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Physics Physics

physics Watch Physics

Wednesday 18 March

Instrumentation and Detectors

Simulation Based Characterization of Deconvolution-Based PMT Waveform Reconstruction Under Large Charge Dynamic Range and Varying Scintillation Time Profiles

Xingyi Lin, Jinghuan Xu, Yongbo Huang et al. (+3 more)

Photomultiplier tubes (PMTs) are widely used as photon sensors for neutrino and dark matter detection. Accurate charge and time information extracted from PMT waveforms is crucial for event reconstruction. An algorithm based on deconvolution technology was proposed and applied to the reconstruction of PMT waveforms. This study further investigated the reliability of the deconvolution algorithm when handling a large charge dynamic range (0-200 photoelectrons), varying scintillation time profiles, and muon-induced large signals. Monte Carlo data confirmed that the deconvolution algorithm exhibits relatively stable reconstruction performance: under the simulation conditions described in this paper (including a noise level of 0.1 PE, single photoelectron charge resolution of 30%, 1 GHz sampling rate, 1000 ns window, three undershoot configurations, and eight scintillation time profiles), the residual non-linearity of charge reconstruction is controlled to approximately 1% over the range of 0 to 200 photoelectrons, and the algorithm is capable of handling muon-induced large signals. The reconstruction performance depends on adequate baseline recovery; a waveform window that is too short relative to the undershoot tail leads to degraded reconstruction quality, which can be mitigated by extending the sampling window.
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All authors Xingyi Lin, Jinghuan Xu, Yongbo Huang, Jingzhe Tang, Tianying Xiao, Yingke Li

Physics and Society · Cross-listed from cs.LG

Conditional Inverse Learning of Time-Varying Reproduction Numbers Inference

Lanlan Yu, Quan-Hui Liu, Haoyue Zheng et al. (+1 more)

Estimating time-varying reproduction numbers from epidemic incidence data is a central task in infectious disease surveillance, yet it poses an inherently ill-posed inverse problem. Existing approaches often rely on strong structural assumptions derived from epidemiological models, which can limit their ability to adapt to non-stationary transmission dynamics induced by interventions or behavioral changes, leading to delayed detection of regime shifts and degraded estimation accuracy. In this work, we propose a Conditional Inverse Reproduction Learning framework (CIRL) that addresses the inverse problem by learning a {conditional mapping} from historical incidence patterns and explicit time information to latent reproduction numbers. Rather than imposing strongly enforced parametric constraints, CIRL softly integrates epidemiological structure with flexible likelihood-based statistical modeling, using the renewal equation as a forward operator to enforce dynamical consistency. The resulting framework combines epidemiologically grounded constraints with data-driven temporal representations, producing reproduction number estimates that are robust to observation noise while remaining responsive to abrupt transmission changes and zero-inflated incidence observations. Experiments on synthetic epidemics with controlled regime changes and real-world SARS and COVID-19 data demonstrate the effectiveness of the proposed approach.
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All authors Lanlan Yu, Quan-Hui Liu, Haoyue Zheng, Xinfu Yang

Fluid Dynamics · Cross-listed from cs.LG

Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer

Mohammad Nooraiepour

Accurate prediction of permeability tensors from pore-scale microstructure images is essential for subsurface flow modeling, yet direct numerical simulation requires hours per sample, fundamentally limiting large-scale uncertainty quantification and reservoir optimization workflows. A physics-informed deep learning framework is presented that resolves this bottleneck by combining a MaxViT hybrid CNN-Transformer architecture with progressive transfer learning and differentiable physical constraints. MaxViT's multi-axis attention mechanism simultaneously resolves grain-scale pore-throat geometry via block-local operations and REV-scale connectivity statistics through grid-global operations, providing the spatial hierarchy that permeability tensor prediction physically requires. Training on 20000 synthetic porous media samples spanning three orders of magnitude in permeability, a three-phase progressive curriculum advances from an ImageNet-pretrained baseline with D4-equivariant augmentation and tensor transformation, through component-weighted loss prioritizing off-diagonal coupling, to frozen-backbone transfer learning with porosity conditioning via Feature-wise Linear Modulation (FiLM). Onsager reciprocity and positive definiteness are enforced via differentiable penalty terms. On a held-out test set of 4000 samples, the framework achieves variance-weighted R2 = 0.9960 (R2_Kxx = 0.9967, R2_Kxy = 0.9758), a 33% reduction in unexplained variance over the supervised baseline. The results offer three transferable principles for physics-informed scientific machine learning: large-scale visual pretraining transfers effectively across domain boundaries; physical constraints are most robustly integrated as differentiable architectural components; and progressive training guided by diagnostic failure-mode analysis enables unambiguous attribution of performance gains across methodological stages.
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Plasma Physics

DustNET: enabling machine learning and AI models of dusty plasmas

Zhehui Wang, Justin C. Burton, Niklas Dormagen et al. (+30 more)

