Fluid Dynamics · Cross-listed from cs.LG
Distributed physics-informed neural networks via domain decomposition for fast flow reconstruction
Yixiao Qian, Jiaxu Liu, Zewei Xia et al. (+3 more)
Physics-Informed Neural Networks (PINNs) offer a powerful paradigm for flow reconstruction, seamlessly integrating sparse velocity measurements with the governing Navier-Stokes equations to recover complete velocity and latent pressure fields. However, scaling such models to large spatiotemporal domains is hindered by computational bottlenecks and optimization instabilities. In this work, we propose a robust distributed PINNs framework designed for efficient flow reconstruction via spatiotemporal domain decomposition. A critical challenge in such distributed solvers is pressure indeterminacy, where independent sub-networks drift into inconsistent local pressure baselines. We address this issue through a reference anchor normalization strategy coupled with decoupled asymmetric weighting. By enforcing a unidirectional information flow from designated master ranks where the anchor point lies to neighboring ranks, our approach eliminates gauge freedom and guarantees global pressure uniqueness while preserving temporal continuity. Furthermore, to mitigate the Python interpreter overhead associated with computing high-order physics residuals, we implement a high-performance training pipeline accelerated by CUDA graphs and JIT compilation. Extensive validation on complex flow
benchmarks demonstrates that our method achieves near-linear strong scaling and high-fidelity reconstruction, establishing a scalable and physically rigorous pathway for flow reconstruction and understanding of complex hydrodynamics.
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All authors Yixiao Qian, Jiaxu Liu, Zewei Xia, Song Chen, Chao Xu, Shengze Cai
General Physics
Model selection with the Pantheon+ Type Ia SN sample
Namit Chandak, Fulvio Melia and Junjie Wei
Recent discoveries, e.g., by JWST and DESI, have elevated the level of tension with inflationary $\Lambda$CDM. For example, the empirical evidence now suggests that the standard model violates at least one of the energy conditions from general relativity, which were designed to ensure that systems have positive energy, attractive
gravity and non-superluminal energy flows. In this Letter, we use a recently compiled Type Ia
supernova sample to examine whether $\Lambda$CDM violates the energy conditions in the local Universe, and carry out model selection with its principal competitor, the $R_{\rm h}=ct$ universe. We derive model-independent constraints on the distance modulus based on the energy conditions and compare these with the Hubble diagram predicted by both $\Lambda$CDM and $R_{\rm h}=ct$, using the Pantheon+ Type Ia
supernova catalog. We find that $\Lambda$CDM violates the strong energy condition over the
redshift range $z \subset (0, 2)$, whereas $R_{\rm h}=ct$ satisfies all four energy constraints. At the same time, $R_{\rm h}=ct$ is favored by these data over $\Lambda$CDM with a likelihood of $\sim 89.5\%$ versus $\sim 10.5\%$. The $R_{\rm h}=ct$ model without inflation is strongly favored by the Type Ia
supernova data over the currrent standard model, while simultaneously adhering to the general relativistic energy conditions at both high and low
redshifts.
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Space Physics
Venting and Outgassing Simulations of Pressurized Lunar Modules: Contamination of the Lunar Environment
Stefano Boccelli, William M. Farrell, Prabal Saxena et al. (+1 more)
One objective of Artemis science is to determine the impact human activities have on the lunar environment, which might compromise science objectives and measurements. We perform a preliminary analysis of the contamination associated with airlock venting and outgassing from a prototype lunar-module geometry intended to host astronauts on the lunar surface. The air flow generated by the depressurization of the airlock, expanding in the lunar exosphere, is studied using the Direct Simulation Monte Carlo (DSMC) method for two different venting configurations and the particle flux on the surface is computed as a function of the distance from the the module. Outgassing from the main body of the module – assumed to be covered with a Multi-Layer Insulation (MLI) blanketing – and from the
solar panels is then analyzed using a view-factor method, employing outgassing rates from the literature.Our results give preliminary indications of the distance at which contamination levels fall below the values characteristic of native species in the lunar atmosphere. Scientific measurements targeting 40Ar should be carried farther than 30–100 meters from the module, while the detection of lower-abundance species such as polar-crater water might require to travel up to and beyond 3 km from the module.
