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Physics Physics

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Thursday 30 July

Computational Physics · Cross-listed from nlin.PS

Nonlinear Fourier spectral signatures of rogue waves observed in Bose-Einstein condensates

Zhihao Zhang, Yankai Huang, Tiantian Li et al. (+2 more)

Modulation instability provides an important framework for understanding rogue wave (RW) formation on continuous backgrounds. However, the formation mechanism and nonlinear spectral structures of RWs in Bose-Einstein condensate (BEC) matter-wave systems with vanishing boundary conditions remain largely unexplored. Here, we employ the nonlinear Fourier transform (NFT), based on the integrable structure of the focusing nonlinear Schrödinger equation and the Zakharov-Shabat scattering problem, to investigate two representative classes of first-order RWs in BEC systems. Through nonlinear spectral analysis and Darboux reconstruction, we demonstrate that both Gaussian-wave-packet-induced extreme localization events and experimentally observed Peregrine solitons are governed by the coherent dynamics of discrete soliton modes encoded in the nonlinear spectrum. For Gaussian initial states, increasing the initial width leads to an increasing number of discrete eigenvalues, resulting in a transition from fundamental solitons and bound states to Christmas-tree-like RW structures. For experimentally observed Peregrine solitons, localized perturbations reshape the discrete spectral configuration and phase evolution, enabling coherent focusing of multiple bound soliton modes. Furthermore, we reveal the spectral mechanism of higher-order RWs and propose an inverse spectral-engineering approach based on discrete-spectrum phase matching. Our results provide a nonlinear spectral perspective for understanding and controlling RW formation in matter-wave systems with vanishing boundary conditions.
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All authors Zhihao Zhang, Yankai Huang, Tiantian Li, Denglong Wang, Jie Peng

Optics

Multi-mode fiber enabled multi-wavelength optical trapping and dynamic manipulation

Yikun Shen, Chuangye Zhang, Yuquan Zhang et al. (+7 more)

Optical fiber tweezers offer distinct advantages for long-distance manipulation, compact integration, and minimally invasive operation in biological environments. However, most optical fiber tweezers rely on single-mode fibers (SMFs), which are constrained by limited optical mode diversity and reduced control flexibility. Although multi-mode fibers (MMFs) support a wider spectrum of propagation modes, their inherent mixed guided modes with low coherence become a long-standing limitation for the design of focused trapping configurations. To address these limitations, we propose and experimentally validate a fully MMF-based optical tweezer system integrated with a micro-lens structure fabricated on the fiber facet, enabling stable optical trapping across multiple wavelengths and dynamic manipulation of trapped cells. Employing 532 nm continuous-wave and 800 nm femtosecond lasers, we demonstrate that both light sources can generate tightly focused optical spots through the micro-lens with a high numerical aperture (NA>0.7), achieving robust trapping and axial dynamic manipulation of cells. Compared with conventional SMF-based tweezers, this approach leverages the broadband and multi-mode properties of MMFs, allows for wavelength-flexible and dynamically adjustable trapping of cells, and paves the way for lab-on-fiber biophotonic platforms with potential applications such as interventional manipulation, cell sorting, and cellular fluorescence analysis.
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All authors Yikun Shen, Chuangye Zhang, Yuquan Zhang, Jiahui Pan, Siwei Chen, Hongyang Xu, Yixuan Chen, Qi Jin, Xiaocong Yuan, Changjun Min

Applied Physics · Cross-listed from cond-mat.mtrl-sci

Unconventional and Fragile Magnetic Exciton in a van der Waals Quantum Magnet

Kai-Xuan Zhang, Min Zhang, Minjae Kim et al. (+25 more)

The recently discovered magnetic exciton in the van der Waals (vdW) antiferromagnet NiPS3 exemplifies these phenomena, exhibiting several distinctive characteristics. Despite extensive investigation, much of its physics remains unresolved, with key questions about why the NiPS3 magnetic exciton is so sharp and optically bright despite the nominally spin-forbidden transition, posing significant challenges to a proper understanding and practical manipulation of the exciton. An urgent question is to what extent it is due to chemical disorder, magnetic weakening, lattice modification, or intrinsic instability of the bright exciton itself: answers to which will put stringent constraints on possible theoretical models. Here we address these questions using hydrostatic pressure as a clean, continuous, reversible, and in-situ tuning parameter. We find that the sharp photoluminescence peak is drastically suppressed by as little as 0.4 GPa and completely quenched by 1.5 GPa, with demonstrating its reversibility. Crucially, this bright-to-dark conversion occurs without magnetic, crystallographic, or electronic reconstruction despite an increase in the Neel temperature, as established by Raman, X-ray absorption, nuclear magnetic resonance spectroscopy, and first-principles many-body calculations. Our results demonstrate that the optical brightness of the magnetic exciton is independent of chemical disorder, lattice expansion, and weakening of magnetic order, indicating that a higher-order correlated mechanism governs the bright exciton. We further propose experimentally constrained microscopic scenarios involving exciton pairing, crystal-field-controlled spin-orbit mixing, and symmetry breaking, providing a framework for future tests of entangled magnetic exciton in correlated quantum magnets.
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All authors Kai-Xuan Zhang, Min Zhang, Minjae Kim, Yong-Hyun Kim, Junghyun Kim, Heejun Yang, Pyeongjae Park, Chaebin Kim, Mangesh Diware, Junik Hwang, Youjin Lee, Byeong-Gwan Cho, Hyeong-Do Kim, Tae-Yeong Koo, Chunhua Chen, Mingtao Li, Xujie Lü, Wenge Yang, Kee-Hoon Kim, Seung-Ho Baek, Hyeonsik Cheong, Sung-Keun Lee, Beom Hyun Kim, Christopher Lane, Jian-Xin Zhu, Zhaorong Yang, Young-Woo Son, Je-Geun Park