Dusty plasmas are ubiquitous throughout the universe, spanning laboratory and industrial plasmas, fusion devices, planetary environments, cometary comae, and interstellar media. Despite decades of research, many aspects of their behavior remain poorly understood within a unified framework. While numerous theoretical and numerical models describe specific phenomena, such as dust charging, transport, waves, and self-organization, fully predictive models across the wide range of spatial and temporal scales in both laboratory and natural systems remain elusive. Conventional plasma descriptions rely on coupled differential equations for particle densities, momenta, and energies, but their solutions are often limited by computational cost, numerical uncertainties, and incomplete knowledge of boundary conditions and transport processes. Recent advances in machine learning (ML), particularly deep neural networks, offer new opportunities to complement traditional physics-based modeling. Here we review ML and artificial intelligence (AI) approaches, termed bottom-up data-driven methods, for dusty plasma research. Central to this effort is Dust Neural nEtworks Technology (DustNET), a community-driven dataset initiative inspired by ImageNet, integrating experimental, simulation, and synthetic data to enable predictive modeling, uncertainty quantification, and multi-scale analysis. DustNET-trained models may also be deployed in real-time experimental settings under edge computing constraints. Combined with emerging multi-modal AI foundation models and autonomous agents, this framework provides a pathway toward a unified, physics-informed understanding of dusty plasmas across laboratory, industrial, space, and astrophysical environments.
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All authors Zhehui Wang, Justin C. Burton, Niklas Dormagen, Cheng-Ran Du, Yan Feng, John E. Foster, Susan S. Glenn, Max Klein, Christina A. Knapek, Lorin Matthews, André Melzer, Edward Thomas, Chuji Wang, Jalaan Avritte, Shan Chang, Neeraj Chaubey, Pubuduni Ekanayaka, John A. Goree, Truell Hyde, Chen Liang, Zhuang Liu, Zhuang Ma, Ilya Nemenman, Elon Price, A. S. Schmitz, Mike Schwarz, Saikat C. Thakur, M. H. Thoma, Hubertus M. Thomas, L. Wimmer, Wei Yang, Zimu Yang, Xiaoman Zhang

Computational Physics

Modeling Decay Heat with a Simplified Depletion Chain in OpenMC

Tanmay Gupta and Benoit Forget

OpenMC can be used to computationally model depletion and produce estimates of decay heat. As an input to depletion simulations, OpenMC requires a depletion chain that details nuclide transmutation pathways. The simplified CASL depletion chain was designed to track relatively few nuclides while still accurately modeling the effective neutron multiplication factor and nuclide number densities. However, the CASL chain dramatically underestimates decay heat due to the many nuclides it does not contain. In this work, we modify the CASL depletion chain to improve its accuracy while maintaining its computational efficiency. We demonstrate the effectiveness of adding pseudo-nuclides to the CASL chain, with each pseudo-nuclide capturing the behavior of a large group of nuclides. We further introduce "delay nuclides," which dramatically improve the accuracy of decay heat estimates.
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Classical Physics · Cross-listed from math-ph

Generalized Snell's laws for rough interfaces

Christophe Gomez and Knut Sølna

In this paper, we consider the reflection and transmission problem of waves by a rapidly oscillating rough interface that exhibits general mixing properties. Using an asymptotic analysis based on a separation of scales, corresponding to a paraxial (parabolic) scaling regime, we precisely characterize the specular and speckle (diffusive) components of the reflected and transmitted fields. A critically scaled interface is considered, in the sense that the amplitudes of the interface fluctuations and the central wavelength are of the same order. When the correlation length of the interface fluctuations is of the same order as the beam width, random specular components arise in both the reflected and transmitted waves, while no speckle component is observed. Equivalently, the reflected and transmitted fields are essentially confined to the cones formed by the specular components (specular cones) with directions given by the classical Snell's law of reflection and refraction. When the correlation length is smaller than the beam width, a specular homogenization regime emerges. In this case, the rough interface can be approximated by an effective flat interface, yielding deterministic specular reflected and transmitted cones. However, broader cones containing the specular cones appear, within which the wavefields form speckle patterns (speckle cones) whose total energy is of leading order. We provide the two-point correlation functions of these speckle patterns and establish a central-limit-theorem-type result, showing that they can be modeled as Gaussian random fields. These results enable the identification of generalized Snell's laws of reflection and transmission, which depend on an effective scattering operator at the interface.
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Chemical Physics · Cross-listed from cond-mat.soft

Free-Energy Analysis of Bubble Nucleation on Electrocatalytic Surfaces

Qingguang Xie, Paolo Malgaretti, Othmane Aouane et al. (+2 more)

Bubble nucleation at catalyst surfaces plays a critical role in the operation of electrolyzers. However, achieving controlled bubble nucleation remains challenging due to limited understanding of the underlying mechanisms. Here, we present a free-energy model that quantitatively predicts both the activation energy and critical nucleus size of bubbles at given supersaturation, temperature, pressure, and surface wettability. We find that the activation energy $\Delta G_{max}$ decreases with increasing supersaturation $\zeta$, following a power-law scaling of $\Delta G_{max} \sim \zeta^{-2}$, while the critical nucleus radius $R_c$ scales as $R_c\sim \zeta^{-1}$. Our theoretical predictions for the critical nucleus radius of hydrogen, oxygen and nitrogen bubbles are in quantitative agreement with experimental measurements. Finally, we present a simple model that couples gas diffusion and electrochemical reaction kinetics to determine the maximum gas supersaturation at a given current density. Our results advance the fundamental understanding of bubble nucleation at catalyst surfaces and provide practical guidelines for catalyst layer design to improve the performance of electrolyzers.
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All authors Qingguang Xie, Paolo Malgaretti, Othmane Aouane, Simon Thiele, Jens Harting

