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Physics Physics

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Saturday 23 May

Medical Physics

Deep learning-based self-supervised detection and localization of magnetic resonance imaging anomalies in artificial intelligence-supported radiotherapy workflows

Mustafa Kadhim, Viktor Rogowski, Emilia Persson et al. (+7 more)

Artificial intelligence is increasingly integrated into radiotherapy workflows, yet it remains vulnerable to out-of-distribution image data that can introduce unexpected behavior in clinical tasks. Deep learning (DL)-based anomaly detection for pelvic magnetic resonance imaging (MRI) remains limited and transparent evaluation for full automation is lacking. This study aimed to develop two self-supervised anomaly-detection framework variations in radiotherapy for pelvic and brain MRI.A two-stage self-supervised DL-framework was trained separately for pelvic and brain MRI utilizing three public datasets. The first stage compressed normal image data into discrete representations, and a second stage modeled the representation distribution. Anomaly-evidence was obtained by combining perceptual image differences with token-surprisal scores via negative log-likelihood estimation. Automated detection was evaluated for two anatomical cohorts: pelvis, encompassing both synthetic global anomalies and real clinical anomalies, and brain, comprising clinically annotated anomalies. Performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). For each cohort, a subset of normal images was reserved for analysis. Promising exploratory performance across evaluation cohorts was obtained. AUCs were 0.97 (95% CI 0.96-0.99) for synthetic pelvic anomalies, 0.95 (95% CI 0.90-1.00) for clinical pelvic anomalies, and 0.81 (95% CI 0.74-0.87) for brain anomalies. Qualitative heatmap analysis showed spatial agreement between detected anomalies and reference annotations. The framework variations showed promising anomaly detection performance across pelvic and brain MRI, supporting its potential for automated MRI quality control in radiotherapy. Heatmap-based visualization further improved interpretability by spatially localizing detected anomalies.
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All authors Mustafa Kadhim, Viktor Rogowski, Emilia Persson, Camila Gonzalez, André Haraldsson, Sofie Ceberg, Mikael Nilsson, Malin Kügele, Sven Bäck, Christian Jamtheim Gustafsson

Computational Physics

The Method of Simultaneous Solutions Applied to Neutron Transport and Heat Conduction

Dean Price, Brian Kiedrowski and Benoit Forget

This paper provides an initial description of the Method of Simultaneous Solutions, a Monte Carlo approach that simultaneously solves multiple Boltzmann-transport-like phenomena. Here, it is used to simultaneously solve the neutron transport and heat conduction equations. Analytically-derived weighting factors are tracked through a neutron transport-governed random walk to tally statistical estimators that can be used to calculate the temperature distribution. In this initial presentation, the method is readily applicable to neutron-heat multiphysics problems where the heat source and neutron source are identically distributed spatially. The primary theoretical benefit of MOSS lies in the reduction of computational cost that occurs from the removal of a dedicated routine to solve the heat conduction equation. Practically, branching processes required to capture the disparate boundary conditions associated with these separate physical phenomena can lead to large computational times dedicated to a single physics. In addition, this correlated sampling-based method can suffer from infinite variance associated with statistical estimators if the stochastic processes being tracked are too different. The final drawback demonstrated in this paper is that the approximation of heat conduction as a Boltzmann transport-governed process leads to errors in calculated temperatures. The paper explores these drawbacks on two demonstration problems, a problem consisting of slab geometry and a problem consisting of a hexagonal pin cell.
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General Physics

Critical Hawkes Processes with Random Fertilities: Stationarity in Law Beyond Infinite Mean Activity

Didier Sornette

Genuinely critical dynamics have been proposed to organize many natural and social systems, yet exact criticality is usually thought to preclude stationarity because the mean activity diverges. I show that this conclusion is not generally valid for self-exciting Hawkes point processes. At criticality, stationarity in law is controlled not by the mean intensity, but by local finiteness of the infinite-past Poisson-cluster construction. The relevant object is the fixed-window hitting probability \(H_T(u)\), the probability that a cluster born at time \(-u\) contributes at least one event to a window of length \(T\). For memory tails \(\mathbb{P}(T>t)\sim t^{-\theta}\) and fertility tails \(\mathbb{P}(\kappa>x)\sim x^{-\gamma}\), I prove stationarity for \(1<\gamma<2\) and \(\theta>\gamma\) via a finite-mean-lifetime criterion. In the finite-memory, finite-variance regime, \(H_T(u)\) is asymptotically comparable to the cluster-survival probability, and the exact local-finiteness condition fails. A direct asymptotic analysis of \(H_T\) gives the sharper condition \(\theta>\gamma-1\) for stationarity to hold in the infinite-fertility-variance regime. Thus broad fertility fluctuations can stabilize critical Hawkes dynamics in law, producing locally finite stationary sample paths despite infinite mean activity.
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Optics

Simultaneous Type-0 and Type-I Optical Parametric Oscillation in Submicron Poled Thin-Film Lithium Niobate