Chemical Physics · Cross-listed from quant-ph

Quadruply Bonded Mo2 Molecules: An Innate Emitter-Resonator Quantum System in Free Space

Miao Meng, Ying Ning Tan, Zi Cong He et al. (+5 more)

In recent decades, significant progress has been made in constructing and studying individual quantum systems based on two level atoms (molecules) and photons. Here we demonstrate that the quadruply bonded Mo2 unit, with a MoMo bond distance as short as 2.1A, functions as an innate emitter resonator molecular quantum system capable of trapping visible light photons between the two molybdenum atoms under ambient conditions, thereby generating an intense quantized local electromagnetic field with an extremely small mode volume. The resonance fluorescence spectra of three Mo2 complexes indicate that the intermetallic Mo-Mo charge transfer transition is coherently coupled to the local scattering field, exhibiting vacuum Rabi splitting and Mollow triplets. Resonant coupling of single molecules and N-molecule ensembles to the scattered light through sideband excitation produces a sequence of discrete optical modes spanning a broad wavelength range, with polaritonic transitions identical to those observed in Ni2 based systems. These results establish the Mo2 molecule as an independent emitter resonator integrated quantum system that enables quantum optical experiments in free space using conventional spectroscopic instrumentation. This work extends quantum electrodynamics into molecular science, providing new insights into metal metal bonding, molecular physics, and light matter interactions.
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All authors Miao Meng, Ying Ning Tan, Zi Cong He, Zi Hao Zhong, Jia Zhou, Yu Li Zhou, Guang Yuan Zhu, Chun Y. Liu

Physics and Society

Targeted Quarantine Strategies Mitigate SIS-model Cyber Epidemics on Complex Networks

Ho Yuen Wong and Tak Shing Tai

The rapid expansion of highly connected and heterogeneous digital infrastructures has fundamentally altered the dynamics of cyber epidemics, making it increasingly important to understand how malware propagates under realistic structural and policy conditions. In this study, we employ an SIS epidemic model on complex networks to investigate virus spreading under a range of cybersecurity scenarios, including minimally protected environments, anti-virus, vulnerable legacy systems, mixed security systems, rapidly evolving virus, and targeted quarantine of key nodes. Using simulations on scale-free topologies, we characterize how infection prevalence evolves over time and identify conditions under which outbreaks become explosive versus controlled. Our results show that vulnerable and rapidly evolving virus scenarios lead to fast and almost complete compromise, while antivirus and mixed-security configurations significantly reduce peak infection levels. Most importantly, we demonstrate that targeted quarantine of a small fraction of structurally important nodes can both delay the onset of large scale outbreaks and lower steady state infection, revealing a threshold in the fraction of quarantined nodes which required to alter spreading of computer virus. These findings highlight the critical role of network topology and selective protection in enhancing the resilience of cyber-physical infrastructures.
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Fluid Dynamics

Mesh Adaptation on Hybrid Unstructured Meshes for Immersed Boundary Methods

Jonatan Núñez-de la Rosa, Esteban Ferrer and Eusebio Valero

In this work, we describe a new preprocessing tool for mesh adaptation on hybrid unstructured meshes with a target application on immersed boundary methods. The tool has as input an unstructured, hybrid, and conforming mesh generated by an external mesh generation software, and the main goal is to refine this mesh around immersed geometries in such a way that the CFD solver using the immersed boundary method can simulate flow problems in an accurate and efficient manner. The input background mesh can be made of different types of elements, like tetrahedra, hexahedra, prisms, and pyramids, which, unlike Cartesian meshes, permit for a more flexible mesh. Hybrid unstructured meshes enable one to use the immersed boundary technology in a new class of flow problems where the full geometry is decomposed into a fixed geometry part and a changing geometry part. A body-fitted mesh is generated for the fixed geometry while for the changing one is used the immersed boundary method. We simulate several flow problems to test the new meshes, including subsonic flow past a cylinder and subsonic flow past an NACA0012 airfoil, both using finite volume and discontinuous Galerkin methods and solving the Navier–Stokes equations. As an industrial example of our mesh generation, we consider the simulation of a multi-element airfoil: in this case, a mesh generation software generates an unstructured conforming background mesh for the slat and main airfoil, while the flap is placed as immersed geometry in this body-fitted mesh. As accurate and efficient results are sought, this mesh is refined around the flap and then the subsonic flow at high-lift flow conditions is simulated with a finite volume method coupled with an immersed boundary method and using the Reynolds–averaged Navier–Stokes equations. The reported numerical simulations are in good agreement with experimental data.
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Biological Physics · Cross-listed from quant-ph

Exploring the use of quantum computing for facilitating spatially and temporally resolved models of a biological cell

Muralikrishnan Gopalakrishnan Meena, Dileep Kishore, Jerry M. Parks et al. (+5 more)