Fluid Dynamics

Adaptive near-contact repulsion in conservative Allen-Cahn phase-field lattice Boltzmann multiphase model

Andrea Montessori, Maria Rosa Lisboa, Marco Lauricella et al. (+1 more)

Unresolved thin-film dynamics often causes spurious coalescence in diffuse-interface simulations of multiphase flows. We address this issue by introducing a fully local repulsive near-contact flux in a conservative Allen–Cahn phase-field model coupled to lattice Boltzmann hydrodynamics. The interaction activates only for oppositely oriented nearby interfaces, with a strength that self-adjusts based upon an analytical estimate of the local film thickness extracted from the phase field. The resulting method circumvents nonlocal geometric procedures, preserves computational efficiency, and is well suited to massively parallel implementations. Tests on collision benchmarks and three-dimensional bubble swarms demonstrate robust suppression of artificial merging and physically consistent near-contact dynamics.
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All authors Andrea Montessori, Maria Rosa Lisboa, Marco Lauricella, Sauro Succi

Fluid Dynamics

Entrainment of magnetic fluid by a moving boundary of a plane gap

Denis S. Goldobin and Yuriy L. Raikher

A fluid mechanics problem is solved which technological prototype is a fluid acoustic contact that is an inherent element of ultrasonic non-destructive testing procedures. It is well known that the acoustic contact established with an ordinary fluid suffers from essential disadvantage that is the loss of stability due to the gravity-induced fluid leakage in the course of dynamic scanning. The use of magnetic fluid (MF) is one of the ways to resolve the issue. A compact portion of MF held in place by a permanent magnet enables one to maintain a stable acoustic contact (fluid bridge) under arbitrary orientation of the ultrasonic sensor and, simultaneously, to radically minimize the drain of the contact fluid. The model system under consideration comprises a MF bridge that fills a flat gap, one of whose boundaries moves with constant velocity. Due to its wetting by the fluid, the receding plane carries away a fluid film thus depleting the contact. Theoretical expressions are obtained which define the profile of the film in the dynamic regime and the dependencies of the magnetic fluid drain on the boundary velocity, gap height and configuration of the imposed magnetic field. On that basis the optimal parameters are evaluated which ensure effective retention of the fluid contact under minimal drain of the fluid from it.
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Popular Physics

Why the Future Is Not Trading: Causally Inert Events as a Test for Time Travelers

David Awad

We present an empirical argument against the existence of single timeline backward time travel using the price behavior of prediction markets. If rational agents could travel backward in time, binary prediction contracts would converge to degenerate prices (0 or 1) immediately upon market formation. We observe no such behavior across large datasets of resolved contracts. This yields a directly falsifiable prediction and sharpens prior economic arguments while avoiding reliance on physical experimentation. The argument requires only the existence of a single profit motivated agent in the future capable of interacting with markets along a closed timelike curve intersecting the market's spacetime location. We further argue that such agents would have no incentive to conceal trades in causally inert events, where outcomes are independent of market prices, implying that any such activity would be visible in aggregate price behavior. While many worlds interpretations evade this test, we argue that only single timeline models are empirically falsifiable, and prediction market evidence is inconsistent with their existence.
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Plasma Physics

Low-dimensional geometry learning for turbulence prediction in optimized stellarators

Xishuo Wei, Handi Huang, Haotian Chen et al. (+4 more)

The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reactor design, global physics such as turbulent transport also need to be optimized besides the confined single particle orbit, or properties estimated using local estimations and heuristic formulations. The first-principle global transport code is too computationally expensive to integrate into the optimization process. The fast surrogate global transport model based on machine learning is a good alternative choice, but the amount of data required to train the surrogate model is numerous due to the high degree-of-freedom of the stellarator design. The work shows that the stellarator design with quasi-helically(QH) symmetric geometry is approximately distributed in a low dimensional latent space, which can be explicitly found by deep learning. This discovery makes it possible to generate global gyrokinetic simulation data for training surrogate models to directly optimize the stellarator geometry for turbulent transport, energetic particle instability, and MHD modes. Using the low dimensional latent space and data analysis methods, the relation between linear zonal residues and axis-excursion is found, providing a simple guide to optimize low turbulent transport QH stellarators.
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All authors Xishuo Wei, Handi Huang, Haotian Chen, Hongxuan Zhu, Zhe Bai, Samuel Williams, Zhihong Lin

Optics

Low-Loss Optical Nanofibers with Submicron Waist Diameters and Millimeter-Scale Waist Lengths

Guanghui Su, Timothy H. Nguyen, Balthazar Loglia et al. (+5 more)