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All authors Stefano Boccelli, William M. Farrell, Prabal Saxena, Orenthal J. Tucker
Data Analysis and Statistics · Cross-listed from math-ph
When Stein-Type Test Detects Equilibrium Distributions of Finite N-Body Systems
Jae Wan Shim
Starting from the probability distribution of finite N-body systems, which maximises the Havrda–Charvát
entropy, we build a Stein-type goodness-of-fit test. The Maxwell–Boltzmann distribution is exact only in the thermodynamic limit, where the system is composed of infinitely many particles as N approaches infinity. For an isolated system with a finite number of particles, the
equilibrium velocity distribution is compact and markedly non-Gaussian, being restricted by the fixed total energy. Using Stein's method, we first obtain a differential operator that characterises the target density. Its eigenfunctions are symmetric Jacobi
polynomials, whose orthogonality yields a simple, parameter-free statistic. Under the null hypothesis that the data follows the finite-N distribution, the statistic converges to a chi-squared law, so critical values are available in closed form. Large-scale Monte Carlo experiments confirm exact size control and give a clear picture of the power. These findings quantify how quickly a finite system approaches the classical limit and provide a practical tool for testing kinetic models in regimes where normality cannot be assumed.
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Plasma Physics
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado et al. (+13 more)
Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of
plasma dynamics from sparse, noisy, and incomplete sensors readings. The complexity of the underlying physics and the heterogeneity of experimental data pose formidable challenges for conventional numerical methods, and highlight the promise of modern data-native approaches. A major obstacle in realizing this potential is, however, the lack of curated, openly available
datasets and standardized
benchmarks. Existing fusion
datasets are scarce, fragmented across institutions, facility-specific, and inconsistently annotated, which limits reproducibility and prevents a fair and scalable comparison of AI approaches. In this paper, we introduce TokaMark, a structured
benchmark to evaluate AI models on real experimental data collected from the Mega Ampere Spherical Tokamak (MAST). TokaMark provides a comprehensive suite of tools designed to unify access to multi-modal fusion data and standardize evaluation protocols. The
benchmark includes a curated list of 14 tasks spanning a range of physical mechanisms, exploiting a variety of diagnostics and covering multiple operational use cases. A baseline model is provided to facilitate transparent comparison and validation within a unified framework. By establishing a unified
benchmark, TokaMark aims to accelerate progress in data-driven AI-based
plasma modeling, contributing to the broader goal of achieving sustainable and stable fusion energy. The
dataset,
benchmark, documentation, and tooling are open-sourced under https://github.com/UKAEA-IBM-STFC-Fusion-FMs/tokamark_baseline.
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All authors Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado, Nicola C. Amorisco, Tobia Boschi, George K. Holt, Andrea Loreti, Eszter Székely, Alexander Whittle, Adriano Agnello, Stanislas Pamela, Alessandra Pascale, Robert Akers, Juan Bernabe Moreno, Sue Thorne, Mykhaylo Zayats
Instrumentation and Detectors · Cross-listed from eess.IV
U-Net Based Image Enhancement for Short-time Muon Scattering Tomography
Haochen Wang, Pei Yu, Liangwen Chen et al. (+6 more)
Muon Scattering
Tomography (MST) is a promising non-invasive inspection technique, yet the practical application of short-time MST is hindered by poor image quality due to limited
muon flux. To address this limitation, we propose a U-Net-based framework trained on Point of Closest Approach (PoCA) images reconstructed with simulation MST data to enhance image quality. When applied to experimental MST data, the framework significantly improves image quality, increasing the Structural Similarity Index Measure (SSIM) from 0.7232 to 0.9699 and decreasing the Learned Perceptual Image Patch Similarity (LPIPS) from 0.3604 to 0.0270. These results demonstrate that our method can effectively enhance low-statistics MST images, thereby paving the way for the practical deployment of short-time MST.
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All authors Haochen Wang, Pei Yu, Liangwen Chen, Weibo He, Yu Zhang, Yuhong Yu, Xueheng Zhang, Lei Yang, Zhiyu Sun
Fluid Dynamics
On the wake and flapping dynamics of different aspect ratio flags
Gaétan Raynaud and Karen Mulleners
The flapping of flags is a classical problem involving fast and large amplitude deformations of a thin flexible plate and unsteady flow phenomena. We perform systematic time and space-resolved measurements of the deformation and drag acting on flapping flags for various aspect ratios and mass ratios. Bending waves travel from the root to the tip at a speed close to the incoming flow and the typical wavelength of the waves scales with the length of the flag. With smaller aspect ratio, the local dynamic pressure exerted by the fluid on the flag is reduced, lowering the wave propagation speed, and reducing the tip frequency. The effect of aspect ratio on the
vortex formation in the near wake is analysed using flow field measurements. We identified two characteristic length scales, the ratio of the flag area over its perimeter $L^*$ and the square root of area $\sqrt{HL}$ that scale the circulation shed during a cycle. Changes in aspect ratio and mass ratio generate a wide scattering of the mean drag coefficient, ranging from 0 to 0.55. We discuss a kinematic-based model for the mean drag coefficient. This model uses the mass ratio and the typical tip speed, which depends linearly on the wave speeds, to predict the mean drag coefficient without any fitting parameter.
