Optics
Cavity-Mediated Long-Range Cooperative Coupling between Localized Plasmons and Molecular Excitons
Livin Paul, Alona Vidishcheva, Gal Sandik et al. (+2 more)
Strong coupling between molecules and electromagnetic fields has emerged as a powerful strategy to modify the physical and chemical properties of molecules, enabled by the formation of hybridized energy levels through strong light-matter interactions. Rather than purely photonic or plasmonic modes, hybrid cavity fields that integrate plasmonic and photonic contributions provide versatile platforms with distinct advantages for tailoring light-matter interactions. Here, we present hybrid modes formed through the coupling between localized surface plasmon resonances of Au nanoparticles and Fabry-Perot microcavity modes. Furthermore, by using this hybrid platform, we demonstrate that the cavity field can give rise to cooperative coherent coupling between the spatially separated plasmonic nanoparticles and the molecules by mediating long-range dipole-dipole interactions. As we show, this cavity-mediated coupling also enhances the plasmon-
exciton mixing, as compared to their direct interaction. This configuration opens new avenues for tailoring light-matter interactions at the nanoscale by supporting novel hybrid plexcitonic states that incorporate contributions from plasmons,
excitons, and extended cavity fields.
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All authors Livin Paul, Alona Vidishcheva, Gal Sandik, Adina Golombek, Tal Schwartz
Fluid Dynamics · Cross-listed from cond-mat.soft
Quantum Enhancement of Particle-Size Segregation
Tomás Trewhela
Segregated states based on particle size emerge in granular materials from the competition between segregation and diffusive remixing. Here, we show that
quantum coherence can enhance segregation beyond this classical limit. We introduce an open
quantum cellular automaton for bidisperse mixtures that combines coherent transport and dissipative segregation. The automaton reproduces experimental and continuum-theory segregation dynamics, with segregation degrees collapsing onto a theoretical Péclet-dependent relationship. However, weakly decohering systems exhibit a coherence-driven transport regime that produces more strongly segregated steady states than classical predictions. Across a broad parameter range, the steady-state degree of segregation collapses onto two dimensionless numbers governing the competition between segregation,
diffusion, and decoherence. These results identify
quantum coherence as a mechanism for enhancing particle-size segregation and establish a framework for studying transport phenomena in open many-body systems.
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Computational Physics · Cross-listed from cs.MS
NektarIR: A Domain-Specific Compiler for High-Order Finite Element Operations on Heterogeneous Hardware
Edward Erasmie-Jones, Giacomo Castiglioni and David Moxey
Modern high performance computing (HPC) applications must target heterogeneous hardware. This requires significant work to ensure domain specific implementations translate to highly performant kernels across a range hardware types and vendors, each requiring bespoke optimization to make use of the specific target architecture. Through the development of a domain specific compiler built with the multi-level intermediate representations (MLIR) project, one can express a high-level, close to the specific domain, abstraction that is progressively lowered to a low, close to metal, abstraction. At each intermediate representation (IR), appropriate optimizations can be applied without costly analysis due to the knowledge embedded in the domain specific IRs. We apply this method to the construction of discrete differential operators for use in spectral/hp element method solvers for computational fluid dynamics (CFD). Here, the performance is driven by a small set of common finite element operators that are composed to create kernels for the discrete differential operators used to solve weak partial differential equations. We create our own MLIR dialect to represent these operators and implement a bespoke lowering pipeline to facilitate the just-in-time compilation of these kernels for both CPU and GPU architecture and illustrate performance comparisons with the Nektar++ spectral/hp element framework.
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Optics
Experimental Investigation of Surface Passivation Chemistries for Optical Nanotweezers
Maxwell T. Ugwu, Kewei Li, Abayomi Opadele et al. (+2 more)
Nanotweezers are actively investigated as a powerful means to reversibly trap and characterize nanoparticles with profound biological and environmental importance. To ensure that these particles can be reversibly trapped and released, surface passivation is essential. To mitigate the issue of fouling, we investigated the antifouling properties of poly(sodium styrene sulphate) (PSS) synthesized using the Atom Transfer Radical Polymerization (ATRP) technique on our previously reported gold-based Interferometric Electrohydrodynamic Tweezers (IET) device. Fluorescence and interferometric scattering (ISCAT) imaging were used to record trapping performance and study the antifouling properties of our passivated nanotweezer device. The results show that PSS exhibits superior anti-fouling performance against polystyrene nanoparticles when compared to 11-mercaptoundecanoic acid (MUA). By comparing the antifouling properties of PSS and zwitterionic poly(methacryloyloxyethyl phosphorylcholine) (PMPC) in preventing extracellular vesicle adhesion, we found that both exhibited similar performance. Overall, the ATRP technique is broadly applicable across nanotweezer substrates with appropriately chosen initiators.