Whole-cell simulation, modeling all of a cell's functional systems over its life cycle, is an outstanding challenge in computational biology. Even the simplest living cell contains thousands of interacting proteins and metabolites (on the order of trillions of atoms) whose full functional dynamics spans roughly five orders of magnitude in space (nm to $\mu$m) and nearly nineteen in time (fs to hours). Further, many of the governing physical and chemical properties remain incompletely characterized. Simulating such complex systems at fully atomistic resolution over a full cell cycle is computationally intractable on classical architectures, raising a central question: Can quantum computing offer a viable path to whole-cell simulations that integrate molecular- and systems-level complexity? This Perspective examines the potential of quantum computing across three hierarchical scales: atomistic-molecular modeling, metabolic and regulatory networks, and whole-cell spatial modeling. We present a complexity analysis comparing classical and quantum algorithms for representative biological problems, identifying regimes of substantial theoretical speedup under specified algorithmic assumptions. We highlight algorithmic developments designed to leverage both near-term exploratory and fault-tolerant quantum architectures, and discuss practical bottlenecks: data encoding overhead, system conditioning, measurement constraints, and hybrid quantum-HPC integration. Together, these results outline a roadmap for quantum-accelerated whole-cell modeling and the biological insights such multiscale frameworks may eventually enable.
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All authors Muralikrishnan Gopalakrishnan Meena, Dileep Kishore, Jerry M. Parks, Luke Bertels, Dilipkumar N. Asthagiri, Travis Humble, Thomas L. Beck, Mitchel J. Doktycz

Fluid Dynamics

Modified Dynamic Mixed Subgrid-scale Models for Geophysical Flows: Forced Two-Dimensional and $\beta$-plane Turbulence

Anantha Narayanan Suresh Babu, Akhil Sadam and Pierre F.J. Lermusiaux

Subgrid-scale (SGS) models for large-eddy simulations (LES) of geophysical turbulence typically need to balance dissipative regularization with backscatter, the upscale transfer of energy from unresolved to resolved scales. Dynamic mixed models (DMMs) combine functional eddy viscosity and structural closures through dynamically estimated coefficients that are least-squares optimal with respect to the Germano identity error (GIE). We show that this classical DMM least-squares estimation can be dominated by the structural component, thereby limiting the functional component's dissipative regularizing role. To address this limitation, we develop a modified Gram-based framework to construct a novel parametric family of fully-coupled, sequential, and fully-decoupled DMMs with tunable structural-functional balance. We evaluate the resulting closures using an idealized forced two-dimensional and $\beta$-plane turbulence framework with the Leith model and the fourth-order nonlinear gradient model. A priori results show that structurally-dominated models achieve strong agreement with the ideal SGS forcing and accurately reproduce local SGS energy exchange, including backscatter. However, in a posteriori tests, structurally dominated models exhibit noise-like artifacts with high-wavenumber spectral deviations, indicating insufficient net dissipation. In contrast, the sequential DMM in which the functional component is determined first and then corrected by the structural component retains much of the a priori structural accuracy while improving the a posteriori vorticity fields, spectra, and domain-averaged diagnostics. Spectral SGS energy and enstrophy-transfer analyses show that this sequential DMM permits backscatter at scales larger than the forcing scale with enhanced dissipation at smaller scales, thereby improving the balance between instantaneous structural fidelity and long-term accuracy.
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Chemical Physics

Storing Sensor Events in the Interconnection Strength of Conducting Polymer Dendrites

Antoine Baron, Bilel Hafsi, Enrique H. Balaguera et al. (+1 more)

If electronics drives only electrons to charge electrodes, natural systems learn by moving matter to evolve. Morphogenesis in sessile organisms can be seen both as a fabrication process and as an operative mechanism. However, intricating manufacturing and programming functionalities in electronic hardware is not conventional. In this study, we experimentally implement such a concept of an evolutionary electrical system using a neuro-inspired electronic nose as a model, to store a history of sensory data in the physical properties of the electrical interconnects of sensing elements. Triggered only by volatile molecule exposures, different sensing elements change instantaneously and reversibly their impedance, so pulse voltages enable the electrochemical growth of conducting polymer dendrites. The strength of the evolving interconnects is specific to the sensing materials and to the nature of volatile molecules to which they are exposed. The dendritic growths occur exclusively when exposed to volatile samples, and stop immediately after interrupting the exposure. The capability of such "passive memory" was also assessed by simulating a network architecture, which showed that this way of storing information should greatly diminish the fabrication complexity of a highly dense sensing array while realistically enabling its calibration to classify user-specific environment exposures. By demonstrating that memory in electronics can be a concept linked to manufacturing like in living organisms, this study shows that low material resources and low energy activation can be exploited for practical electronic applications in future-emerging sensing technologies.
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All authors Antoine Baron, Bilel Hafsi, Enrique H. Balaguera, Sébastien Pecqueur

Plasma Physics

Coaxial Gyromagnetic Nonlinear Transmission Line with a Slow-Wave Structure: 2D/3D FDTD Modeling

Serhii Karelin and Ivan Onishchenko

A coaxial gyromagnetic nonlinear transmission line (GNLTL) enables pulse-front compression and oscillation packet generation in the 0.3-10 GHz range. Numerical modeling of such systems typically employs a 2D axially symmetric FDTD scheme coupled with the parallel solution of full non-linearized Landau-Lifshitz-Gilbert equations. Previous studies have shown that using an insulating dielectric with a permittivity $\epsilon$>60 enables the generation of long-lasting oscillations via a Cherenkov-like effect. However, practical implementation is limited due to the severe high-frequency losses inherent in materials with such extreme permittivity, like water. To overcome this limitation and enhance performance, we propose a novel design utilizing a spiral central conductor to mimic a high-permittivity environment, with the ferrite placed inside the spiral. Due to the loss of axial symmetry, the FDTD model was extended to 3D coordinates. Simulations confirm that the spiral conductor configuration generates a prolonged oscillation packet similar to that of a high-permittivity dielectric waveguide, but with significantly reduced losses and improved energy transfer efficiency.
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2607.27545 · 30 Jul · Submitted to IEEE Transactions on Plasma Science (Manuscript ID: TPS18901). Materials of this paper were presented as a poster report "2D/3D FDTD Modeling of Coaxial Waveguide with Magnetized Nonlinear Ferrite and Slow-Wave Structure" at the XXXII International Workshop on Optical Wave & Waveguide Theory and Numerical Modelling, 8-10 April 2026, Lausanne, Switzerland (https://owtnm26.epfl.ch/)