Fengyan Yang and Hong X. Tang

We demonstrate dual optical parametric oscillations in a thin-film lithium niobate microring resonator enabled by polarization-insensitive submicron periodic poling. Under degenerate backward quasi-phase matching, the counter-propagating signal and idler wavevectors cancel, such that the phase-matching condition is determined solely by the pump wavevector. As a result, a single submicron poling period simultaneously supports both Type-0 (TM $\rightarrow$ TM + TM) and Type-I (TM $\rightarrow$ TE + TE) parametric interactions in the same device. Temperature-controlled resonance alignment further enables selective activation of either polarization channel. We observe optical parametric oscillation with thresholds of 290\,{\textmu}W for the Type-0 process and 3\,mW for the Type-I process, both comparable to state-of-the-art on-chip OPO thresholds. These results establish submicron poled TFLN as a compact and reconfigurable platform for polarization-diverse parametric light generation.
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Optics · Cross-listed from quant-ph

Dissipative acousto-mechanical parametric interface between high-overtone acoustics and flexural phonons

Xun Ji, Huanying Sun, Longhao Wu et al. (+4 more)

High-overtone bulk acoustic wave resonators (HBARs) promise advanced phononics, yet achieving nonlinearity remains challenging. We demonstrate a radiation-pressure-type parametric interaction between GHz HBARs and low-frequency flexural modes in a suspended silicon nitride membrane, where mechanical displacement modulates the external dissipation rate to enable dissipative acousto-mechanical coupling. Benefiting from the high quality factor, the system enters the resolved-sideband regime at room temperature, yielding acousto-mechanically induced transparency. We observe tunable Kerr nonlinearity and generate coherent HBAR frequency combs via two-tone driving. Notably, our dissipative coupling strength is 20 times larger than the dispersive coupling, the highest ratio among reported hybrid dissipative-dispersive coupling systems, resulting in the experimental observation of amplification in the reflection spectra under red-sideband driving. The ability to interface dense HBAR modes with a common mechanical resonator provides a scalable on-chip platform for multimode phononic information processing, with quantum phononics potentially achievable at sub-Kelvin temperatures.
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All authors Xun Ji, Huanying Sun, Longhao Wu, Qichun Liu, Yulong Liu, Mika A. Sillanpää, Tiefu Li

Atmospheric and Oceanic Physics

JAX-SCM v1.0: a modern atmospheric single-column model for boundary layer research

Maximilian Pierzyna

We present JAX-SCM v1.0, an open-source atmospheric single-column model for boundary layer research, implemented in Python using the JAX computing library. The model solves for horizontal wind, potential temperature, and specific humidity, combined with prognostic turbulent kinetic energy and turbulent statistics parameterized by the Mellor-Yamada-Nakanishi-Niino level-2.5 (MYNN-2.5) turbulence closure. We verify the implementation against three well-established benchmark cases covering neutral (turbulent Ekman layer), stable (GABLS1), and convective (Wangara Day 33) conditions. Close agreement with reference solutions is demonstrated across all regimes. By building on JAX, the model benefits from just-in-time compilation and native GPU support. While JAX-SCM is not yet fully differentiable, basing it on JAX also lays the foundation for future integration with machine learning components. The model is designed for simplicity and modularity, lowering the barrier to entry for users and developers alike.
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Optics

Hybrid spectral-spatial domain registration for nanometric tracking in digital in-line holographic microscopy

Kamil Kalinowski, Mikolaj Rogalski, Piotr Arcab et al. (+3 more)

Digital in-line holographic microscopy enables label-free tracking and metrology, but achieving nanometric sub-pixel displacement accuracy over a wide capture range remains challenging. Frequency-domain registration based on the discrete Fourier transform (DFT) is globally stable and tolerant to large shifts, yet it suffers from sub-pixel quantization and interpolation artifacts that limit precision near zero displacement. In contrast, spatial-gradient refinement such as Lucas-Kanade (LK) can reach very high sub-pixel accuracy, but it is strongly initialization-limited and prone to divergence outside a narrow convergence basin. Here we propose a Hybrid Spectral-Spatial Domain (HSSD) framework that resolves this trade-off by combining the global robustness of a DFT-based coarse estimator with LK refinement of the residual motion. The DFT stage provides reliable initialization and substantially extends the capture range, while the LK stage suppresses the precision floor by reducing interpolation and quantization errors characteristic of standalone frequency-domain methods. We validate HSSD using numerical simulations and experiments in a transmission in-line holographic imaging system, achieving nanoprecision displacement measurement and stable tracking across a wide range of displacements and defocusing conditions. This hybrid strategy enables reliable nanometric localization in regimes where standalone DFT or LK methods either fail to converge or saturate in accuracy.
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All authors Kamil Kalinowski, Mikolaj Rogalski, Piotr Arcab, Emilia Wdowiak, Piotr Zdankowski, Maciej Trusiak

Computational Physics · Cross-listed from cond-mat.stat-mech

Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization

Khen Cohen, Mark Glass, Meir Feder et al. (+1 more)

We introduce a physics-inspired continuous relaxation framework that yields substantially improved solutions for NP-hard combinatorial optimization problems, including Quadratic Unconstrained Binary Optimization (QUBO), binary sparse coding, and planted-solution Ising models. By parameterizing discrete binary variables as continuous wave-like states on the complex unit circle, we inherently smooth highly non-convex energy landscapes. We show that representing binary variables as complex phases reveals an implicit regularization mechanism that promotes convergence toward discrete states. Extracting this mechanism yields significant improvements even within standard real-valued optimization frameworks, using this regularizer explicitly. Empirically, this regularization yields vastly higher ground-state convergence rates than standard real-valued alternatives. Our models achieved zero error in large-scale 160x160 QUBO tasks under severe noise (sigma=0.25), and outperformed traditional algorithms (OMP and LASSO) in underdefined sparse coding with perfect recovery at sigma=0.15. The solver's robustness was further validated by recovering exact ground-state configurations in 8 out of 11 rigorously engineered planted-solution benchmarks.
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All authors Khen Cohen, Mark Glass, Meir Feder, Yaron Oz