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Plasma Physics
Enhanced TNSA Ion Acceleration via Optical Confinement and Geometric Plasma Focusing in Annular Sector Targets
Mohammad Rezaei-Pandari, Mahdi Shayganmanesh and Mohammad Hossein Mahdieh
Enhancing the conversion efficiency and maximum energy of
laser-driven ion beams is a critical challenge for applications in
hadron therapy and high-energy density physics. In this work, we present a comprehensive two-dimensional Particle-In-
Cell (PIC) simulation study comparing Target Normal Sheath Acceleration (TNSA) from standard flat foils and novel annular sector (C-shaped) targets. Under identical ultra-intense
laser irradiation (a0=10, tau=25 fs), the annular sector geometry demonstrates a substantial enhancement in acceleration performance driven by two synergistic mechanisms: electromagnetic cavity confinement and
geometric plasma focusing. Our analysis reveals that the target void acts as an optical trap, sustaining oscillating electromagnetic fields for over 300fs via multiple internal reflections. This confinement results in a total
laser energy absorption of 49% (compared to 16% for flat targets), which yields a peak electron temperature of 5.1 MeV more than double the 2.2MeV observed in flat targets. Furthermore, phase space diagnostics confirm that ion bunches accelerated from the converging cavity walls superimpose at the
geometric center, creating a localized high-density focal spot. Consequently, the annular target increases the
proton cut-off energy to 22MeV (vs. 12MeV for flat targets) and boosts Carbon ion energies beyond 60MeV. These findings establish that tailoring target
curvature to exploit optical trapping and
geometric focusing offers a robust pathway for developing compact, high-efficiency
laser-ion sources.
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Atomic Physics
Sawtooth wave adiabatic passage in a grating magneto-optical trap
Peter K. Elgee, Ananya Sitaram, Sara Ahanchi et al. (+4 more)
We demonstrate sawtooth wave adiabatic passage (SWAP) in a grating magneto-optical trap (MOT) operating on the $^1$S$_0$ $\rightarrow$ $^3$P$_1$ transition of neutral $^{88}$Sr. From numerical simulations of SWAP using our
laser beam geometry, we find that SWAP provides greater cooling than triangle wave frequency modulation despite the complex polarization environment of a grating MOT. The simulation is confirmed by our experimental results, where we demonstrate a factor of two improvement in transfer efficiency between our $^1$S$_0$ $\rightarrow$ $^1$P$_1$ grating MOT and our $^1$S$_0$ $\rightarrow$ $^3$P$_1$ grating MOT. We trap up to $3\times10^6$ $^{88}$Sr atoms in the $^1$S$_0$ $\rightarrow$ $^3$P$_1$ grating MOT, at an average temperature of 4.9 $\mu$K with a lifetime of approximately 0.7 s. Our results show that SWAP is effective in non-orthogonal
laser beam geometries, allowing greater duty cycles or higher atom number in sensors based on narrow-line grating MOTs.
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All authors Peter K. Elgee, Ananya Sitaram, Sara Ahanchi, Nikolai N. Klimov, Stephen P. Eckel, Gretchen K. Campbell, Daniel S. Barker
Medical Physics
Causal machine learning reveals age-dependent radiation dose effects on mandibular osteoradionecrosis
Jingyuan Chen, Yunze Yang, Olivia M. Muller et al. (+8 more)
Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into interventional treatment guidelines. Here we apply causal machine learning to establish causal effects of radiation dose parameters on mandibular osteoradionecrosis (ORN) in 931 head and neck cancer patients treated with volumetric-modulated arc therapy. Using generalized random forests, we demonstrate that all examined dosimetric factors exhibit significant positive causal effects on ORN development (average treatment effects: 0.092-0.141). Integration with explainable machine learning reveals substantial treatment effect heterogeneity, with patients aged 50-60 years showing the strongest causal dose-response relationships (conditional average treatment effects up to 0.229), while patients over 70 years demonstrate minimal effects. These results suggest that age-stratified treatment optimization and personalized treatment planning for the dosimetric factors could reduce ORN risk. Our findings demonstrate that causal inference methods can transform clinical retrospective radiotherapy data into personalized treatment recommendations, providing a methodological framework applicable to toxicity prediction across oncology and other clinical domains where treatment decisions depend on complex dose-response relationships.
