs are trained to capture the entrainment through the base of the boundary layer, which cannot be represented with a local diffusive closure. The parameterization is trained using large-eddy simulations in an a posteriori fashion, where parameters are calibrated with a loss function that explicitly depends on the actual time-integrated variables of interest rather than the instantaneous subgrid fluxes, which are inherently noisy. NORi conserves tracers by design, uses realistic nonlinear thermodynamics, and demonstrates excellent prediction and generalization capabilities in capturing entrainment dynamics under different convective strengths, background stratifications, rotation, and wind forcings. NORi is shown to simulate the seasonal
evolution of the boundary layer at Ocean Weather Station Papa with similar performance to the state-of-the-art two-equation k-epsilon closure. When implemented in a double-gyre simulation, it is numerically stable for at least 100 years, despite only being trained on two-day horizons, and can be run with time steps as long as one hour. Combining highly expressive neural networks with a physically grounded base closure proves to be a robust paradigm for designing parameterizations for
climate models: data required and training cost are drastically reduced, inference performance can be directly optimized as a primary objective, and numerical stability is implicitly promoted through training.
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All authors Xin Kai Lee, Ali Ramadhan, Andre Souza, Gregory LeClaire Wagner, Simone Silvestri, John Marshall, Raffaele Ferrari
Computational Physics
On the Construction of High-Order and Exact Pressure Equilibrium Schemes for Arbitrary Equations of State
Christopher DeGrendele, Nguyen Ly, Francois Cadieux et al. (+3 more)
Typical fully conservative discretizations of the Euler compressible single or multi-component fluid equations governed by a real-fluid equation of state exhibit spurious pressure oscillations due to the nonlinearity of the thermodynamic relation between pressure, density, and internal energy. A fully conservative, pressure-
equilibrium preserving method and a high-order, fully conservative, approximate pressure-
equilibrium preserving method are presented. Both methods are general and can handle an arbitrary equation of state and arbitrary number of species. Unlike existing approaches to discretize the multi-component Euler equations, we do not introduce non conservative updates, overspecified equations, or design for a specific equation of state. The proposed methods are demonstrated on inviscid smooth interface advection problems governed by three equations of state: ideal-gas, stiffened-gas, and van der Waals where we show orders of magnitude reductions in spurious pressure oscillations compared to existing schemes.
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All authors Christopher DeGrendele, Nguyen Ly, Francois Cadieux, Michael Barad, Dongwook Lee, Jared Duensing
Computational Physics
GPU-Portable Real-Space Density Functional Theory Implementation on Unified-Memory Architectures
Atsushi M. Ito
We present a GPU-portable implementation of a real-space density functional theory (DFT) code “QUMASUN” and
benchmark it on the new
Plasma Simulator featuring Intel Xeon 6980P CPUs, and AMD MI300A GPUs. Additional tests were performed on an NVIDIA GH200 GPU. In particular MI300A supports unified
memory and GH200 supports coherent
memory interconnect, simplifying GPU porting. A lightweight C++ lambda-based layer enables CPU, CUDA, and HIP execution without OpenMP/OpenACC preprocessor directives. For diamond (216 atoms) and tungsten (128 atoms) systems, MI300A and GH200 achieve 2.0-2.8 $\times$ and 2.3-2.4 $\times$ speedups over a 256-core Xeon node. The compute-bound kernels, which are fast
Fourier transforms (FFT), dense
matrix-
matrix multiplications (GEMM) and
eigenvalue solver, show substantial acceleration on both GPUs, indicating that the present GPU-portable approach can benefit a wide range of
plasma-fusion simulation codes beyond DFT.
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Geophysics
Uncertainty Quantification of the Fresh-Saltwater Interface from Time-Domain Electromagnetic Data
Arsalan Ahmed, Thomas Hermans, David Dudal et al. (+1 more)
Geophysical methods provide a cost-effective way to characterize the subsurface for hydrogeological projects, but they rely on solving an inverse problem. Traditionally, deterministic approaches are used, which face challenges due to non-uniqueness.
Stochastic methods offer uncertainty quantification but demand high computational resources.
Bayesian Evidential Learning (BEL) bypasses full
stochastic inversion by approximating the posterior distribution at lower cost. However, as with Monte Carlo techniques, efficiency depends on the number of inversion parameters. We show that incorporating prior knowledge into parameterization reduces unknowns and computational burden. Using time-domain electromagnetic data, we identify fresh - saltwater interfaces in the Flemish coastal aquifer. Conventional blocky or smooth deterministic inversions often misrepresent this transition zone as too sharp or too gradual. To address this, we parameterize the zone with two variables - depth and thickness - assuming a linear transition. This retains the compactness of parametric inversion while allowing sharp or gradual interfaces like voxel-based methods. To assess reliability, we invert these parameters stochastically using BEL with Thresholding (BEL1D-T). Results indicate this approach effectively captures uncertainty for synthetic and field data. The transition zone remains uncertain due to survey design and inherent non-uniqueness, yet our probabilistic method achieves this without the heavy computational cost of traditional
stochastic approaches.