Data Analysis and Statistics

On the Statistical Interpretation of Discoveries in LHC Data

S. V. Chekanov and E. J. Weik

We examine discovery criteria at the Large Hadron Collider (LHC) within a model-independent framework, with particular emphasis on the statistical signatures of new physics. This study is motivated by the recent shift from model-specific searches based on a small number of distributions to broad, model-agnostic strategies, which offer substantially greater sensitivity to unexpected phenomena. We revisit the well-known criterion of a local statistical significance of $5\,\sigma$ for the observation of new phenomena in invariant-mass distributions and discuss how this threshold should be modified to account for look-elsewhere effects arising not only from multiple bins within a given distribution, but also from the simultaneous consideration of multiple distributions. We present a simple but statistically conservative relation between local and global significances in the presence of multiple invariant-mass distributions at the LHC, which can serve as a useful first approximation for planning future measurements.
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Applied Physics

P-dit Probabilistic Ising Machine for Solving the Quadratic Assignment Problem

Christian Duffee, Chadbourne M. Burling-Smith, Jordan Athas et al. (+4 more)

Combinatorial optimization problems represent a wide range of real-world scenarios where complicated interactions make it difficult to find the best solution. One example is the quadratic assignment problem (QAP), which involves determining the optimal placement of facilities at set locations which minimizes the products of material flow and facility distance. This representation is descriptive of many real-world scenarios, including the aggregate transportation costs of a supply chain. In this work, a probabilistic Ising machine (PIM) approach is implemented using probabilistic d-dimensional variables (p-dits), which are generalized, multi-state and multi-dimensional extensions to probabilistic bits (p-bits). Each p-dit corresponds to a location and stochastically oscillates between facility assignments based on the influence of the other p-dits. We show that with the same runtime and CPU, the PIM finds the best-known solution on 95% of considered instances from the QAP Library dataset, compared to just 36% for the standard Gurobi solver. For the unique largest problem in the library, a 2 to 3 order-of-magnitude decrease is observed in the time needed to reach specific solution qualities. We also show parallelization of our PIM through GPU implementations. A comparison to state-of-the-art QAP solver algorithms shows that they are consistently outperformed by both CPU and GPU implementations of the p-dit Ising machine.
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All authors Christian Duffee, Chadbourne M. Burling-Smith, Jordan Athas, Andrea Grimaldi, Giovanni Finocchio, Ermin Wei, Pedram Khalili Amiri

Computational Physics · Cross-listed from math.NA

Geometry-Preserving Nudged Elastic Band and Dimer Methods under Anisotropic Force Uncertainty

Yifan Yu and Yangshuai Wang

The nudged elastic band (NEB) and Dimer methods are standard tools for computing minimum-energy paths and index-one saddle points in atomistic transition problems. They are increasingly driven by surrogate or learned force models, whose force errors are often anisotropic and spatially varying near transition states and defect cores, where saddle-search iterations are most sensitive. We introduce uncertainty-aware NEB and Dimer methods (UA-NEB, UA-Dimer) that use covariance as an optimizer-level reliability metric while preserving the mean-potential saddle-search equations: an oblique normal projection for NEB and covariance-weighted rotation and translation for Dimer. Both algorithms fit Robbins–Monro recursions; under a local Lyapunov stability hypothesis, verified explicitly for a canonical UA-NEB setting and stated as a hypothesis for UA-Dimer, the stochastic iterations converge almost surely within the corresponding local stability neighborhood. In the analytic benchmark, UA-NEB reduces mean barrier error by $21\%$ relative to stochastic NEB and UA-Dimer reduces the reflected-gradient residual by $22\%$; in the 127-atom tungsten-vacancy benchmark, full UA-NEB reduces mean barrier error by $56\%$ relative to stochastic NEB and by $23\%$ relative to diagonal covariance weighting. These results show that anisotropic uncertainty is most useful when embedded in the constrained geometry of the optimizer rather than collapsed into a scalar acquisition or trust criterion.
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Chemical Physics

A PEMFC-based combined cooling heating and power (CCHP) system with flexible energy supply: Thermodynamic and economic analyses

Yuanming Wang, Shaowen Deng, Rui Chen et al. (+3 more)

Proton exchange membrane fuel cell (PEMFC) systems offer a key approach to hydrogen utilization, and PEMFC-based combined cooling, heating, and power (CCHP) systems pave the way for an efficient and clean energy supply to buildings. In conventional PEMFC-CCHP systems, the heating/cooling capacity and electrical power output are strongly coupled, making it difficult to meet diverse energy demands. This paper presents a novel energy system that integrates an organic Rankine cycle and an absorption heat pump in a parallel configuration, which enables flexible regulation of electricity-cooling capacities in summer and electricity-heating capacities in winter by adjusting the splitting ratio of the waste heat. The impacts of the splitting ratio and key operating parameters on thermodynamic performance and economic performance are quantitatively evaluated. The results show that the ORC can improve electrical efficiency by 2.19 percentage points in summer and 2.78 percentage points in winter. When the current density is fixed at 0.4 A/cm2 and the splitting ratio increases from 0 to 0.5, the cooling capacity of the system varies from 1294 to 647 W, and the heating capacity varies from 2660 to 1330 W. The economic performance is more sensitive to electricity price and hydrogen price than to other parameters, confirmed by their high sensitivity coefficients for net present value (NPV) and internal rate of return (IRR). This system possesses excellent thermodynamic and economic properties, thereby offering significant potential for reducing building energy consumption and carbon emissions.
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All authors Yuanming Wang, Shaowen Deng, Rui Chen, Zhen Zeng, Tianyou Wang, Zhizhao Che