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All authors Jingyuan Chen, Yunze Yang, Olivia M. Muller, Lei Zeng, Zhengliang Liu, Tianming Liu, Robert L Foote, Daniel J Ma, Samir H Patel, Zhong Liu, Wei Liu
Physics and Society · Cross-listed from q-bio.PE
Threshold Resource Redistribution in Spatially-Structured Kinship Networks
Alina Kochocki
We present a model for a threshold-based resource redistribution process in a spatially-explicit population, characterizing the relation between kinship network structure, local interactions and persistence. We find that population survival becomes possible for lower resource densities, but leads to increased network heterogeneity and locally centralized clusters. We interpret this in relation to a feedback between the kinship network structure and reproduction ability.
Agents receive
stochastic resources and solicit additional resources from connected individuals when below a minimum, with each
agent contributing a fraction of their excess based on relatedness. We first analyze a fully-connected population with uniform redistribution fraction and discuss mean field expectations as well as finite size corrections. We extend this model to a hub-and-spoke network, exploring the impact of network asymmetry or centrality on resource distribution. We then develop a spatially-limited population model with
diffusion, local pairing, reproduction and mortality. Redistribution is introduced as a function of relatedness (generational distance through most-recent common ancestor) and distance. Redistribution-dependent populations exhibit a higher level of relational closeness with increased clustering for
agents of highest node strength. These results highlight the interaction of resource density, cooperation and kinship in a spatially-limited regime.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Engineering altermagnetic symmetry to enable anomalous Hall response in Cr$_{1-x}$Mn$_x$Sb
Miriam G. Fischer, Lukas Odenbreit, Olena Gomonay et al. (+10 more)
Altermagnets are a promising class of materials for spintronic applications. However, compounds that simultaneously combine the
symmetry required to support an anomalous
Hall effect with good metallic conductivity and
magnetic ordering temperatures well above room temperature have remained elusive. Here, we demonstrate that partial substitution of Cr by Mn in epitaxial CrSb(100) thin films provides a viable route to engineer the combined structural and
magnetic symmetry necessary to enable an otherwise
symmetry-forbidden anomalous
Hall effect. We systematically explore the
magnetic phase diagram of Cr(Mn)Sb thin films including
neutron diffraction. This allows us to identify a
magnetic symmetry that supports the anomalous
Hall effect, which we demonstrate in Cr(0.75)Mn(0.25)Sb. Guided by Landau theory, we model the field-driven reorientation of the Neel vector and the resulting anomalous
Hall response, achieving good qualitative agreement with the experimental observations.
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All authors Miriam G. Fischer, Lukas Odenbreit, Olena Gomonay, Sonka Reimers, Pascal Manuel, Dmitry Khalyavin, Sean Langridge, Jairo Sinova, Thibaud Denneulin, Joseph V. Vaz, Rafal E. Dunin-Borkowski, Mathias Kläui, Martin Jourdan
Fluid Dynamics
Optimal wind farm energy and reserve scheduling incorporating wake interactions
Marin Mabboux-Fort, Majid Bastankhah, Peter C Matthews et al. (+1 more)
This paper proposes a novel approach for optimal energy and reserve scheduling of wind farms by explicitly modelling wake interactions to enhance
market participation and operational efficiency. Conventional methods often neglect wake effects, relying on power curve estimations that represent an upper limit and reduce
market performance. To address this, a two-stage
stochastic programming framework is developed, integrating a wake-aware power estimation model within the FLORIS simulation software. Wind and reserve uncertainties are addressed through scenario generation and reduction, enabling wind power producers to optimise participation in day-ahead energy and ancillary services
markets, with particular focus on the Frequency Restoration Reserve (FRR). The wake-aware model provides more realistic power output predictions based on site-specific wind and atmospheric conditions, improving scheduling accuracy and reducing imbalance penalties. Wake steering is further employed to mitigate wake-induced losses and increase income through participation in ancillary services. The proposed approach is evaluated through a case study of the London Array offshore wind farm participating in the Great Britain (GB) electricity
markets. Results show that conventional methods estimate production 12-13% higher, leading to imbalance penalties and 3% lower revenue compared with the wake-aware approach accounting for wake interactions. Moreover, the steering-enhanced approach yields an additional 1-2% increase in income relative to the wake-aware baseline. These findings underscore the value of accounting for wake interactions in wind farm scheduling and demonstrate the economic and operational benefits of active wake management, offering insights for improving grid stability and profitability as wind penetration continues to rise.