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All authors Maxwell T. Ugwu, Kewei Li, Abayomi Opadele, Theodore Anyika, Justus C. Ndukaife
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Silicon Nanostructures for Biosensing: From Field-Effect Transistors to Photonic Resonators, and the Long Road to the Clinic
Ang Liu, Jun Cao, Zhihao Sun et al. (+1 more)
Silicon has a unique combination of properties that makes it one of the best material choices for biosensor platforms: it is inexpensive, its native
oxide is atomically smooth, its fabrication processes are CMOS-compatible and have been refined for more than three decades, and it can support many transduction mechanisms in biosensor design. Over the past thirty years, researchers and engineers have used silicon nanostructures to produce ion-sensitive transistors, ultrasensitive nanowire field-effect biosensors, refractive-index-based porous silicon films, microring photonic resonators, suspended cantilevers, luminescent
quantum dots, and solid-state nanopores. These device families have demonstrated successful sensing capabilities at the single-molecule, single-virus, or sub-femtomolar level under laboratory conditions; however, they have rarely been widely deployed in clinical assays. This gap is mainly caused by several well-characterized bottlenecks: for nanowire BioFETs, device variability and Debye screening; for porous silicon, fouling, pore wetting, and surface stability; for silicon photonics, thermal drift, spectral readout, and packaging; and across all platforms, calibration, reproducibility, and validation in real biofluids. In this review, we trace the development of silicon biosensors from their early stages to their current state, search and organize the literature focusing on the three most mature platforms and a set of emerging directions, summarize and compare the performance and bottlenecks of different platforms, and argue that progress over the next decade will come primarily from integrated readout, interface engineering, and systematic benchmarking rather than from the discovery of new silicon nanostructures.
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All authors Ang Liu, Jun Cao, Zhihao Sun, Jingsong Shang
Fluid Dynamics
Towards bridging the gap between data-driven and theoretical turbulence closures in stratified flows
Laure Zanna and Pavel Perezhogin
Turbulence closure models are essential for solving the equations of motion in realistic systems, where fully resolving all relevant scales of motion is computationally infeasible. Developing
turbulence closures remains one of the most challenging problems in fluid dynamics. Specifically, the Navier-Stokes equations, when filtered to isolate large-scale motions, introduce new terms representing the influence of subgrid-scale turbulent stresses. These terms, which can only be computed directly by resolving the
turbulence itself, therefore lead to the closure problem: we must add new equations or introduce assumptions to relate the unresolved scales of motions to the resolved flow. Here we consider the closure problem for oceanic flows, i.e., stratified, Boussinesq, incompressible, in a rotating frame of reference. In particular, we focus on a closure for ocean mesoscale eddies, which have horizontal scales of 10-100km and are key to the redistribution of momentum, energy, and tracers in the ocean. In particular, mesoscale eddies can reinject energy and momentum into the large-scale flow through an inverse energy cascade. Here, we explore a range of theoretical and data-driven ocean mesoscale closures and examine their connections using analytical and data-driven methods. This note aims to bridge the gap between novel methods from artificial intelligence (AI) and machine learning and theoretical fluid dynamics to address significant challenges in the physics of
turbulence.
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Physics and Society · Cross-listed from cs.MA
Artificial collectives of specialists and generalists excel at different tasks
John Meluso, Laurent Hébert-Dufresne, Christoph Riedl et al. (+1 more)
Collective artificial intelligence, where multiple
agents work on shared tasks, holds potential to solve expansive problems in fields from medicine to collective governance. But while prescriptive engineering solutions abound, we lack descriptive scientific understanding of artificial collectives, and therefore principles for how to design resource efficient multi-
agent systems. Through systematic experiments with optimizing
agents, we characterize how
agent interpretive abilities, rationality bounds, and task qualities interact to shape collective performance.