Optics

Wide-field mid-infrared cavity-enhanced upconversion imaging

Yue Song, Jianan Fang, Wen Zhang et al. (+5 more)

Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poled crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle up to 28.5$^\circ$ over a spectral coverage of 2.5–5 $\mu$m. Moreover, a thermal locking approach is used to stabilize the cavity at the high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO$_{2}$ gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications including environment monitoring, gas leakage inspection, and medical diagnostics.
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All authors Yue Song, Jianan Fang, Wen Zhang, Yijing Li, Ben Sun, Zhiwei Jia, Kun Huang, Heping Zeng

Friday 22 May

Computational Physics · Cross-listed from math-ph

xCPS: an xAct package for covariant phase space, Noether charges, and entropy

Juan Margalef-Bentabol

xCPS is an xAct tensor algebra package for symbolic computations within the covariant phase space formalism of field theories. From a generic Lagrangian, xCPS automates the derivation of equations of motion and symplectic currents. It systematically determines whether an infinitesimal transformation in the space of fields is a Noether symmetry and computes the associated Noether charge. Additionally, xCPS can in many cases determine whether a tensorial expression is a divergence and, if so, find its divergence potential. By implementing vertical exterior calculus through a graded, supercommutative wedge product and vertical operators, the package enables efficient computations in gauge theories and higher-derivative models of gravity, including the derivation of thermodynamic quantities like Wald's entropy. xCPS is open-source under the GPL license and available at https://github.com/juanmargalef.
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Optics

Time-frequency analysis of nonlinear Compton scattering via joint probability distributions

Nikita Larin and Daniel Seipt

The interaction of charged particles with an intense laser pulse gives rise to a number of characteristic spectral features of emitted radiation, including the generation of harmonics, spectral broadening due to the phase-dependent ponderomotive red shift, and the emergence of intricate sub-harmonic structures. These effects are accumulated over the course of the interaction with the electromagnetic field and are therefore inherently nonlocal in nature. For a deeper understanding of strong-field quantum electrodynamics (SFQED) processes and their practical applications, it is desirable to employ tools that enable simultaneous analysis in the time and energy domains. In time-frequency analysis, such tools are provided by joint distributions (JDs). In this work, we demonstrate how a JD can be devised within the SFQED framework. Specifically, we focus on constructing a non-negative JD, which allows for a clear probabilistic interpretation. We study the properties of the proposed distribution and test its utility by applying it to the nonlinear Compton scattering in complex laser pulse configurations with carrier-envelope phase and variable polarization.
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Computational Physics

Multi-Fidelity Learning with Shallow Recurrent Decoders for Multi-Physics Applications

Stefano Riva, Carolina Introini, J. Nathan Kutz et al. (+1 more)

In reactor physics, neutronics and multi-physics phenomena can be modelled at different fidelity levels. High-fidelity models based on the Boltzmann transport equation, multi-group diffusion, or computational fluid dynamics are computationally demanding, whereas simplified models, such as zero-dimensional lumped formulations, can be evaluated efficiently at the cost of neglecting spatial details. The computational intractability of detailed models translates into a scarcity of high-fidelity data and an abundance of low-fidelity data, motivating the development of multi-fidelity (MF) learning strategies able to map between the two. This work extends Shallow Recurrent Decoders (SHRED), a machine learning architecture that reconstructs high-dimensional fields from time-series measurements, to multi-fidelity reduced-order modelling, in which the input trajectories are provided by a low-fidelity model. The resulting MF-SHRED is assessed on three benchmark problems: (i) a two-group point-kinetics-to-diffusion; (ii) a non-linear reaction-advection-diffusion system of six chemical species; and (iii) the coupled neutronics-thermal-hydraulics multi-physics model of the Molten Salt Fast Reactor (MSFR). Across all three cases, MF-SHRED reconstructs the high-fidelity fields with relative errors below a few percent, closely approaching the truncation error of the underlying proper orthogonal decomposition, while reducing the computational cost by three orders of magnitude relative to the corresponding high-fidelity solver. MF-SHRED performs comparably to the original sparse-sensor SHRED formulation. These results support the use of MF-SHRED as a non-intrusive reduced-order modelling strategy for reactor physics and multi-physics applications, particularly for design and safety analysis tasks that must be carried out before a facility is even built.
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All authors Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi

Computational Physics

A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles

Maria Bånkestad, Sandra Barman, Magnus Röding et al. (+11 more)

Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids. Their multi-component architecture yields a core-shell structure. Small-angle X-ray scattering (SAXS) is an important characterization technique for LNPs, but recovering internal structure and size distribution from SAXS is an inverse problem with non-unique solutions. Realistic models are often too expensive for systematic exploration. We introduce a machine-learning-accelerated, differentiable framework for SAXS analysis of heterogeneous, polydisperse LNPs. The forward model combines a core-shell particle with a Gaussian random-field interior, a neural surrogate for the monodisperse SAXS map, and a differentiable layer integrating over particle-size distributions. The surrogate reduces prediction cost by four orders of magnitude, while differentiability enables large-scale multi-start fitting and ensemble identifiability analysis. Applied to synthetic and experimental MC3 LNP data, the framework shows that near-identical SAXS fits can arise from distinct parameter modes, with the experimental fits dominated by a trade-off between size-distribution and interior-structure parameters.
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All authors Maria Bånkestad, Sandra Barman, Magnus Röding, Erik Kaunisto, Viktoriia Meklesh, Audrey Gallud, Marco Mendez, Marianna Yanez Arteta, Stefan Norberg, Ann Terry, Smita Chakraborty, Shun Yu, Jerk Rönnols, Sepideh Pashami

Computational Physics

Automatic-Differentiation-Enabled Finite Element Model Updating: Learning Constitutive Neural Networks from Structural Deformation Data

Matthias Knipper, Chenyi Ji, Malte Brand et al. (+1 more)

The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional calibration methods based on homogeneous stress-strain experiments, particularly given the high dimensionality of trainable parameters. This work presents ADE-FEMU (Automatic-Differentiation-Enabled Finite Element Model Updating), an end-to-end differentiable framework that integrates a constitutive neural network model within a JAX-FEM nonlinear solver and identifies its parameters through gradient-based minimization of a measurement-mismatch loss, allowing for training on structural data with limited kinetic information. Thus, it can be considered as an extension of finite element model updating (FEMU) for hyperelastic materials, implemented within a GPU-accelerated, fully automatically differentiable finite element framework Newton tangent stiffness and loss gradients are computed automatically using forward- and reverse-mode automatic differentiation throughout the entire pipeline, thereby removing the need for analytic adjoints or offline surrogate models. ADE-FEMU is demonstrated for two architectures: a grey-box Constitutive Artificial Neural Network (CANN), a polyconvex, fully connected model with high flexibility, and a white-box CANN, an expert-system network with phenomenologically interpretable strain-energy terms. Focusing on incompressible isotropic hyperelasticity, ADE-FEMU is evaluated on three open experimental datasets: (1) full digital image correlation (DIC) of a perforated tensile specimen, (2) a reduced-data scenario with a one-dimensional stretch profile and global force-displacement curve, and (3) a heterogeneous matrix-inclusion system in which both phases constitutive laws are identified and generalized to twenty-two previously unseen samples.
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All authors Matthias Knipper, Chenyi Ji, Malte Brand, Kevin Linka

Instrumentation and Detectors

First results of a high sensitivity and transportable Ring Laser Gyroscope

A. Basti, G. Carelli, G. Di Somma et al. (+6 more)

Within the GINGER project, aimed at installing an array of large frame ring laser gyroscopes for fundamental physics tests and as part of a geophysics observatory located in the underground laboratory at Gran Sasso, Italy (LNGS-INFN), we are developing a ring laser gyroscope design to reduce spurious rotation of instrumental origin and the ability to extend the cavity side length from 1.5 up to 5 m, thanks to the implementation of suitable spacers. The new design led to a prototype, called TRIO, with a side length of 1.52 m, conceived as a transportable instrument. The present paper reports on several preliminary measurements of the Earth angular velocity carried out with TRIO. Results have the twofold objective to assess the instrument performance in the present geometrical configuration and to test validity of the design in view of the GINGER project. To this aim, data obtained with TRIO are compared with typical data acquired with other, previously made, gyroscope prototypes, including the large frame GINGERINO already in operation at the Gran Sasso underground site.
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All authors A. Basti, G. Carelli, G. Di Somma, A.D.V. Di Virgilio, F. Fuso, R. Macchia, E. Maccioni, P. Marsili, A. Pasqualetti

Physics and Society

Crisis, Disengagement, and Structural Realignment: A Threshold Model of Radical-Party Support

Alexander Omelchenko

When does a crisis-induced surge in radical-party support fade away, and when does it become a durable realignment? We address this in a mathematical sociology threshold model on a conserved population. The baseline admits a global classification through a Perron–Frobenius threshold. Adding a crisis-induced disengagement compartment, we separate state shocks (which alter the current state) from structural shocks (which alter parameters). State shocks affect transients but cannot move the long-run attractor; durable realignment requires structural threshold-crossing. We derive a critical shock amplitude and a finite mobilisation-window bound, and show that cumulative structural shifts can produce staircase realignment. A stylised illustration uses German federal elections, 2013–2025.
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Classical Physics

On radiation from hyperbolic motion, behavior of electromagnetic fields, and coordinate transformations at infinity

E. T. Akhmedov and M. N. Milovanova

We show explicitly that radiation from a uniformly accelerating charge escapes outside Rindler wedge, while within Rindler wedge there is no flux through infinity, neither in the Minkowski frame nor in the Rindler frame. This remains true despite the fact that the coordinate transformation between the Rindler and Minkowski frames is not trivial at infinity.
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Optics