Abstract PDF $SYMM ×1

Fluid Dynamics

Gaussian non relativistic spontaneously stochastic hydrodynamics

David Montenegro and Giorgio Torrieri

We study the non-relativistic limit of Gaussian covariant hydrodynamics [1]. We argue that the condition of incompressibility provides additional symmetries matching relativistic hydrodynamics but incompressibility must break down at a “microscopic“ scale. We then develop the renormalization group equations for average and fluctuations w.r.t. that scale, to understand its effect on flows at intermolecular distances where hydrodynamics gives way to statistical mechanics. The resulting dynamics naturally incorporates spontaneous stochasticity as a macroscopic back reaction of statistical mechanics fluctuations, as well as features reminiscent of anomalous dissipation and “wild solutions“ as renormalization group counterterms. We frame these considerations into both a phenomenological discussion of the limits of applicability of fluid dynamics, and a discussion of where physics might shed some light on the mathematical issues associated with turbulence.
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Wednesday 29 July

Computational Physics

Iterative Neural Variational Solver of Committor Function in Under-damped Langevin Dynamics

Tiejun Li, Zhaoqi Liu and Xiang Zhou

The committor function describes the probability that a stochastic dynamical system, while transitioning between two metastable states, will first reach one state rather than the other. It is a central object in transition path theory, providing the accurate reaction rate and the characterization of the transition mechanism through reactive probability flux and the transition state ensemble. While the numerical methods for this function are well studied for over-damped Langevin dynamics where the friction damping coefficient goes to infinity, its computation in the general under-damped case remains an open challenge due to the lack of a variational structure in the underlying backward Kolmogorov equation. This restricts the quantitative investigation of the impact of the finite damping coefficient on rare transitions in real systems. In this work, by decomposing the infinitesimal generator of the underdamped Langevin dynamics into a self-adjoint and a skew-adjoint part, we develop an iterative scheme in which each subproblem has a variational form and can be solved efficiently using neural networks. Fundamentally, this approach can be viewed as a generalized Concus-Golub-Widlund (CGW) splitting method and each subproblem has a variational form, which can be solved efficiently. This iterative scheme also has the flexibility of introducing a preconditioning operator associated with a new overdamped Langevin dynamics, which naturally leads to the design of enhanced adaptive sampling for the subproblems. We validate the efficiency and accuracy of our method on benchmark systems including a one-dimensional double-well potential, the Muller-Brown potential, and alanine dipeptide. The proposed iterative variational framework provides a practical and extensible numerical tool for probing rare transition events in Langevin dynamics across varying damping coefficients.
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Medical Physics

Dual-Mode SERS and Colorimetric Sensor for Lung Cancer VOC-Biomarker Detection Using Hydrogel Patches

Jialin Li, Shaowei Liu, Amil Aligayev et al. (+7 more)

Hexanal, a volatile organic compound (VOC), is a potential biomarker for the early detection of lung cancer. In this study, we developed a dual-mode flexible biosensor that integrates surface-enhanced Raman scattering (SERS) and colorimetric detection for the quantitative analysis of hexanal in human exhaled breath. The biosensor employs Ag nanocubes wrapped with Co-Ni layered double hydroxide (AgNCs@Co-Ni LDH) as a functional matrix, offering both superior SERS enhancement and efficient VOC adsorption properties. To achieve selective detection, AgNCs@Co-Ni LDH were incorporated into agarose hydrogels along with 3-methyl-2-benzothiazolinone hydrazone (MBTH). The resulting MBTH-AgNCs@Co-Ni LDH/hydrogel-patch facilitate the oxidation of hexanal, enabling simultaneous colorimetric and SERS signal generation, while producing acrizine, a blue-colored reaction product. This hydrogel-based dual-mode sensing platform exhibits high selectivity, excellent stability, and precision in SERS-based hexanal detection. The detection limit for the SERS method was determined to be as low as 3.34x10-13 M. Furthermore, developed and optimized compact CNN-based multi-terminal intelligent recognition system for enhanced hydrogel-patch detection through AI-driven, portable, and real-time colorimetric analysis. Therefore, this work not only enables the effective detection of hexanal in the exhaled breath of suspected lung cancer patients, underscoring its potential for early lung cancer screening, but also establishes a foundation for the development of multimodal hydrogel biosensors for broader applications in disease diagnosis.
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All authors Jialin Li, Shaowei Liu, Amil Aligayev, Hao Jiang, Muhammad, Xin Yu, Jin Tao, Jiaqi Wang, Agnieszka Jastrzębska, Qing Huang

Fluid Dynamics

Three-dimensional shock-weak-discontinuity interactions: curvature deposition and post-shock flow response