Optical nanofibers with subwavelength diameters generate strong evanescent fields, enabling efficient light-matter interactions for optical sensing, spectroscopy, and cold-atom experiments. We report a heat-and-pull system for fabricating low-loss optical nanofibers with controllable waist dimensions and investigate the fabrication limits for achieving small waist diameters and long waist lengths. We study factors that influence fabrication performance, including flame geometry, nanofiber dimensions, and surface contamination. Using a multi-hole torch tip that provides a relatively large and uniform heating region, we achieve reproducible fabrication with optical transmission above $99.9\%$ for waist diameters as small as 200 nm for a 1-mm waist length and 250 nm for a 50-mm waist length. We also develop a preparation procedure for fiber splicing and cleaning to minimize transmission loss caused by surface contamination. In addition, we measure long-term transmission degradation due to dust accumulation in a typical laboratory environment and find that nanofibers fabricated in an enclosed setup maintain transmission above $85\%$ for more than 1 hour for nanofibers with a 300-nm waist diameter and waist lengths ranging from 1 to 30 mm. Our work provides practical guidelines for constructing nanofiber fabrication platforms and producing low-loss nanofibers for optics and atomic physics applications.
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All authors Guanghui Su, Timothy H. Nguyen, Balthazar Loglia, Aaron Weinstein, Hanbo Yang, Nami Uchida, Mariam Mchedlidze, Xuejian Wu

Instrumentation and Detectors

Design and First Results of COFFEE3: A 55nm HVCMOS Pixel Sensor Prototype for High-Energy Physics Applications

Xiaomin Wei, Zijun Xu, Weiguo Lu et al. (+25 more)

Motivated by the stringent requirements of the Upstream Pixel (UP) tracker in the LHCb Upgrade II and the Inner Tracking detector (ITK) of the Circular Electron Positron Collider, the COFFEE series of pixel sensor chips have been developed using a 55nm High-Voltage CMOS (HVCMOS) process. The primary objective is to achieve a time resolution of a few nanoseconds under a hit density of up to 100 MHz/cm$^2$, while maintaining fine spatial resolution ($\sim$10 $\mu$m) and reasonable power consumption ($<$200 mW/cm$^2$). Building on the process validation of the COFFEE2 prototype, this work presents the design and preliminary test results of COFFEE3-a prototype integrating two distinct readout architectures. Architecture 1, tailored for the current triple-well process, adopts NMOS-only in-pixel circuitry and innovative column-level readout to handle high hit densities. The time walk of pixel-level signal is controlled within 10 ns, and the Time of Arrival (TOA) and Time over Threshold (TOT) are measured with a system clock with the period of 25 ns in peripheral circuits. Architecture 2, developed for future possible processes with p-type buried layer isolation, features pixel-level time measurement and storage. A chip-level Time-to-Digital Converter (TDC) is used and the part of Voltage-Controlled Delay Line (VCDL) is copied in each pixel to get a high time resolution. The TOA resolution is estimated to be 4.2 ns and the TOT resolution 8.4 ns. COFFEE3, with a layout size of 3$\times$4 mm$^2$, was manufactured and has undergone preliminary tests. Charge injection tests for analog circuits, and laser tests for full readout chains, confirm that both architectures operate as expected. Next step work will focus on characterizing key performance such as the timing resolution, radiation hardness, and tracking performance of minimum ionising particles.
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All authors Xiaomin Wei, Zijun Xu, Weiguo Lu, Yang Zhou, Zhan Shi, Leyi Li, Xiaoxu Zhang, Pengxu Li, Jianpeng Deng, Yang Chen, Yujie Wang, Zhiyu Xiang, Mei Zhao, Cheng Zeng, Mengke Cai, Boxin Wang, Yuman Cai, Bingchen Yan, Anqi Wang, Yu Zhao, Zexuan Zhao, Zheng Wei, Huimin Wu, Ruiguang Zhao, Hongbo Zhu, Yongcai Hu, Jianchun Wang, Yiming Li

Atomic Physics

Time-resolving the birth of photoelectrons in strong-filed ionization with an isolated attosecond pulse

Kunlong Liu, Yidian Tian and Pengcheng Li

To time-resolve attosecond electronic dynamics in general photoionization processes, the technique that retrieves the phase of emitted electronic wave packets without intercepting the interactions is essential. Here, we theoretically demonstrate a scheme that uses isolated attosecond pulses (IAPs) to achieve this goal. Our approach utilizes the coherent interference between the electronic wave packets of interest and the one produced by a subsequent IAP. It is shown that the photoelectron spectral phase that has eluded direct detection so far can be fully recovered from observable photoelectron spectra without perturbing the electron-release process under investigation. By further performing a time-frequency-like analysis on the photoelectron energy spectra with the spectral phase, we reveal the birth processes of photoelectrons in time and the association between electronic energy and birth time in strong-field ionization driven by circularly polarized laser pulses. The present work explores a promising application of IAPs for ultrafast measurement and opens a viable venue for investigating electronic dynamics with quantum phase information.
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Chemical Physics · Cross-listed from stat.AP

Analysis of molecular dynamics simulation data via statistical distances between covariance matrices

Yusuke Ono, Takumi Sato, Kenji Yasuoka et al. (+1 more)