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All authors Arsalan Ahmed, Thomas Hermans, David Dudal, Wouter Deleersnyder
Fluid Dynamics
Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
Ali Rabeh, Suresh Murugaiyan, Adarsh Krishnamurthy et al. (+1 more)
Developing neural surrogates for unsteady flow that generalize across complex geometries and maintain accuracy over extended rollouts remains a critical challenge for accelerating flow simulations. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, and is trained using 841 high-fidelity simulations from the FlowBench
dataset (https://baskargroup.bitbucket.io/). The model attains $\sim 5\%$ relative L2 single-step error and up to 1000x speedups over CFD on 262 test geometries not used for training. We provide physics-centric rollout diagnostics, including probe-based phase lag and Strouhal frequency analysis, to quantify long-horizon fidelity. These reveal accurate near-term transients but systematic error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigation strategies including physics-informed regularization and
diffusion-based refinement. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.
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All authors Ali Rabeh, Suresh Murugaiyan, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Instrumentation and Detectors
Design and Performance Simulation of the Electromagnetic Calorimeter at EicC
Ye Tian, Souvik Maity, Jingyu Li et al. (+6 more)
The electromagnetic calorimeter (ECAL) is a key detector component for precise electron and
photon measurements in electron-ion collision experiments. At the Electron-Ion
Collider in China (EicC), high-performance calorimetry is essential for exploring the internal structure of nucleons and studying the dynamics of
quarks and
gluons within
quantum chromodynamics (QCD). This paper presents the optimized design and performance simulation of the EicC ECAL system. The ECAL consists of three specialized sections tailored to distinct detection environments: (1) an electron-Endcap employing high-resolution pure Cesium Iodide (pCsI)
crystals, (2) a central barrel, and (3) an ion-Endcap, both adopting a cost-effective Shashlik-style sampling calorimeter with improved light yield. Each segment's geometry and material composition have been systematically optimized through Geant4 simulations to achieve excellent energy and position resolutions as well as strong electron-pion discrimination. The simulated performance indicates that the ECAL can achieve energy resolutions of 2 percent divided by sqrt(E) for pCsI
crystals and 5 percent divided by sqrt(E) for Shashlik modules, meeting the design goals of the EicC detector.
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All authors Ye Tian, Souvik Maity, Jingyu Li, Yuancai Wu, Shan Sha, Yutie Liang, Aiqiang Guo, Yuxiang Zhao, Dexu Lin
Applied Physics
Metagrating-based Single-pixel Acoustic Direction Finding
Thomas Macleod, Sebastian Oberst, David A. Powell et al. (+1 more)
Acoustic metamaterials provide new opportunities for compact and efficient wavefront manipulation, extending beyond conventional bulky and power-intensive phased-arrays. In this work, we exploit the spatial encoding properties of the acoustic metagrating aperture to transform incident acoustic fields into compressed measurements for single-pixel acoustic source localisation. The proposed method enables accurate direction finding of acoustic sources over both 180 and 360 degrees angular ranges. Numerical simulations confirm the robustness of the metagrating-based compressive sensing approach against noise and limited sampling. Experimental validation is conducted to verify its feasibility with practical metagrating prototyes. Compared wiith tranditional array-based localisation techniques, the single-pixel metagrating system significantly reduces hardward complexity while maintaining high localisation accuracy. These findings demonstrate the potential of integrating compressive sensing with acoustic metagratings for compact, low-cost, scalable source detection systems, with prospective applications in industrial monitoring, target tracking and non-destructive health monitoring.
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All authors Thomas Macleod, Sebastian Oberst, David A. Powell, Yan Kei Chiang
Medical Physics
Learnt Microwave Image Reconstruction with A Conformal Antenna Array
Wenyi Shao and Beibei Zhou
A deep learning model is proposed for reconstructing 2D dielectric breast images from time-domain signals. Unlike existing learning models that employ a fixed
antenna array, where input data consists solely of measurements, the proposed system integrates
antenna positioning into the processing pipeline. This allows for a conformal
antenna array that adapts to different breast sizes for optimal data collection across various patients, which eliminates undesired signal attenuation in coupling
liquid when implemented for the fixed array. By leveraging
antenna positions, the breast surface can be pre-estimated, enabling the neural network to focus on image reconstruction within the region of interest. Numerical results demonstrate that the proposed model may reconstruct breast images with good quality.
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