Alexander Omelchenko

A weak acoustic, entropy or vortical sheet can cross a finite-strength three-dimensional shock without producing a kink, yet it can deposit a discontinuity of curvature along the intersection curve. We derive a local Euler junction law that separates the active normal-plane scattering problem from its three-dimensional geometric realization. The scattering selects one scalar curvature amplitude, while the orientation and pre-existing shape of the shock determine the full curvature tensor and the downstream flow response. A controlled pair of saddle-shock interactions has identical incident and outgoing modal amplitudes but produces post-shock pressure-gradient vectors separated by 31.6 degrees and vorticity vectors separated by 25.6 degrees. For a perfect gas, acoustic incidence also possesses curvature-neutral branches: outgoing acoustic, entropy and shear waves remain finite while the curvature channel cancels. Convected entropy and in-plane vortical sheets deposit curvature with opposite signs, and their coefficients obey an exact relation imposed by total-enthalpy conservation and acoustic orthogonality; line-tangent vorticity is curvature-transparent. An expanding spherical shock provides an unsteady example with non-zero shock speed and non-zero intersection-line tracking speed, separating the smooth blast-wave acceleration from the deposited acceleration jump. Near an umbilic, the same junction selects or rotates the principal-curvature frame. The resulting law supplies the interface data needed to connect smooth curved-shock reconstructions across a continuously differentiable, piecewise twice-differentiable front.
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Atomic Physics · Cross-listed from cond-mat.quant-gas

Hubbard models with ultracold shielded molecules in optical lattices

Kevin Pérez, Joseph W. Desroches, Kaden R. A. Hazzard et al. (+1 more)

We show that, in appropriate regimes, ultracold collisionally shielded molecules in an optical lattice realize a controlled extended Hubbard model, despite their large-radius hard-core repulsion. We compute the Hubbard parameters and show lattice depth and microwave-induced dipolar interactions provide flexible, independent control of on- and off-site interactions. This makes shielded molecules a powerful platform for extended Hubbard models and enables an interaction microscope that can distinguish inter-hole interactions in doped Mott insulators - crucial to superconductivity and strange metallicity - that are indistinguishable with quantum gas microscopy alone.
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All authors Kevin Pérez, Joseph W. Desroches, Kaden R. A. Hazzard, Tijs Karman

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Land Art as a Big-Data Climate Sensor

Alev Cinbarci and Sean Kalaycioglu

Robert Smithson's 1970 land artwork Spiral Jetty, located in the north arm of Utah's Great Salt Lake, has alternated between submergence and exposure during severe lake decline. We analyze 1,744 co-registered Landsat 4-9 and Sentinel-2 image chips spanning every year and calendar month from 1984 to 2025. A 14-feature complexity signature combines Shannon entropy, multiscale permutation entropy, fractal dimension, lacunarity, gray-level co-occurrence texture, intensity statistics, and ImageNet-pretrained ResNet50 features. These measurements are compared with a 42-year monthly climate and hydrology panel from NASA GISTEMP, USGS NWIS, Open-Meteo, and the Global Carbon Budget. Bootstrap analysis shows that Shannon entropy is a weak proxy and does not support an earlier small-sample claim of positive correlation with global temperature. By contrast, coarse-scale permutation entropy and mean intensity track lake elevation strongly, with Spearman correlations of 0.85 to 0.88 and 95 percent confidence intervals excluding zero. The third principal component of the ResNet50 embeddings emerges without supervision as an AI climate axis, correlating 0.86 with cumulative CO2 and -0.83 with lake elevation. Image complexity leads lake stage by about three years, with Pearson r = 0.58 at lag +3 and a 95 percent confidence interval of 0.40 to 0.73. STL decomposition reveals a non-monotonic trend that rises from 1984 to 2015 and declines sharply thereafter as the lake approaches record-low elevations. Partial correlations controlling for month and sensor confirm robustness to seasonal and sensor effects. These results refine the art-as-thermometer metaphor into an art-as-leading-indicator-of-hydrological-state interpretation. The dataset, feature pipeline, and analysis code are released as a public benchmark.
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General Physics

Strong Gravitational Lensing by Lorentzian-Euclidean Black Hole

Rukkiyya V. P, Shubham Kala and Sini R

We investigate the strong gravitational lensing properties of the Lorentzian Euclidean black hole, a spacetime in which the horizon at $r=2M$ is not a coordinate singularity but a genuine surface of signature change, with the associated curvature singularities removed by two regularization parameters, $\rho$ and $k$. Starting from the null geodesic equations, we derive the photon sphere, the critical impact parameter, and the strong deflection limit coefficients, and use them to obtain the deflection angle and the full set of strong lensing observables, namely the angular position of the relativistic images, their angular separation, the relative magnification, and the differential time delay between successive images. We show that the photon sphere and critical impact parameter increase with $\rho$ and decrease with $k$, indicating that the two parameters have opposite effects on the optical geometry, and evaluate the resulting observables numerically for the supermassive black holes Sgr A* and M87*. Comparison with the Event Horizon Telescope shadow measurements shows that the Schwarzschild limit is mildly disfavored for Sgr A*, whereas M87* places $k$-dependent upper bounds on $\rho$, with the adopted fiducial values lying well below these limits. We further show that the shadow constrains only a combination of $\rho$ and $k$, and identify observables such as shadow circularity, higher order image time delays, and quasinormal mode spectra that are capable of breaking this degeneracy. These results identify gravitational lensing observables as effective probes of the Lorentzian–Euclidean scenario.
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Physics and Society · Cross-listed from cs.MA

PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems

Joyjit Roy, Samaresh Kumar Singh and Sushanta Das

Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, environmental conditions, and vulnerable road users (VRUs) such as pedestrians and cyclists. Most advanced driver assistance systems (ADAS) employ reactive mechanisms that activate only after hazards have emerged, a critical limitation underscored by rising VRU fatalities in the United States. This study introduces PRISM (Proactive Risk Intelligence and Safety Management), an agentic multi-model safety architecture that transitions from reactive crash avoidance to proactive, continuous risk management. PRISM employs inverse crash-probability modeling to convert binary crash classifiers into dynamic, interpretable safety scores. Three specialized models addressing trajectory kinematics, environmental risk, and VRU interaction operate concurrently, coordinated by a reasoning layer incorporating reinforcement learning, contextual memory, and feature-level attribution. The system provides graduated safety interventions across four tiers, from silent monitoring to emergency alerts. Unlike rule-based systems with static thresholds, PRISM dynamically adjusts safety parameters in real time. Validated across 1,296 scenarios from three naturalistic driving datasets without dataset-specific retraining, the system yielded a mean safety score of 68 out of 100, classified 77.6% of scenarios as advisory, and flagged a near-miss rate of 3.8%, with 11% of scenarios escalating to intervention or emergency response. Feature attribution consistently identified trajectory risk and VRU proximity as primary safety factors. PRISM provides a unified, interpretable framework for proactive transportation safety with emphasis on VRU risk reduction in dense urban environments.
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Physics and Society