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All authors Marin Mabboux-Fort, Majid Bastankhah, Peter C Matthews, Mokhtar Bozorg
Chemical Physics
Kohn-Sham density encoding rescues coupled cluster theory for strongly correlated molecules
Abdulrahman Y. Zamani, Barbaro Zulueta, Andrew M. Ricciuti et al. (+2 more)
Coupled cluster theory with a Kohn-Sham reference (KS-CC) can dramatically outperform its Hartree-Fock counterpart for strongly correlated systems, but the origin of these improvements has remained unclear. Here we demonstrate that these improvements arise from differences in the one-particle density
matrix that are encoded into the non-canonical Fock
matrix and not from the nature of the KS orbitals, as is commonly assumed. Equipped with this insight, KS-CCSD(T) can be leveraged to achieve near-chemical-accuracy for electronic and thermochemical properties of transition-metal dimers and main-group compounds. Most strikingly, KS-CCSD(T) qualitatively recovers the entire Cr$_2$ potential energy surface, a notorious failure case for HF-CCSD(T) and single-reference density functional theory. We further introduce a density difference diagnostic that identifies multireference character and guides practitioners toward rational selections of optimal references at mean-field cost. These results establish KS-CCSD(T) as a practical route to treat strong correlation within the "gold standard" framework, and this has immediate implications for machine learning potential development and materials research, areas that heavily rely on KS-DFT for model-parameter fitting.
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All authors Abdulrahman Y. Zamani, Barbaro Zulueta, Andrew M. Ricciuti, John A. Keith, Kevin Carter-Fenk
Optics · Cross-listed from quant-ph
Theory of direct measurement of the quantum pseudo-distribution via its characteristic function
Andrew N. Jordan, David R. M. Arvidsson-Shukur and Aephraim M. Steinberg
We propose a method for directly measuring the
quantum mechanical pseudo-distribution of observable properties via its characteristic function. Vandermonde
matrices of the
eigenvalues play a central role in the theory. This proposal directly finds the pseudo-distribution using weak measurements of the generator of position moments (momentum translations). While the pseudo-distribution can be extracted from the data in a theory-agnostic way, it is shown that under
quantum-mechanical formalism, the predicted pseudo-distribution is identified with the Kirkwood-Dirac pseudo-distribution. We discuss the construction of both the joint pseudo-distribution and a conditional pseudo-distribution, which is closely connected to weak-value physics. By permuting position and momentum measurements, we give a prescription to directly probe the canonical commutation relation and verify it for any
quantum state. This work establishes the theory of a characteristic function approach to pseudo-distributions, as well as providing a constructive approach to measuring them directly.
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Physics Education
Cognitive Load and Situational Interest in Physics Laboratories: A Comparative Study Across Three Instructional Modalities
Razan Hamed and N. Sanjay Rebello
Understanding how an instructional approach shapes student's cognitive resources and engagement is central to improving undergraduate physics education especially for novice learners. This study examines how three instructional modalities (Inquiry-based, Design-based, and Game-based learning) affect cognitive load and situational interest in physics laboratories for non-STEM majors. Guided by the revised Cognitive Load Theory framework, two experiments were conducted across two physics domains: mechanics and electrical
circuits. In each experiment, students completed three laboratory sessions, one in each instructional modality, followed by surveys measuring cognitive load and situational interest. One-way ANOVA analyses revealed significant differences across the three modalities in both experiments. Game-based laboratories consistently yielded the lowest cognitive load and the highest situational interest, while inquiry-based and design-based labs imposed higher cognitive demands, with their relative effects varying by domain. Overall, situational interest exhibited an inverse relationship with cognitive load, suggesting that reduced cognitive demands support greater engagement. These findings emphasize the value of strategically selecting and combining instructional modalities to balance cognitive load and foster meaningful engagement in physics laboratories for novice learners.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
A Nonequilibrium Equation of State for a Turbulent 2D Bose Gas
Yi Jiang, Nikolai Maslov, Andrey Karailiev et al. (+3 more)
Nonequilibrium equations of state can provide an effective thermodynamic-like description of far-from-
equilibrium systems. We experimentally construct such an equation for a direct energy cascade in a turbulent two-dimensional Bose gas. Our homogeneous gas is continuously driven on a large length scale and, with matching dissipation on a small length scale, exhibits a nonthermal but stationary power-law momentum distribution. Our equation of state links the cascade amplitude with the underlying scale-invariant energy flux, and can, for different drive strengths, gas densities, and interaction strengths, be recast into a universal power-law form using scalings consistent with the Gross-Pitaevskii model.