Effective skyrmion number for mixed polarization states of light

Rosario Martínez-Herrero and Ángel S. Sanz

Polarization skyrmions are usually characterized through a unit Stokes vector field, which defines a map from the transverse plane to the Poincaré (Bloch) sphere and allows one to assign a skyrmion number under appropriate boundary conditions. This description assumes complete polarization. In many optical situations, however, the local polarization state is partially polarized and is therefore described by a spectral polarization matrix rather than by a single Jones vector. We formulate a density-matrix-based effective extension of the skyrmion-number construction for statistically stationary, quasi-monochromatic paraxial fields in terms of the normalized spectral polarization matrix. Its eigenvectors define two orthogonal polarization eigenstates, corresponding to antipodal points on the sphere, while the eigenvalue imbalance is fixed by the local degree of polarization \(P({\bf r})\). Averaging the opposite skyrmion-density contributions of these two eigenpolarizations leads to an effective skyrmion number in which the usual pure-state density is weighted by \(P({\bf r})\). The resulting quantity reduces to the conventional skyrmion number for fully polarized fields and vanishes in locally unpolarized regions. In general it is not an integer topological invariant; rather, it is an integrated effective measure, constructed from local Stokes parameters, of the skyrmionic content associated with the polarized part of a mixed optical field. We illustrate the construction with an analytic Gaussian-beam example in which a degree-one polarization texture is combined with a radially varying degree of polarization.
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Geophysics · Cross-listed from stat.AP

Scalable Gaussian Process for Learning Non-Ergodic Ground Motion Model from Physics-Based Simulations with Application to Power Infrastructure Assessment

Jinyan Zhao, Grigorios Lavrentiadis and Domniki Asimaki

This study presents the development and application of a scalable non-ergodic ground motion model (NGMM) for the Los Angeles area. The NGMM is trained and validated on physics-based simulated ground-motion data from a recent Statewide California Earthquake Center (SCEC) CyberShake study. The NGMM is formulated as a Gaussian Process (GP) regression model, where the prior median is defined as the ASK14 ergodic ground-motion model and the posterior median is obtained by learning the non-ergodic effects embedded in the training data. These non-ergodic effects include systematic site and path effects, which are represented in the GP using Matérn and specialized covariance kernels that explicitly characterize path vectors. Implementing the NGMM requires hyperparameter tuning and inference on large datasets (on the order of one million data points or more), posing significant computational challenges for conventional GP approaches. To address this scalability issue, this paper presents a suite of computational strategies, including sparse Cholesky inversion, parallel computing, GPU acceleration, and stochastic gradient descent minimization. Despite these advances, the full CyberShake dataset (on the order of hundreds of millions of data points) remains computationally prohibitive. Therefore, aleatory variability is modeled separately using a mixed-effects formulation to represent within-event and between-event variability. The developed NGMM has two primary applications: interpolation of partially observed ground-motion fields and predictive modeling for ground motions in unobserved earthquake scenarios. Validation results on independent datasets demonstrate accurate performance in both applications. A case study of power transmission network assessment in an Mw 6.7 Puente Hill scenario further demonstrated that the developed NGMM closely reproduces physics-based simulation results.
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Biological Physics · Cross-listed from cond-mat.soft

Resonances in Overdamped Odd Materials

Julius Kiln and Alexander Mietke

Odd viscoelasticity arises in parity-violating nonequilibrium materials, where it leads to unconventional mechanical responses and oscillatory relaxation even in overdamped systems. While many living and active chiral materials present promising candidates to exhibit odd viscoelasticity, there is currently no approach that allows for a rheological inference of the large number of elastic and viscous moduli that even a minimal isotropic odd viscoelastic material can depend on. Generalizing the century-old Papkovich-Neuber ansatz to active materials, our work introduces an odd Papkovich-Neuber (OPN) solution – an analytic solution for any isotropic linear odd fluid or solid, each described by up to 6 independent moduli – that enable us to study the boundary-driven response in geometries that mimic common rheology methods. OPN solutions reveal three physically distinct resonances in odd viscoelastic solids that are characteristic of the underlying material moduli and can all be interpreted within a single geometric framework. Underlying this unification is an equivalent description of overdamped odd viscoelastic materials in terms of damped harmonic oscillators. Resonances appear as the effective damping coefficients of these oscillators vanish, which is facilitated by the activity that powers odd material properties.
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Atmospheric and Oceanic Physics · Cross-listed from cs.CV

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing

Manuel Pérez-Carrasco, Maya Nasr, Zhan Zhang et al. (+16 more)

Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantification. We present a machine learning framework for plume detection from MethaneSAT retrieved column-averaged dry-air mole fractions of methane. We address two core challenges: the scarcity of labeled MethaneSAT data and the need for inference reliability across diverse atmospheric and surface conditions. We first demonstrate that Mask R-CNN with a ResNet-50 backbone outperforms a U-Net semantic segmentation baseline on both MethaneAIR (an airborne version of MethaneSAT) and MethaneSAT data, with pixel-level F1 score gains of 10.49 and 5.48 percentage points respectively. To address MethaneSAT data scarcity, we evaluate three cross-sensor transfer strategies leveraging MethaneAIR flights and synthetic plumes. Mask R-CNN with ResNet-50 fine-tuned from MethaneAIR pre-trained weights is the most effective strategy, achieving instance-level precision of 0.60 and recall of 0.98 at the baseline operating point. A physics-informed post-processing pipeline converts detections into two operationally distinct modes. The first is a high-sensitivity mode that applies morphological filtering and proximity-based merging for comprehensive emission screening, achieving precision of 0.71 and recall of 0.94. The second is a high-precision mode that additionally applies a distribution-based classifier for confident source attribution, achieving precision of 0.92 and recall of 0.70. Manual review of detections classified as false positives against our wavelet-based reference plume mask labels shows that some correspond to real methane enhancements... Our data and code used in this work are publicly available at: https://doi.org/10.7910/DVN/FR959H
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All authors Manuel Pérez-Carrasco, Maya Nasr, Zhan Zhang, Apisada Chulakadabba, Javier Roger, Raia Ottenheimer, Sébastien Roche, Maryann Sargent, Chris Chan Miller, Daniel Varon, Jack Warren, Luis Guanter, Kang Sun, Jonathan Franklin, Jia Chen, Cecilia Garraffo, Xiong Liu, Ritesh Gautam, Steven Wofsy