Agents range from specialists, with narrow interpretive abilities, to generalists, with broad ones. Collectives of specialists correspond to sparse, centralized networks, while collectives of generalists correspond to dense, decentralized ones. We show that interpretive network properties have small performance effects on average (0.07 standard deviations of performance). However, for specific task qualities, these effects are 4.5 times larger (0.33 sd) and can reach much higher for certain task qualities (1.84 sd). This leads collectives of generalists to perform better on tasks that involve generating, choosing, and coordinating, while collectives of specialists with a few generalist mediators perform better on tasks that involve negotiating. Rationality bounds then moderate these relationships. At loose bounds, specialists outperform generalists through more effective sampling of high-dimensional decision spaces. At tight bounds, generalists outperform specialists through better gradient estimation. A fundamental trade-off between performance and convergence speed emerges at moderate bounds. These findings suggest that multi-
agent design could benefit from matching interpretive networks to both task demands and
agents' computational limits, with implications for the efficiency and energy costs of multi-
agent systems.
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All authors John Meluso, Laurent Hébert-Dufresne, Christoph Riedl, H. Oliver Gao
Plasma Physics
Phase-Space Energy Transfer of Wave-Particle Interactions using the Field-Particle Correlation Technique and Linear Plasma Theory with JET-PLUME
Collin R. Brown, Gregory G. Howes, Kristopher G. Klein et al. (+1 more)
The collisionless transfer of energy between fields and particles through wave-particle interactions is a fundamental process in space
plasmas but remains incompletely characterized because many mechanisms operate across a wide parameter range and diverse
plasma conditions. The Field-Particle Correlation (FPC) technique reveals velocity-space signatures of particle energization by correlating measured electric field fluctuations with changes in the velocity distribution. Fully mapping these signatures across
plasma parameters requires an impractically large number of kinetic simulations or observations. To address this challenge, we introduce JET-PLUME (Judging Energy Transfer in a
Plasma in a Linear Uniform Magnetized Environment), an extension of the PLUME Vlasov-Maxwell dispersion solver. JET-PLUME uses PLUME's ability to model parallel drifting bi-Maxwellian distributions to examine phase-space energy transfer by adding an analytic
Fourier-space formulation of the FPC. This approach isolates the contribution of individual resonances, separates degenerate
entropy mode components, and allows systematic analysis of unstable, growing modes. Dimensionless expressions extend the results across a broad parameter range and highlight the role of off-diagonal elements of the susceptibility tensor in coupling electric field and current response. We show that during kinetic Alfvén wave damping, the perpendicular field can drive parallel ion currents among particles with large perpendicular velocity, reducing the net Landau damping. The resulting velocity-space signatures, accessible through JET-PLUME, demonstrate how analytic formulations of phase-space energy transfer can reveal novel physics of wave-particle interactions across diverse
plasma environments.
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All authors Collin R. Brown, Gregory G. Howes, Kristopher G. Klein, Jason M. TenBarge
Plasma Physics
Absolute Radar-REMPI via Two-Color REMPI and Absolutely Calibrated CMS for Diagnostics of Species in Gaseous Mixtures
Mathis Malaussena, Liam West, Kenneth Reindersma et al. (+2 more)
This work demonstrates the initial development of the Absolute Radar-REMPI diagnostic technique, a development of the Radar-REMPI technique that provides an approach for absolutely calibrated measurements, which is universally applicable to any type of tested gaseous species. Absolute measurements are achieved through the utilization of two-color Resonance Enhanced Multiphoton Ionization (REMPI) and absolutely calibrated Coherent Microwave Scattering (CMS). Two-color REMPI of molecular oxygen (O2) was demonstrated using two intersected
laser beams: a 355 nm beam and a tunable parametric beam at 241-242 nm. The results confirm that the two-beam configuration can successfully generate measurable ionization associated with the targeted transition of O2 within the beam intersection region, as verified using an ICCD camera. These findings establish a foundation for the further development of Absolute Radar-REMPI, which has the potential to enable highly sensitive, real-time diagnostics of absolute species densities in unsteady flowfields such as those occurring in hypersonic shock tunnels and electric propulsion systems.