Bridging Dependence: From Opinion Leader Influence to Standard Correlation Measures

Jonas Karge

As a cornerstone result of social choice and democracy the- ory, the Condorcet Jury Theorem (CJT) provides probabilistic guaran- tees for the correctness of majority decisions under idealized conditions, in particular assuming that voters are statistically independent. When generalizing the original CJT, a standard approach in the literature to model dependence introduces an external opinion leader (OL) whose in- fluence affects individual voting behavior. In this paper, we take the first steps toward a unified framework for modeling dependence by translat- ing the OL model into standard correlation measures, namely covariance and correlation coefficient. These translations build conceptual bridges between different dependence models in the voting literature and facili- tate the application of existing theoretical results across frameworks.
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Physics and Society

Toward a social psychology of AI: language-model agents reproduce human-like minimal-group bias

Messi H.J. Lee

Language-model agents now interact in groups, but evaluations that probe memorised stereotype content or use models to simulate people leave this social behaviour unmeasured. We adapt the minimal-group paradigm—social psychology's classic test of intergroup bias—into a controlled probe: an agent distributes points among anonymous peers bearing only an arbitrary group label. Across four reasoning models, mere categorisation into meaningless groups elicited in-group favouritism that vanished under a group-blind control and was concentrated in the numerical minority: minority deciders over-allocated to their own group relative to their numbers, majority deciders allocated close to proportionally, and the asymmetry closed at equal group sizes. Disabling reasoning in one model did not remove the disposition—if anything it grew—but nearly erased the minority-majority asymmetry, implicating deliberation in where bias concentrates rather than whether it appears. These open-weight reasoning models reproduce the behavioural signature of human intergroup discrimination, independent of stereotype content, and social psychology's theories and methods offer a paradigm for measuring and governing AI's social behaviour.
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General Physics

Curvature of Physical Space and Geometric Projection: A Possible Geometric Understanding of Quantum Mechanics and Relativity

Xiang Qi

Starting from a formal comparison between the Schrödinger equation and the Jacobi field equation, this paper proposes the hypothesis that, within the framework of non-relativistic quantum mechanics, the sectional curvature of microscopic physical space can be expressed as \(K(x) = 2m(E-V(x))/\hbar^2\), and attempts to offer a possible unified geometric understanding of quantum mechanics and relativity. The paper is divided into three parts. The first part identifies the Schrödinger equation, in its mathematical form, as a Jacobi field equation. The wave function is made to correspond to a Jacobi field, giving it the geometric meaning of the deviation of geodesics in physical space. Phenomena such as quantum tunneling, energy quantization, and the path integral are then interpreted, from this perspective, as geometric manifestations of different spatial curvatures. The second part proposes a geometric projection scheme based on semi-geodesic coordinates. The transverse metric factor of a curved space, which satisfies a Jacobi field equation, is taken as a unified geometric framework. It is demonstrated that the metric correction terms in classical mechanics, special relativity, general relativity, and quantum mechanics can all be derived from the same equation, with the curvature \(K\) arising from different physical sources. The third part extends this framework to statistical mechanics.
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Instrumentation and Detectors

Serenity-S1, a CMS Tracker Readout and Data Processing Card for HL-LHC

G. Fedi, L. E. Ardila-Perez, M. Balzer et al. (+13 more)

The CERN Large Hadron Collider will be upgraded to the High-Luminosity LHC, increasing the particle-collision rate and the demands on detector readout. A key element of this upgrade is the new CMS Tracker, whose silicon sensors will generate data at an aggregate rate of about 10 TB/s. This data must be routed efficiently to processing systems and filtered before storage. Serenity-S1 is an ATCA readout and data-processing card based on an AMD Virtex UltraScale+ VU13P FPGA with 128 high-speed serial transceivers. Twenty 12-channel Samtec FireFly optical modules provide up to 120 transmit and 120 receive channels at line rates of up to 25 Gb/s, while an additional optical module supports CMS Tracker links at up to 10 Gb/s per channel. The modular optical layout, combined with the large FPGA, makes Serenity-S1 suitable for several applications within and beyond CMS. ATCA provides the power and cooling required for operation in the CMS service cavern. Low-level management and the IPMI interface are provided by an OpenIPMC mezzanine, while an AMD Kria K26 system-on-module running AlmaLinux 9 performs higher-level board management and application control. Firmware and software frameworks provide common infrastructure so that users can focus on application-specific logic. The paper describes the hardware, firmware and software architecture of Serenity-S1 and its deployment in CMS Tracker integration and R&D activities.
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All authors G. Fedi, L. E. Ardila-Perez, M. Balzer, M. Fuchs, M. Holmberg, A. Howard, G. M. Iles, H. A. Krause, T. Mehner, D. Parker, M. Pesaresi, A. Rose, K. Whalen, T. Williams, J. Zhao, for the CMS Collaboration