Molecular dynamics (MD) simulations are powerful tools for elucidating the macroscopic physical properties of materials from microscopic atomic behaviors. However, the massive, high-dimensional datasets generated by MD simulations pose a significant challenge for analysis, necessitating efficient dimensionality reduction and feature extraction techniques. While existing methods such as principal component analysis and unsupervised learning have been utilized, issues regarding data efficiency and computational cost remain. In this study, we propose a statistical analysis framework focusing on the analysis of the particle data distributions through their covariance matrices, corresponding to the second-order moments of MD trajectory data. Discrepancies between system states are quantified using statistical distances between these covariance matrices. By applying dimensionality reduction to the resulting distance matrix, we extract lower-dimensional features that characterize the systems' dynamics. We validate the proposed method using Lennard-Jones (LJ) particle systems under different temperature conditions, as well as separate bulk systems of ice and liquid water. The results of LJ particles demonstrate an approximately linear correlation between the first principal component obtained through dimensionality reduction of the distance matrix and the diffusion coefficient. This suggests that global physical properties can be effectively inferred from local statistical information, such as covariance matrices, offering a data-efficient alternative for analyzing complex molecular systems. Furthermore, in the case of separate bulk systems of ice and liquid water, the method successfully distinguishes between the two phases, highlighting its potential for characterizing phase transitions and structural differences in molecular systems.
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All authors Yusuke Ono, Takumi Sato, Kenji Yasuoka, Linyu Peng

Optics

Virtual Polarization Modulation: Enabling CSI-Free DCO-OFDM over Dynamic OWC Channels

Tian Cao, Ping Wang, Tianfeng Wu et al. (+2 more)

In dynamically varying optical wireless communication (OWC) links, conventional quadrature amplitude modulation (QAM) in optical orthogonal frequency-division multiplexing (OFDM) requires frequent channel estimation and equalization, incurring pilot overhead and processing latency. This paper proposes a virtual polarization modulation (VPM)-based direct-current-biased optical OFDM (DCO-OFDM) scheme that maps each data symbol onto the three-dimensional Stokes space and places its corresponding Jones vector across two adjacent OFDM subcarriers. Using a rotation-based analytical framework, closed-form symbol error rate (SER) expressions are derived for arbitrary spherical constellations, along with upper and lower bounds and high signal-to-noise ratio (SNR) approximations. The framework is further extended to practical OWC scenarios with frequency-selective channels and atmospheric turbulence. Monte Carlo (MC) simulations validate the theoretical results. The results show that under practical OWC impairments, VPM outperforms QAM with least-squares (LS) channel estimation and minimum mean square error (MMSE) equalization. At a target SER of $10^{-5}$, 16-VPM achieves SNR gains of approximately 7.5 dB and 4 dB over equalized 16-QAM and 8-QAM, respectively, in frequency-selective channels, and a 6 dB advantage over equalized 16-QAM under atmospheric turbulence. By eliminating the need for channel state information, the proposed VPM-based DCO-OFDM provides a robust and low-latency solution for dynamic OWC links.
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All authors Tian Cao, Ping Wang, Tianfeng Wu, Kaile Wang, Jian Song

Chemical Physics · Cross-listed from quant-ph

Engineering strong coupling with molecular coatings in optical nanocavities

Athul S. Rema, Adrián E. Rubio López and Felipe Herrera

Quantum emitters near the surface of silver nanoparticles undergo Rabi oscillations in electronic population dynamics due to strong coupling with near-field multipole modes that are not radiative. Low-frequency nanoparticle dipole modes are radiative but do not couple strong enough to quantum emitters. These features limit the observation of strong coupling. Using macroscopic quantum electrodynamics theory within a Lorentzian pseudo-mode approximation for the non-Markovian interaction kernel, we demonstrate that by coating spherical silver nanoparticles with a thin molecular J-aggregate layer, the resulting core-shell plexciton resonance restructures the local electromagnetic vacuum at dipole-mode frequencies to enable Rabi oscillations for quantum emitters that otherwise would only undergo exponential population decay. Specifically, we show for quantum dot emitters in the near field of silver nanospheres of 20 nm radius, that weak-to-strong coupling crossovers can be induced using 2 nm J-aggregate shells. Our work demonstrates the potential of molecular aggregates to enable deep sub-wavelength structuring of the vacuum field for the observation of coherent quantum dynamics in optical nanocavities.
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Biological Physics

Complex versus Complicated Systems Biology, Universality versus Detailed Modelling

Kunihiko Kaneko

Biological systems are generally complicated and/or complex. In the former approach, one sets up a model with a large number of parameters to describe the system in detail. The latter approach focuses on understanding the universal aspects of biological systems. In this case, an appropriate simple model represents a universality class. The extraction of universal properties is supported by evolutionary robustness and the reduction of dimensionality in high-dimensional states. Integrating the data-driven omics approach with the universality approach is an important step in systems biology.
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Computational Physics · Cross-listed from cond-mat.stat-mech

Crossover effects on the phase transitions phenomena translated by arborecences and spectral properties

Roberto da Silva

This study investigates how visibility graphs constructed from Monte Carlo Markov Chain time series of spin models capture the critical behavior of the system. More precisely, we show that this approach identifies continuous phase transitions as well as important nuances, such as crossover effects occurring in the transition from a critical line to a first-order line through a tricritical point, as observed, for example, in the Blume–Emery–Griffiths model or, in a simpler setting, in the Blume–Capel model. By applying Kirchhoff's theorem, we show that the number of spanning trees of the resulting graphs serves as a sensitive indicator of these phase transitions. Furthermore, a qualitative analysis of the adjacency matrices based on random matrix theory provides additional evidence for these phenomena. The methodology developed here can potentially be extended to the analysis of criticality in empirical time series from complex systems, such as climate, financial, and epidemiological data, where the Hamiltonian governing the dynamics is not necessarily known.
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Tuesday 17 March

Atmospheric and Oceanic Physics

Evaluating data-driven background ensembles covariances from Graphcast: a case study for Hurricane Lee (2023)