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All authors Mathis Malaussena, Liam West, Kenneth Reindersma, Nicholas Babusis, Alexey Shashurin
Fluid Dynamics
Receptivity and Biorthogonal Decomposition in a Reacting Temporal Mixing Layer
Sriram P. Kalathoor and Joseph C. Oefelein
We examine receptivity and biorthogonal decomposition in a reacting temporal mixing layer using direct and adjoint eigenmodes of a finite-thickness compressible linearized operator built from the mean reacting base state. The analysis focuses on the Kelvin–Helmholtz branch and asks how the reacting base state modifies the selected temporal instability, where localized forcing most efficiently excites it, and how strongly the associated modal family is represented in time-resolved planar simulation data. Receptivity maps are constructed for mass, momentum, thermal, and mixture-fraction forcing channels using an energy-weighted adjoint projection, with biorthogonality enforced by the corresponding direct–adjoint inner product. A complementary biorthogonal decomposition provides modal amplitudes and cumulative few-mode reconstructions at the fundamental streamwise wavenumber. The finite-thickness branch is interpreted against a compressible
vortex-sheet reference built from the outer-stream states. The reacting layer supports an unstable finite-thickness Kelvin–Helmholtz family over low-to-moderate wavenumbers even though the discontinuous reference is essentially neutral. Mass forcing leads the raw localized receptivity maps, mixture-fraction forcing follows through composition-pressure coupling, and chemistry-weighted thermal forcing identifies the strongest thermochemical support of the same family. The results show how distributed reacting thermodynamics reorganize compressible shear-layer instability and how that reorganized branch remains embedded in the nonlinear flow.
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Instrumentation and Detectors
Deep-Ocean Application-Specific Neutrino Experiment
Takumi Araki, Simran Chauhan, Lyla Choi et al. (+17 more)
This report introduces the concept, prototype design, projected costs, and scientific goals of a mobile experiment for detecting geoneutrinos originating from uranium and thorium decay chains in the Earth's mantle. This will constrain the
planet's radiogenic heat production and unearth its geochemical makeup. This design of a deep-ocean mobile
neutrino experiment, which is not mirrored by any active or planned experiments, supports physics and geoscience's goal of multi-modal data on the Earth's internal composition and structure. Based on geoscientific studies, this design is expected to achieve a 50–100-fold reduction in crustal background compared to similarly sized continental detectors, thereby enabling direct measurements of mantle geoneutrinos. The multiple stereoscopic projections enabled by the detector's unique mobility can map spatial variations in heat-producing elements within the mantle. Beyond discussing the design, we report on our collaboration's most recent hardware developments in the active prototyping of this detector. We briefly highlight the potential multiuse and interdisciplinary nature of this detector.
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All authors Takumi Araki, Simran Chauhan, Lyla Choi, Brian C. Crow, Max A. A. Dornfest, John Graham, Misaki Hosoya, John G. Learned, Viacheslav A. Li, William F. McDonough, Takeru Ohno, Takanobu Ono, Taichi Sakai, Jackson Seligman, Nathan Sibert, Shang-Wen Stradleigh, David Vartanyan, Hiroko Watanabe, Zhihao Xu, Jeffrey G. Yepez
Chemical Physics
A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis
Chen Yang, Zheng Fang, Hanyu Sun et al. (+7 more)
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great potential for its rapid and automated interpretation. However, the scarcity of experimental NMR
datasets has constrained deep learning in this domain to narrow, task-specific applications that lack broad generalization. Here, we introduce UltraNMR, a large-scale foundation model for NMR that leverages the intrinsic properties of NMR spectra to learn generalizable spectral representations. We collected 158 million paired simulated $^{1}$H and $^{13}$C NMR spectra to train UltraNMR, employing multiple domain-specific pre-training objectives. UltraNMR captures both intra-spectral and inter-spectral dependencies, enabling seamless simulation-to-real adaptation. We demonstrate that adapting UltraNMR to a range of molecular structure analysis tasks on experimental NMR spectra consistently yields state-of-the-art performance and clearly outperforms UltraNMR variants trained directly on downstream data without simulation pre-training. We also construct a large-scale NMR spectral vector library by encoding simulated NMR spectra using UltraNMR, covering 94 million unique molecules and enabling effective structure-aware
retrieval. In real-world applications, UltraNMR facilitates the structural elucidation of two previously unknown natural products from Chinese herbal medicines recorded in the Chinese Pharmacopoeia. These results suggest that large-scale simulation pre-training can effectively bridge the simulation-to-real gap, enabling robust and generalizable molecular structure analysis of real-world NMR spectra.
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All authors Chen Yang, Zheng Fang, Hanyu Sun, Fanjie Xu, Hongxin Xiang, Hanyu Gao, Xiangxiang Zeng, Yuqiang Li, Xiaojian Wang, Jun Xia
Chemical Physics
Empowering Polymeric Materials Discovery by Artificial Intelligence
Chenyao Ma, Linda Zhang, Yuheng Chen et al. (+29 more)
Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural
evolution across multiple length and time scales. Consequently,
polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools,
polymer databases, predictive models, AI
agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific
reasoning. We argue that
polymer science is entering an era of autonomous discovery, in which data, simulation,
reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for
polymer innovation, fundamentally transforming how
polymer research is conducted.