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All authors Yi Jiang, Nikolai Maslov, Andrey Karailiev, Christoph Eigen, Martin Gazo, Zoran Hadzibabic
Atomic Physics · Cross-listed from quant-ph
Quantum simulation of the Dicke model in a two-dimensional ion crystal
Bryce Bullock, Sean R. Muleady, Jennifer F. Lilieholm et al. (+6 more)
Quantum many-body systems driven far from
equilibrium can exhibit phase transitions, chaos and
entanglement, but probing these phenomena in large, nearly closed systems remains challenging. The Dicke model, which couples an ensemble of two-level systems collectively to a single bosonic mode, provides a fundamental framework for exploring these phenomena in light-matter interactions. Here we show that a two-dimensional
crystal of approximately 100 trapped ions realizes this model and accesses integrable dynamical phase transitions, signatures of chaotic dynamics and fluctuation-driven
spin-
phonon excitation growth. Adiabatic elimination of the vibrational mode reveals
ferromagnetic and paramagnetic dynamical
spin phases, whereas strong
spin-
phonon coupling produces irregular trajectories and local relaxation consistent with
quantum thermalization. Following a quench from an unstable fixed point, correlated excitation growth precedes generalized
vacuum Rabi collapses and revivals; calibrated calculations identify two-mode squeezing 2.6 dB below the standard
quantum limit, supported by reconstruction from
spin measurements. By connecting collective dynamical instabilities to experimentally accessible signatures of
quantum correlations, these results establish large ion
crystals as a scalable platform for investigating non-
equilibrium light-matter dynamics and generating resources for
quantum-enhanced sensing.
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All authors Bryce Bullock, Sean R. Muleady, Jennifer F. Lilieholm, Yicheng Zhang, Arghavan Safavi-Naini, Robert J. Lewis-Swan, John J. Bollinger, Ana Maria Rey, Allison L. Carter
Physics and Society
To clean or not to clean: The free-rider problem in sequentially shared resources
Alexander Feigel and Alexandre V. Morozov
Shared resources enhance productivity yet at the same time provide channels for biological and digital contamination, turning physical or digital hygiene into a cooperation dilemma prone to free-riding. Here we introduce a game of sequential sharing of common resources, an empirically parameterized evolutionary model of population dynamics in sequential-use settings such as gyms and shared workspaces. The success of the strategies implemented in the model, such as cleaning equipment before or after use, are based on the trade-offs between cleaning costs, contamination risk, and social incentives to mitigate disease transmission. We find that cooperative hygiene can be achieved by lowering the effective costs of cleaning, strengthening pro-social incentives, and monitoring population-level noncompliance. Remarkably, stability of fully altruistic populations is primarily affected by the cleaning costs. In contrast, increasing effective infection costs, for example through punishment, appears less important in this case. The model's evolutionary dynamics exhibit multi-stability, hysteresis, and abrupt shifts in strategy composition, broadly consistent with empirical observations from shared-use facilities. Our framework offers testable predictions and is amenable to quantitative calibration with behavioral and environmental data. Our predictions can be used to inform the design of cost-effective public health and digital security policies.