Zhihong Chen and Xuguang Wang

Short-term background ensemble covariances (BEC) are crucial for ensemble-based data assimilation (DA). However, limited studies so far have examined the fidelity of the cost-effective data-driven model in producing the short-term BEC for hurricane data assimilation. In this study, we evaluate the background ensemble spread and correlations from GraphCast against those of GEFS for Hurricane Lee (2023) during both its intensification and non-intensification phases. Specifically, the BEC in the hurricane vortex, the hurricane environment, and the vortex-environment interactions are examined. Within the hurricane vortex, the background ensemble of GraphCast is less dispersive than GEFS. The two models agree well on the background ensemble correlations that are tied to the primary circulation but show a larger correlation difference associated with the secondary circulation, indicating the two models represent unbalanced and diabatic processes differently. In the hurricane environment, binned univariable correlations show linear relationships between the two models, with a weaker horizontal geopotential height correlation in GraphCast. GraphCast also shows a reduced spread and a flatter empirical orthogonal function spectrum of the 500 hPa geopotential height background ensemble, with more perturbation growth distributed to smaller-scale features such as shortwaves. For the vortex-environment interaction, the two models produce close background ensemble correlation patterns for Lee's track but differ more for intensity. Overall, GraphCast can produce broadly consistent short-term BEC for Hurricane Lee compared to those of GEFS. However, systematic difference exists in certain variables, scales, and processes, suggesting the need to further investigate its fidelity in a cycled hurricane DA context.
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Physics and Society

From Heard to Lived Opinions: Simulating Opinion Dynamics with Grounded LLM Agents in Economic Environments

Ryuji Hashimoto, Masahiro Kaneko, Ryosuke Takata et al. (+2 more)

Opinion dynamics (OD) studies how individual opinions evolve and generate collective patterns such as consensus and polarization. While recent work explores OD using populations of LLM-based agents focusing on opinion exchange, it typically does not incorporate individuals' lived experiences, such as economic outcomes of past decisions, which play a critical role in shaping opinions. We propose a novel OD simulation framework that grounds LLM-based agents in an economic environment, allowing them to act and receive environmental feedback. Our simulations exhibit coherent OD at both individual and population levels: individual opinions follow structured trajectories shaped by economic experiences, with adverse conditions inducing opinion rigidity, while at the population level, collective opinions co-move with economic conditions, with inequality amplifying polarization and price instability driving larger distributional shifts. These results highlight the importance of grounding LLM-based agents in environments to capture collective OD.
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All authors Ryuji Hashimoto, Masahiro Kaneko, Ryosuke Takata, Takehiro Takayanagi, Kiyoshi Izumi

Geophysics

Climate and ocean circulation changes toward a modern snowball Earth

Takashi Obase, Takanori Kodama, Takao Kawasaki et al. (+4 more)

It has been hypothesized that the Earth may have experienced snowball events in the past, during which its surface became completely covered with ice. Previous studies used general circulation models to investigate the onset and climate of such snowball events. Using the MIROC4m coupled atmosphere–ocean climate model, this study examined the changes in the oceanic circulation during the onset of a modern snowball Earth and elucidated their evolution to steady states under the snowball climate. Abruptly changing the solar constant to 94% of its present-day value caused the modern Earth climate to turn into a snowball state after ~1300 years and initiated rapid increase in sea ice thickness. During onset of the snowball, extensive sea ice formation and melting of sea ice in the mid-latitudes caused substantial freshening of surface waters and salinity stratification. By contrast, such salinity stratification was absent if the duration between the change in the solar flux and the snowball onset was short. After snowball onset, the global sea ice cover and the buildup of salinity stratification caused drastic weakening in the deep ocean circulation. However, the meridional overturning circulation resumed within several hundred years after the snowball onset because the density flux by sea ice production weakens the salinity stratification. While the evolution of the oceanic circulation would depend on the continental distribution and the evolution of continental ice sheets, our results highlight the gradual growth of sea ice and associated brine rejection are essential factors for the transient evolution of the oceanic circulation in the snowball events.
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All authors Takashi Obase, Takanori Kodama, Takao Kawasaki, Sam Sherriff-Tadano, Daisuke Takasuka, Ayako Abe-Ouchi, Masakazu Fujii

History and Philosophy of Physics

Projection and Invariance in Scientific Explanation

Harry Sticker

Any representational enterprise must omit variation in order to function. NASA still uses Newtonian mechanics, though Einstein superseded Newton, and the standard picture of scientific progress cannot explain how. A description that omitted nothing would be identical to its subject and would explain nothing. This paper argues that omission is not a defect but the central structural feature of any enterprise that builds representations from incomplete information. The key concept is projection: a principled mapping from underlying complexity to a descriptive space that partitions states into equivalence classes, omits within-class variation, and makes patterns visible that would otherwise be lost. Projection is simultaneously revelatory and constitutive: it makes genuine invariants tractably accessible while bringing into being the concepts through which they become expressible. The paper distinguishes vertical cases, in which earlier projections survive as limiting cases of more refined successors with recoverable omission, from horizontal cases, in which omission is constitutive, and invariants are accessible only at the level of the projection that defines them. The framework accounts for persistent pluralism in mature sciences, treats the renormalization group as a systematic implementation of the invariant-tracking criterion, and defends a level-relative realism on which higher-level projections reveal genuine structural features of the world. The deepest claim is an inversion of the standard picture: perspectival structure is not a concession to complexity but the condition for invariant detection. A world rich in invariants cannot be exhausted by a single projection.
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Atomic Physics