Instrumentation and Detectors

UPLOAD-HELIX: High-Helicity Single-Mode Microwave Haloscope with Low-Noise Interferometric Readout for Ultralight Axion Dark Matter

Robert C. Crew, Emma C.I. Paterson, Maxim Goryachev et al. (+5 more)

We propose a superconducting single-mode microwave haloscope based on chiral cavity resonators for the detection of ultralight dark matter axions over the mass range $4\times10^{-19}$-$~4\times10^{-14}\,\mathrm{eV}$. Building on the single-mode chiral-cavity concept introduced by Bourhill et al. [Phys. Rev. D 108, 052014 (2023); arXiv:2208.01640], we develop a resonator geometry compatible with subtractive manufacturing from high-purity bulk niobium, taking advantage of the substantially lower surface resistance achievable relative to the additively manufactured Möbius cavity proposed in the earlier work. An inverse-design framework is then used to maximise a figure of merit derived to minimise the measurement time required to achieve a fixed experimental sensitivity. The resulting optimised bulk-niobium design achieves a figure of merit more than three orders of magnitude larger than the additively manufactured Mob̈ius benchmark. An experimentally informed microwave interferometric readout model, incorporating measured electronics noise and active suppression of pump amplitude noise, is used to project the sensitivity of the proposed experiment. For an acquisition time of three months, the haloscope is projected to reach $g_{a\gamma\gamma}<10^{-11}\,\mathrm{GeV}^{-1}$ across more than four orders of magnitude in axion mass. The projected sensitivity extends approximately one order of magnitude below the current exclusion limits set by CAST, providing a practical pathway towards a high-sensitivity direct search for ultralight dark matter axions.
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All authors Robert C. Crew, Emma C.I. Paterson, Maxim Goryachev, Eugene N. Ivanov, Pashupati Dhakal, Tugrul Talha Ersoz, Michael E. Tobar, Jeremy F. Bourhill

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval, and Application to Interferometric Sensing

Jordan Lontsi Tedongmo, Yann Ferrec, Laurence Croizé et al. (+3 more)

Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage. In this context, the Horizon Europe SCARBOn project assesses a low-cost satellite constellation featuring the NanoCarb imaging interferometer as its core sensor for monitoring CO2 and CH4 emissions in the atmosphere. However, estimating CO2 and CH4 concentrations with high revisit and spatial coverage poses significant challenges: full-physics retrieval algorithms commonly used rely on repeated high-resolution radiative transfer (RT) simulations, which are computationally expensive when using line-by-line RT models. As an alternative, we propose in this study a feedforward multilayer perceptron (MLP) surrogate designed to accurately and efficiently predict top-of-atmosphere radiances in the CO2 weak band, using a combined mean absolute error (MAE) loss on radiances and RT Jacobians to preserve both spectral accuracy and sensitivity to geophysical parameters. Coupling the MLP-based RT surrogate with the NanoCarb instrumental response yields an efficient and precise forward model for NanoCarb measurements, which shows promising results for CO2 concentration retrieval.
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All authors Jordan Lontsi Tedongmo, Yann Ferrec, Laurence Croizé, Pablo Musé, Gabriele Facciolo, Andrés Almansa

Optics · Cross-listed from cs.CV

From Pixels to PCells: A Neurosymbolic Approach to Photonic Component Creation

Aadarsh Agarwal, Kenaish Al Qubaisi and Dirk Englund

We present PixCell, a neurosymbolic system in which multimodal agents convert a visually presented photonic component into a parametric program over a small domain-specific language (DSL) of geometric primitives. A system enabling deterministic visual verification renders evaluation asymmetrically cheaper than the generation attempt. While models using multi-seed sampling and iterative revision reach a mean best-turn IoU of only 0.416, multimodal agents through PixCell's interface and verifier consistently exceed 0.9 mean IoU, with scores reaching 0.974 and 0.955 across eight component targets while also satisfying source contracts. These results demonstrate that frontier multimodal agents can reliably understand and render executable parametric representations from visual targets. Using these live parameters, cross-stack studies on an interferometer reconstruct primitive programs that satisfy an 8.0 nm free spectral range target and the original footprint constraint on modeled 220-nm SOI, 400-nm SiN, and 400-nm TFLN stacks. PixCell further carries a paper-derived splitter from visual reconstruction through SOI full-wave simulation, producing symmetric propagation and balanced outputs. Finally, the same executable verifier supplies a training reward and dataset used to train a Qwen3.6-35B-A3B model with LoRA and GRPO without supervised demonstrations. On eight training-excluded paper figures, its mean champion IoU rises from 0.422 after eight initial attempts to 0.491 after three verifier-guided revision rounds. These results therefore establish a controlled framework for measuring, retargeting, and improving visual-to-parametric photonic component design.
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Geophysics

Hybrid Quantum CNN for Cross-Sensor Spaceborne Volcanic Thermal Activity Recognition Worldwide

Claudia Corradino, Federica Torrisi, Alessandro Grilli et al. (+5 more)

As Earth Observation (EO) enters the Big Data era, the exponential volume of daily satellite imagery poses significant computational and storage challenges for classical Deep Learning (DL) models. Moreover, current approaches often struggle to generalize across heterogeneous sensors and volcanic environments while requiring large labeled datasets and substantial computational resources. These limitations are particularly critical for emerging On-Board Processing (OBP) applications, where memory, computational power, and annotated data are inherently limited. This work proposes a Hybrid Quantum AlexNet architecture for cross-sensor recognition of volcanic thermal activity at the global scale. The proposed model combines a classical convolutional backbone for high-level spatial features extraction with a parameterized quantum circuit (PQC) acting as a variational layer. By embedding high-level image representations into a high-dimensional Hilbert space, the quantum layer learns task-specific representations that enhance feature discrimination. Experimental results demonstrate that the proposed hybrid quantum model learns more discriminative feature representations, leading to improved cross-sensor transferability and robustness across heterogeneous volcanic environments using fewer trainable parameters and reduced training data than its classical counterpart.
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All authors Claudia Corradino, Federica Torrisi, Alessandro Grilli, Tommaso Catuogno, Mattia Verducci, Elisabetta Paladino, Luigi Giannelli, Alessandro Sebastianelli