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General Physics
High-Level Fault-Tolerant Abstractions for Quantum-Gate Circuit Design and Synthesis: PQC and Topological Anyon Architectures (TQC) for Categorical Computations in SU(2)_3 TQFT and D-brane Stability
Vaidik A Sharma and Sainath Bitragunta
We propose a dual-architecture
quantum simulation framework for modeling morphisms and stability conditions in the bounded derived category $\mathbf{D}^b(\mathrm{Coh}(X))$, with applications to D-brane physics on Kähler and non-Kähler
manifolds. Two physically executable
quantum realizations are constructed: parameterized
quantum circuits (PQCs) implemented on conventional gate-based
qubit platforms, and a topological
quantum computing (TQC) realization using braiding and fusion of Fibonacci anyons modeled via SU(2)$_3$ modular tensor categories. In the PQC model, we encode slope functionals S(F) and stability constraints as variational observables, mapping derived morphisms to
unitaries that evolve over parameterized angles. The output expectation values simulate
quantum-corrected Chern class inequalities with deformation terms $\delta$, capturing
quantum corrections to classical
geometric stability. In the TQC model, we engineer braid group representations to implement functorial transformations such as spherical twists and autoequivalences as sequences of fault-tolerant braid operations. This bifurcated approach provides a robust engineering pipeline for simulating categorical stability and homological
algebra on
quantum hardware, bridging abstract derived category theory with executable
quantum architectures.
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Plasma Physics
Explosive eruption cycles in a rotating Z-pinch
David N. Hosking, Luca Swinnerton and Rahul Kesavan
A transonic shear flow directed along
magnetic field lines can linearly stabilize a steep pressure gradient in a confined magnetohydrodynamic (MHD)
plasma. In Z-pinch geometry, we show that, like the edge pedestal in tokamak devices, this transport barrier – which we call the “MHD pedestal” – is metastable, i.e., unstable to finite-amplitude displacements of flux tubes. We simulate the slow formation of an MHD pedestal in a heated and sheared Z-pinch, which collapses on reaching a critical height, expelling an order-unity fraction of the confined thermal energy. The MHD pedestal then rebuilds and the process repeats, in a manner analogous to the ELM cycle seen in fusion experiments. We show that the available energy of the metastable
equilibrium, and the most energetically favorable amount of ejected
plasma, can be calculated from first principles via combinatorial optimization of flux-tube interchanges.
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Optics · Cross-listed from quant-ph
Modeling integrated frequency shifters and beam splitters
Manuel H. Muñoz-Arias, Kevin J. Randles, Nils T. Otterstrom et al. (+3 more)
Photonic
quantum computing is a strong contender in the race to fault-tolerance. Recent proposals using
qubits encoded in frequency modes promise a large reduction in hardware footprint, and have garnered much
attention. In this encoding, linear optics, i.e., beam splitters and phase shifters, is necessarily not energy-conserving, and is costly to implement. In this work, we present designs of frequency-mode beam splitters based on modulated arrays of coupled resonators. We develop a methodology to construct their effective transfer
matrices based on the SLH formalism for
quantum input-output networks. Our methodology is flexible and highly composable, allowing us to define $N$-mode beam splitters either natively based on arrays of $N$-resonators of arbitrary connectivity or as networks of interconnected $l$-mode beam splitters, with $l<N$. We apply our methodology to analyze a two-resonator device, a frequency-domain phase shifter and a Mach-Zehnder interferometer obtained from composing these devices, a four-resonator device, and present a formal no-go
theorem on the possibility of natively generating certain $N$-mode frequency-domain beam splitters with arrays of $N$-resonators.
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All authors Manuel H. Muñoz-Arias, Kevin J. Randles, Nils T. Otterstrom, Paul S. Davids, Michael Gehl, Mohan Sarovar
Applied Physics · Cross-listed from nucl-ex
Westcott $g$ Factors Extended to Arbitrary Neutron Energy Spectra
D.A. Matters, A.M. Hurst and T. Kawano
Westcott $g$ factors are used in
Neutron Activation Analysis (NAA) and
Prompt Gamma-ray Activation Analysis (PGAA) to evaluate the impact of non-$1/v$ behavior in the
neutron-capture cross sections of certain
nuclei on activation product yields. This non-$1/v$ behavior arises from the presence of
neutron resonances in the
neutron-capture cross sections that overlap with the source
neutron spectrum at low ($<5$~eV) energies. Historically, Westcott $g$ factors that have been cataloged for NAA and PGAA applications are the result of calculations that assume a Maxwellian
neutron velocity distribution with a given average temperature. In this study, we use this approach with updated
neutron-capture cross sections from the Evaluated