Instrumentation and Detectors

Characterization of Aluminum Microwave SQUID Multiplexers for CE$\nu$NS Detection

James Amidei, Antoine Armatol, Corinne Augier et al. (+92 more)

We present the design, fabrication, and characterization of an aluminum-based six-channel microwave SQUID multiplexer ($\mu$MUX) prototype for transition-edge sensor (TES) readout in the RICOCHET experiment. The device consists of aluminum coplanar-waveguide resonators and RF SQUIDs with Dolan-style Al/AlO$_x$/Al Josephson junctions. By measuring the resonator scattering parameters at a range of probe tone frequencies, powers, and flux bias points, we demonstrate agreement between the device response and existing multiplexer models. We also characterize the noise performance in both open-loop and flux-ramping modes. With a high electron mobility transistor (HEMT) amplifier, open-loop measurements yield a flux sensitivity of 1-1.5 $\mu\Phi_0/\sqrt{Hz}$. With flux-ramp modulation, low-frequency 1/f noise is suppressed, and the flux sensitivity is around 3-4 $\mu\Phi_0/\sqrt{Hz}$, corresponding to a current sensitivity of 24-33 $pA/\sqrt{Hz}$ at the input coil. We further demonstrate a reduction in readout noise by incorporating a Josephson traveling-wave parametric amplifier (JTWPA) between the $\mu$MUX and the HEMT. This achieves an open-loop flux sensitivity of 0.3-0.6 $\mu\Phi_0/\sqrt{Hz}$ and an effective system noise temperature below 1 K. These results establish aluminum $\mu$MUX devices as a viable and extensible readout technology for low-noise cryogenic detector arrays.
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All authors James Amidei, Antoine Armatol, Corinne Augier, Louis Bailly-Salins, Guillaume Baulieu, Laurent Bergé, Julien Billard, Juliette Blé, Gaby Brenot, Guillaume Bres, Jean-Louis Bret, Alexandre Broniatowski, Martino Calvo, Antonella Cavanna, Antoine Cazes, Emanuela Celi, David Chaize, Mohammed Chala, Maurice Chapellier, Luke Chaplinsky, Ran Chen, Ion Cojocari, Jules Colas, Laurent Couraud, Elspeth Cudmore, Maryvonne De Jesus, Pierre de Marcillac, Nicole Dombrowski, Louis Dumoulin, Alan Durnez, Romain Faure, Sylvain Ferriol, Enectali Figueroa-Feliciano, Joseph A. Formaggio, Stephane Fuard, Jules Gascon, Cloé Girard-Carillo, Andrea Giuliani, Corinne Goy, Cyrille Guerin, Leïla Haegel, Patrick M. Harrington, Scott A. Hertel, Cyrus F. Hirjibehedin, Christophe Hoarau, Ziqing Hong, Daemon Howard, Jean-Christophe Ianigro, Yong Jin, Alexandre Juillard, Dimitar Karaivanov, Temirlan Khussainov, Andrew Kubik, Jacob Lamblin, Tatiana Le Bellec, Laetitia Leroy, Mingyu Li, Alexey Lubashevskiy, Stefanos Marnieros, Romain Martin, Nicolas Martini, Julien Minet, Alessandro Monfardini, Franck Mounier, Bethany M. Niedzielski, Valentina Novati, Wiliam D. Oliver, Emiliano Olivieri, H. Douglas Pinckney, Denys V. Poda, Dmitrii Ponomarev, Jean-Sébastien Real, Faith C. Reyes, Alejandro Rodriguez, Sergey Rozov, Irina Rozova, Brianna Ryan, Deeksha Sabhari, Silvia Scorza, Kyle Serniak, Renaud Serra, Yegor Shevchik, Torsten Soldner, Hannah Stickler, Caitlyn Stone-Whitehead, Christian Ulysse, Wouter Van De Pontseele, Sergey Vasilyev, Paul Vittaz, Steven Weber, Evan Wolfe, Wayne Woods, Evgeny Yakushev, Jiatong Yang, Daniya Zinatulina

Atomic and Molecular Clusters · Cross-listed from cond-mat.mtrl-sci

ChatMOSP: A Chemistry-Grounded Mobile Agent for Working-State Catalyst Simulations

Sanyang Ye, Rui Qi, Beien Zhu et al. (+1 more)