Computational Physics · Cross-listed from cond-mat.soft

Design principles for energy dissipation in viscoelastic network metamaterials

Niranjan Sarpangala, Sean Fancher, Prashant K. Purohit et al. (+1 more)

Mechanical energy dissipation in networked materials is relevant for applications from vibration isolation to impact protection, yet identifying optimal dissipative architectures in large disordered truss networks is computationally prohibitive with conventional finite element methods. We develop an efficient graph Laplacian-based spectral framework for viscoelastic truss networks, in which the full continuum dynamics of each rod are retained exactly and the problem size scales with the number of joints rather than element-level discretization points. Using this framework, we investigate how redistributing cross-sectional areas within a network (without changing material composition) controls energy dissipation. We find that random redistribution typically reduces dissipation relative to a uniform baseline, while gradient-based optimization yields nontrivial architectures whose form is governed by the intrinsic attenuation length of the base material. Focusing on driving frequencies near a global resonant mode of the network, we show that the optimal mass distribution decays from the source (driven joint) with the attenuation length scale, and at small attenuation lengths the optimal architecture is independent of the boundary conditions. These results motivate future studies of dissipation length scale based design principles on more complex disordered architectures and provide an efficient computational framework for exploring such structures at scale.
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All authors Niranjan Sarpangala, Sean Fancher, Prashant K. Purohit, Eleni Katifori

Physics Education

Intro2QC: An Approachable Introduction to Quantum Computing for STEM Education in High Schools

Jaimie A. Greasley and Thomas E. Baker

In recent years, quantum computing has gained strong traction on the global stage. Pioneering developments in quantum engineering, algorithms and software reinforce the view that quantum computation will eventually catalyze transformative technological advancements across diverse sectors. In anticipation of the future impact of quantum computing, many governments, universities and tech organizations have invested heavily into advancing quantum science, boosting quantum readiness, and building a diverse, talented workforce through quantum education initiatives. Specialized university programs, public awareness campaigns and STEM outreach activities all serve a critical function in fostering a deeper understanding of quantum physics and quantum technologies among non-experts. At the level of high school, a major vision is for these initiatives to reshape the teaching of 20th century physics in STEM classrooms, and inspire the future generation of quantum scientists and professionals. In the current paper, we are presenting the materials we have created for an outreach workshop introducing quantum computing to high school students in an approachable and engaging way. Intro2QC is an exploration of quantum computing's relevance to solving real-world challenges by relating how the unintuitive behaviours of quantum systems can be useful in providing computational advantage. The workshop materials include an online interactive quantum programming tutorial hosted on the Intro2QC website, along with the full lesson plan for STEM instructors and the presentation slides available on the Github repository. This single-session 90-minute workshop has been delivered to approximately 160 students in multiple school groups from Grades 9-12 in British Columbia Canada in the highlight of the International Year of Quantum Science and Technology.
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Chemical Physics · Cross-listed from cond-mat.soft

Phase transitions and microphases in elastomers. I. Emergence of stable domains

Manu Mannattil, Haim Diamant and David Andelman

Elasticity often plays a key role in regulating phase separation in physical systems. Recent experiments have shown that elastic effects can be used to control microphase separation in swollen elastomers. Here, microphase separation arises from a mismatch between the characteristic length scales of elastic and thermodynamic interactions. In this first part of a two-part paper, we show that microphase formation in elastomers can be explained using conventional theories of elasticity through a nonlocal thermodynamic-elastic coupling arising from volume conservation. Our theory reproduces the observed dependence of phase transition temperature and domain size on elastomer stiffness in isotropically swollen elastomers. In the companion paper, we investigate the effects of anisotropic swelling and inhomogeneous elastic moduli.
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Chemical Physics · Cross-listed from cond-mat.soft

Nanobubbles, pristine emulsions, high ionic strength electrokinetics – paradoxes of Colloid and Interface Science

Andrei Dukhin, Renliang Xu and Darrell Velegol

There are three paradoxes in the modern Colloid and Interface Science supported by large bulk of experimental evidence and contradicting classical theoretical models: - Electrokinetics at high ionic strength; - Nanobubbles having life span on scale of days and weeks without any surface stabilization; - Pristine emulsions having life span on scale of days and weeks without any surface stabilization. We overview many dozens of experimental papers by broad spectrum of scientific groups from many countries. We consider this vast experimental data as unambiguous evidence that these phenomena exist. On other hand, classical theoretical models deny such possibility. This contradiction between experiment and theory justifies introduction of new theoretical models. We overview these models. It turns out that there is one common feature between most promising of them – assumption regarding structured water layer at hydrophobic interface. That is why we combine these three phenomena in this review. There is extensive literature on century old idea of the structured water layer, experimental and theoretical. We overview this literature, which provides convincing support to this hypothesis. We discuss existing theoretical models that incorporate the structured interfacial water layer for the successful explanation of these phenomena in more detail. Theoretical model of electrokinetics at high ionic strength was developed several decades ago. Theoretical model explaining paradoxical longevity of nanobubbles and pristine emulsions by interaction between structured water layer and electric double layer is more recent.
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