Nuclear Data File, version VIII.1 (ENDF/B-VIII.1) to tabulate Westcott $g$ factor values for a broad range of Maxwellian distribution temperatures, comparing the results against currently-available $g$ factors from International Atomic Energy Agency tables and other sources. It was discovered during this analysis that the use of guided thermal and cold-
neutron beams at certain facilities necessitates an approach for evaluating Westcott $g$ factors based on arbitrary non-Maxwellian spectra. In this paper, we present an approach for calculating $g$ factors with user-specified
neutron spectra, and we apply these methods to obtain Westcott $g$-factors for guided- and cold-
neutron beams at the Budapest Research Reactor and the Forschungsreaktor München II reactor. Open-source software has been developed as part of this study that can be used to perform these calculations for applications in PGAA and NAA experiments
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Applied Physics · Cross-listed from quant-ph
Quantum noise scaling in continuously operating multiparameter sensors
Aleksandra Sierant, Diana Méndez-Avalos, Santiago Tabares Giraldo et al. (+1 more)
We experimentally investigate the
quantum noise mechanisms that limit continuously operating multiparameter
quantum sensors. Using a hybrid rf-dc optically pumped magnetometer, we map the
photon shot noise,
spin projection noise, and measurement back-action noise over an order of magnitude in probe power and a factor of three in pump power while remaining
quantum-noise-limited. We observe linear, quadratic, and cubic scaling of the respective total noise powers with probe
photon flux, together with a quadratic dependence of back-action on pump
photon flux, in quantitative agreement with a
stochastic Bloch-equation model. At higher probe powers, additional probe-induced relaxation modifies the
spin-noise spectrum while preserving the integrated noise scaling. Our results reveal fundamental, resource-dependent trade-offs unique to continuously monitored multiparameter sensors and establish experimentally the
quantum limits governing their optimal operation.
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All authors Aleksandra Sierant, Diana Méndez-Avalos, Santiago Tabares Giraldo, Morgan W. Mitchell
Optics
Efficient net-gain integrated optical parametric amplifier in the quantum regime
Yung-Cheng Kao, Jiaqi Huang, Ian Briggs et al. (+2 more)
Optical parametric amplifiers (OPAs) are promising to overcome the wavelength coverage and noise limitations in conventional optical amplifiers based on rare-earth doping and
semiconductor gain. However, the high power requirement remains a major obstacle to the widespread use of OPAs. Integrated OPAs can in principle improve the pump efficiency with tight mode confinement; however, challenges associated with propagation loss, limited nonlinearity, and susceptibility to nanoscale fabrication imperfections prevent them from competing with conventional bulk and fiber-based OPAs. Here, we demonstrate a highly efficient integrated OPAs with continuous-wave net gain. The pump efficiency is improved by over one order of magnitude. Phase-sensitive gain of 23.5 dB is demonstrated, significantly exceeding previous integrated OPAs, using only 110 mW pump power and no cavity enhancement. This is achieved with parametric down-conversion in thin-film lithium niobate waveguides using the adapted poling technique to maintain the coherence of nonlinear interactions. Moreover, the high parametric gain exceeds fibre-chip-fibre losses, leading to appreciable net gain up to 10 dB. The 3 dB bandwidth is approximately 120 nm, covering telecommunication S-, C-, and Lbands.
Quantum-limited noise performance is confirmed through the measurement of output field fluctuation below the classical limit. We further demonstrate that signalto-noise ratio in noisy optical communications can be increased by leveraging this efficient integrated OPA. Our work marks a significant step towards ideal optical amplifiers with strong amplification, high efficiency,
quantum-limited noise, large bandwidth, and continuous-wave operation, unlocking new possibilities for next-generation photonic information processing systems.
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All authors Yung-Cheng Kao, Jiaqi Huang, Ian Briggs, Pao-Kang Chen, Linran Fan
Atomic Physics
Extended Rydberg Lifetimes in a Cryogenic Atom Array
Junlan Jin, Yue Shi, Youssef Aziz Alaoui et al. (+4 more)
We report on the realization of a $^{133}$Cs optical tweezer array in a cryogenic blackbody radiation (BBR) environment. By enclosing the array within a 4K radiation shield, we measure long Rydberg lifetimes, up to $406 (36)\,\mu$s for the $55 P_{3/2}$ Rydberg state, a factor of 3.3(3) longer than the room-temperature value. We employ single-
photon coupling for coherent manipulation of the ground-Rydberg
qubit. We measure a small differential dynamic polarizability of the transition, beneficial for reducing dephasing due to light intensity fluctuations. Our results pave the path for advancing neutral-atom two-
qubit gate fidelities as their error budgets become increasingly dominated by $T_1$ relaxation of the ground-Rydberg
qubit.
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All authors Junlan Jin, Yue Shi, Youssef Aziz Alaoui, Jingxin Deng, Yukai Lu, Jeff D. Thompson, Waseem S. Bakr