Catalytic nanoparticles restructure dynamically under reaction conditions, so their working morphology and activity are governed by temperature, pressure, and gas composition. However, converting experimentally specified environments into physically meaningful morphology-performance simulations remains difficult because the translation of reaction conditions into model-specific energetic, kinetic, and execution parameters requires the specialized knowledge in computational catalysis. Here we report ChatMOSP, a chemistry-grounded mobile scientific agent that translates natural-language and voice-expressed catalytic requests into parameter-validated simulations using the Multi-scale Operando Simulation Package. ChatMOSP maps catalyst identity, temperature, pressure, gas composition, and target observables onto multiscale structure reconstruction and kinetic Monte Carlo tasks, retrieves database parameters or constructs missing inputs from an online literature-retrieval workflow, and executes validated MOSP workflows. Using CO oxidation on Pd nanoparticles as an example, we verify the ChatMOSP simulations capture the temperature-induced transition from faceted to rounded morphologies observed by in-situ TEM experiments either by built-in database or from web-retrieved literature information when the parameters are absent. Moreover, we demonstrate the capability of ChatMOSP to perform end-to-end study at mobile devices to simulate a pressure-coverage-morphology-activity feedback cycle for Pt CO oxidation to interpret the oscillatory CO conversion. These results establish ChatMOSP as a physically constrained mobile agent for accessible and interpretable catalyst working-state simulations.
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All authors Sanyang Ye, Rui Qi, Beien Zhu, Yi Gao

Optics

Dynamic Coherent Diffractive Imaging Using Only a Support Constraint in the Complex Plane

Yaocheng Tian, Taichi Tsuchiya, Yu-chen Karen Chen-Wiegart et al. (+3 more)

We show that a bounded temporal increment prior on the sample dynamics is sufficient to reconstruct a time-varying phase object from a near-field diffraction movie, under the thin-film approximation. The time evolution of the field is parameterized by a multiplicative inter-frame update factor, and a bound on its complex-plane support enforces a bounded phase increment and a passive amplitude constraint. Reconstruction of the dynamic field is thereby converted into a feasibility problem with two projection operators: a measurement-domain modulus projection and an object-domain circular-sector projection. We validate the approach experimentally using a spatial light modulator as a calibrated dynamic sample in two cases: a reaction–diffusion phase pattern with spatially expanding extent, and a growing phase pattern whose accumulated phase reaches $10\pi$. In both cases the reconstructed phase trajectory agrees well with the ground truth. We then apply the same framework, without modification, to in-situ monitoring of a photo-polymer 3D printing process, recovering the spatiotemporal phase induced by polymerization under a spatially patterned blue light. The reconstructed phase trajectory provides an observable for photo-chemical system identification and process control.
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All authors Yaocheng Tian, Taichi Tsuchiya, Yu-chen Karen Chen-Wiegart, Horacio D. Espinosa, Yuichiro Kunai, George Barbastathis

Chemical Physics

Excitation density controlled regimes of collective light–matter dynamics

Wenxiang Ying and Abraham Nitzan

Theoretical descriptions of collective light–matter dynamics often rely on the mean-field (MF) or single-excitation (SE) approximations, yet the parameter regimes where they apply are rarely clearly delineated. Here we show that representative limiting regimes are characterized by two independent parameters: the number of molecules $N$ and the excitation number $N_{\rm exc}$. In the Tavis–Cummings model, when $N\gg 1$ and the excitation density $N_{\rm exc} / N \to 0$, MF and SE descriptions agree and yield linear collective dynamics, showing harmonic Rabi oscillations. At finite excitation density ($N_{\rm exc} / N \sim \mathcal{O}(1)$), the large-\(N\) limit remains accurately described by MF dynamics but becomes nonlinear in $N_{\rm exc} / N$, manifested by a Duffing equation for the cavity amplitude with anharmonic Rabi frequency. We further show that cluster expansion systematically restores finite-$N$ correlations beyond MF. When local vibronic interactions are included, the same linear collective limit is reached by both approximations, with SE reaching it through polaron decoupling and MF through linearization. This two-parameter regime map clarifies the limits in which different theoretical descriptions provide controlled descriptions of collective light–matter dynamics.
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Fluid Dynamics

Quasi-DNS with chemical kinetics for near blow-out dynamics of a single multi-injection burner element for future gas turbine applications

Kazuki Abe, Youhi Morii and Kaoru Maruta

Gas turbine combustors increasingly operate close to lean blow-out (LBO) limits, where small changes in fuel-air mixing or flow structure can destabilize the flame and alter near-blow-out dynamics. Conventional design practice relies mainly on Reynolds-averaged Navier-Stokes (RANS) simulations, which often overpredict turbulent mixing and cannot resolve unsteady flame anchoring and local extinction near burner hardware. We develop a Quasi-DNS workflow with detailed methane-air chemistry for a single element of a multiple-injection burner, modeled as a coaxial burner with a mixing tube and downstream combustion chamber, as a methodological basis for near-blow-out analysis. The workflow is implemented in OpenFOAM and comprises: (i) a simplified three-dimensional sector geometry with a 30-degree domain to capture circumferential vortices at the mixing tube outlet, (ii) boundary conditions and inlet profiles reproducing coaxial jet and preheated air conditions, (iii) a reduced Yang-Pope mechanism validated against GRI 3.0 using Cantera laminar flame speed, ignition delay, and counterflow diffusion flame calculations, (iv) grid generation and convergence checks based on flame structure and scalar dissipation rate, and (v) diagnostics including methane- and CO-based flame indices to classify local combustion modes. We demonstrate the workflow for a partially premixed methane flame at an overall equivalence ratio of phi = 0.45 and a perfectly premixed reference at the same phi. The Quasi-DNS results show limited mixing inside the mixing tube and strong vortical mixing at the outlet, leading to extended and structured heat-release regions that differ markedly from the smoother, more compact flames predicted by RANS. The methodology provides a reusable framework for analyzing flame structure, stabilization, and LBO-relevant dynamics in coaxial burners with mixing tubes.
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