Rydberg-State Hopping in a Wavemeter-Locked Dissipative Time-Crystal System

Darmindra Arumugam

Rydberg-state hopping is demonstrated in a wavemeter-locked two-photon rubidium system (Rb D2 probe at 780 nm and 480 nm coupler), enabling rapid and repeatable switching between the 65S1/2 and 63D5/2 states without cavity or frequency-comb stabilization. A Fizeau-interferometer wavemeter provides the error signal for a digital feedback loop that simultaneously stabilizes the coupler and commands discrete Rydberg-state changes. The lock achieves sub MHz frequency stability and acquisition rates up to 6.5 GHz/s (0.4283 GHz engaged in 66 ms), extrapolating to ~0.93 s for a ~6 GHz 65S to 63D transition. Time resolved spectra reveal reemergent dissipative time-crystal oscillations after each hop, with distinct state dependent fundamentals and harmonics. This approach addresses the need for dynamically reconfigurable Rydberg state control for on resonant multi band field detection, while the DTC frequency reconfigurability enables adaptive, low frequency E field sensing in compact, cavity free architectures.
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Optics · Cross-listed from cond-mat.mes-hall

Spontaneous Polarization Suppression of Exciton-Exciton Annihilation in 3R-Stacked MoS$_2$ Bilayers

Tae Gwan Park, Xufan Li, Kyungnam Kang et al. (+4 more)

Rapid exciton-exciton annihilation (EEA) in two-dimensional semiconductors limits access to high-density excitonic regimes essential for efficient optoelectronic operation under strong excitation. Here, we show that EEA is suppressed by repulsive dipole-dipole interactions between interlayer excitons polarized by the spontaneous polarization intrinsic to rhombohedral (3R)-stacked MoS$_2$ bilayers. Using ultrafast pump-probe spectroscopy, we measure an EEA rate of $\gamma_{\rm EEA}=(5.03\pm0.99)\times10^{-3}$ cm$^2$ s$^{-1}$ in 3R bilayers, which is approximately 18.2-fold smaller than that in monolayers and 2.9-fold smaller than that in nonpolar 2H bilayers. Despite the higher exciton diffusivity recently reported for 3R relative to 2H bilayers, the reduced EEA rate in 3R indicates a rate-limited regime governed by the close-encounter annihilation probability rather than diffusion. A rate-limited annihilation model incorporating a dipole-dipole repulsive potential captures the observed ratio $\gamma_{{\rm EEA},3{\rm R}}/\gamma_{{\rm EEA},2{\rm H}}\approx0.35$ for an exciton-exciton encounter distance of $\sim$1.3 nm, consistent with the bilayer exciton Bohr radius. These results show that spontaneous polarization in 3R-stacked bilayers suppresses nonlinear excitonic losses and provides a route toward high-density excitonics.
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All authors Tae Gwan Park, Xufan Li, Kyungnam Kang, David B. Geohegan, Christopher M. Rouleau, Alexander A. Puretzky, Kai Xiao

Computational Physics · Cross-listed from cs.CE

Integration of local and global surrogates for failure probability estimation

Audrey Gaymann, Juan M. Cardenas, Sung Min Jo et al. (+2 more)

This paper presents the development of an algorithm, termed the Global-Local Hybrid Surrogate (GLHS), designed to efficiently compute the probability of rare failure events in complex systems. The primary goal is to enhance the accuracy of reliability analysis while minimizing computational cost, particularly for high-dimensional problems where traditional methods, such as Monte Carlo simulations, become prohibitively expensive. The proposed GLHS builds upon the foundational work of Li et al., by integrating an adaptive strategy based on the General Domain Adaptive Strategy (Adcock et al.). The algorithm aims to approximate the failure domain of a given system, defined as the region in the input domain where the system transitions from safe to failure modes, described by a limit state surface. This failure domain is not explicitly known and must be learned iteratively during the analysis. The method employs a buffer zone, defined as the region surrounding the limit state surface. Within this buffer zone, Christoffel Adaptive Sampling is utilized to select new samples for constructing localized surrogate models, which are designed to refine the approximation in regions critical to failure probability estimation. The iterative process proceeds until convergence is reached. This results in a hybrid methodology that integrates a global surrogate to capture the overall trend with local surrogates that concentrate on critical regions near the limit state function. By adopting this strategy, the GLHS method balances computational efficiency with accuracy in estimating the failure probability.
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All authors Audrey Gaymann, Juan M. Cardenas, Sung Min Jo, Marco Panesi, Alireza Doostan

Optics

Dynamical Drexhage Effect: Amplified Emission in Time-Modulated Electromagnetic Environments

Juan Carlos Obeso Jureidini, Michael Reitz, Piper Fowler-Wright et al. (+4 more)

We investigate the effect of nonrelativistic motion on the emission dynamics of a dipole emitter moving next to a reflecting interface. Within the formalism of macroscopic QED, we obtain a general equation of motion for the dipole amplitude in terms of the dyadic Green's function, yielding a dynamical extension of the Drexhage effect. At short dipole-surface distances, the dipole can be described as a parametric oscillator featuring time-dependent dampings and Lamb shifts, both arising from the self-induced modulation of the surrounding electromagnetic environment. Importantly, these time-dependent parameters do not always average out, leading to amplification of the dipole amplitude and the radiated intensity when considering certain sinusoidal trajectories with specific modulation amplitudes and frequencies. We derive threshold modulation amplitudes as function of the relative permittivities at the interface. Qualitatively, in the vicinity of certain epsilon-near-zero materials, amplification is possible purely by modulation of the damping. Our findings open up avenues for the dynamic control of light-matter interaction in nanophotonic environments.
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All authors Juan Carlos Obeso Jureidini, Michael Reitz, Piper Fowler-Wright, Arghadip Koner, Hadiseh Nasari, Andrea Alù, Joel Yuen-Zhou

Chemical Physics

Ultrafast laser-driven quantum dynamics in positronium chloride

Einar Aurbakken, Håkon Emil Kristiansen, Simen Kvaal et al. (+2 more)

We present a computational study of the laser-driven quantum dynamics of positronium (Ps), PsH, and PsCl at the time-dependent Hartree-Fock level of theory. To eliminate finite-basis effects and to properly capture continuum dynamics, we use a spherical polar pseudospectral representation. The multicomponent theory and its implementation are described in detail. We find that while the presence of the positron delays electron ionization in PsH, a slight enhancement of electron ionization is observed in PsCl. In both cases, the positronic response is faster than that of the electrons. We propose that the formation of PsCl may be directly observed through photopositron spectra in the multiphoton regime, where PsCl peaks are expected at roughly twice the energy of Ps peaks, making PsCl clearly distuinguishable from Ps. In the tunelling regime, however, photopositron rescattering peaks may only be distuinguishable if the amount of Ps is sufficiently low.
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All authors Einar Aurbakken, Håkon Emil Kristiansen, Simen Kvaal, Antoine Camper, Thomas Bondo Pedersen

Atmospheric and Oceanic Physics

A Tug-of-War Between Baroclinic Eddies and Convection: Implications for Icy Moon Oceans

Shuang Wang, Wanying Kang and Cheng Li

In many geophysical and planetary environments, such as Earth's ocean and atmosphere as well as subsurface oceans of icy satellites, convection driven by bottom geothermal heating usually coexists with baroclinic eddies driven by lateral buoyancy/temperature gradients. These processes compete against each other, with convection destabilizing the stratification and baroclinic eddies re-stabilizing it, thereby controlling whether the bottom heat flux is significantly redistributed as it is transmitted to the upper surface. Using scaling analysis and numerical simulations, we show that a stratified layer persists near the upper surface up to ${\rm Ra}_{v}\sim {\rm Ra}_h^{5/2}$, where ${\rm Ra}_h\equiv \Delta b_0/(L_zf^2)$ measures the imposed upper-surface buoyancy contrast $\Delta b_0$ and ${\rm Ra}_v\equiv B_0/(L_z^2f^3)$ measures the strength of the bottom buoyancy flux $B_0$, $L_z$ is the domain depth and $f$ is the Coriolis parameter. For ${\rm Ra}_v<{\rm Ra}_h^{5/2}$, baroclinic eddies dominate over convection, maintain the upper stratified layer, and completely deflect the bottom buoyancy/heat input into meridional transport. In contrast, when ${\rm Ra}_v>{\rm Ra}_h^{5/2}$, convective plumes penetrate the stratification and transport buoyancy/heat vertically with negligible deflection. Building on these results, we further propose a scaling law for the meridional buoyancy/heat transport in this system. Applications to icy satellites are discussed.
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Biological Physics · Cross-listed from nlin.AO

Molecular-scale, nonlinear actomyosin binding dynamics drive population-scale adaptation and evolutionary convergence

Jake McGrath, Colin Johnson and José Alvarado

Biological actuators – from myosin motors to muscles – follow Hill's model where a dimensionless parameter $\alpha$ captures the nonlinear coupling between contraction rate and force generation. Our prior work identified a characteristic $\alpha^* = 3.85 \pm 2.32$ across natural muscles and showed that $\alpha^*$ optimizes a power-efficiency tradeoff, potentially explaining its prevalence in nature. However, those results reflected short-term actuation tasks whereas phenotypic distributions in $\alpha$ emerge over evolutionary timescales. Here, we use numerical simulations of self-propelled agents to explore how nonlinear actomyosin actuation (parameterized by $\alpha$) shapes population dynamics. Agents of different $\alpha$ compete for resources and reproduce with slight mutations. Without mutations, resource availability drives populations in $\alpha$ toward distinct behaviors: under abundance or scarcity, specialized $\alpha$ survive. However, with mutations and selection, populations evolve toward distributions centered around the characteristic $\alpha^*$ observed in nature. Further, we show that the mutation rate $\delta$ governs a balance between adaptability and robustness: large $\delta$ generates instability and extinction, small $\delta$ prevents feedback, while intermediate $\delta$ enables long-term adaptability while remaining robust to short-term noise. Our results suggest that nonlinear actuation provides a general understanding of energy management in actomyosin systems across a wide range of timescales, ranging from the task-specific to evolutionary. These insights may guide the rational design of active materials with adaptive properties.
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