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All authors Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, Rui Su, Gang Wang, Muyao Yu, Dong Zhong, Jie Zhu, Weibo Gong, Huan Gu, Limin Li, Chen Shen, Rui Wu, Zhenghao Wu, Kan Xu, Min Zhou, Donglin He, Xiayun Huang, Shan Jiang, Pengfei Ou, Jiayu Peng, Yuwei Zhang, Jie Zhao, Di Zhang, Piao Ma, Zhenghao Li, Hao Li
Fluid Dynamics
State estimation of Rayleigh-Bénard convection with reduced-order models
Enrique Flores-Montoya, André F. C. da Silva and André V. G. Cavalieri
In this work, we develop a state estimation framework for two-dimensional Rayleigh-Bénard (RB) convection that combines a stable Galerkin reduced-order model (ROM) with an extended Kalman filter (EKF). The ROM, constructed from controllability modes of the linearised Boussinesq equations, provides the nonlinear dynamical model for the filter prediction step. Direct numerical simulations (DNS) are used to generate synthetic measurements for data assimilation. We assess filter performance across periodic, quasiperiodic, and chaotic regimes, demonstrating that the filter tracks the most energetic modes with high fidelity and achieves time-averaged reconstruction errors below $14\%$ for velocity and $9\%$ for temperature. We apply the ROM-based EKF to a hybrid simulation scenario where the system state is assimilated from coarse PIV-like velocity measurements. It is shown that velocity observations alone suffice to reconstruct the state, including the temperature field. Finally, we exploit the Kalman gain
matrix to develop a greedy sensor placement strategy that progressively removes the least informative sensors. The
algorithm reveals a clear hierarchy among sensor types and can be used to derive skeletal observation configurations. It also provides guidance on which measurement variables and spatial locations are most informative for state correction. The present framework is general, and may be applied to other quadratic Galerkin ROMs for state estimation.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
A Defect-Free Model of Amorphous Silicon with Pristine Electronic Structure
Louise A. M. Rosset, Chinonso Ugwumadu, Stephen R. Elliott et al. (+2 more)
Amorphous silicon (a-Si) is understood to be the canonical continuous random network material, ideally defined by fully fourfold coordination. Here, we show that a defect-free ('ideal') model of a-Si from machine-learning-driven molecular-dynamics simulations [L. A. M. Rosset et al., Nat. Commun. 16, 2360 (2025)], subsequently evaluated with hybrid-level density-functional theory computations, can accurately reproduce the experimentally observed electronic bandgap. We compare this model with one resulting from the Wooten-Winer-Weaire (WWW) bond-switching approach and with other recent approximants to ideal a-Si. More broadly, our work provides a platform for studies of band tails, optical properties, and transport in a-Si.
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All authors Louise A. M. Rosset, Chinonso Ugwumadu, Stephen R. Elliott, David A. Drabold, Volker L. Deringer
Biological Physics · Cross-listed from q-bio.PE
West Nile virus outbreak in Italy modelled with the quantum Game of Life
Andrea Fontana, Simone Tambascia, Ciro Di Carluccio et al. (+3 more)
In the last years, an anomalously high spreading of West Nile virus (WNV) has been observed in Italy, with particularly high peaks of infections in southern Lazio, Campania and Veneto regions. The main disease vector for WNV is represented by Culex pipiens mosquitoes, which spread human infections through their bites. Here, we investigate WNV fever epidemic
diffusion during summer season 2025 in Italy through a computational approach based on a
quantum version of the Game of Life (GOL) cellular automaton model. Specifically, human dynamics evolves according to the GOL rules, while
stochastic dynamics of disease vectors, i.e., mosquitoes, as well as their interaction with humans, simultaneously occur. We show that this model fits the curves of cumulative infected individuals with high accuracy, either at local and average-regional level, with only optimization of mosquito birth and removal rates parameters. Furthermore, leveraging model flexibility, we show that changes in model parameters values elucidate system response to environmental variations. For instance, we quantify, e.g., the impact of mosquito spreading containment measures or sudden mosquito increasing abundance due to climatic and ecological changes. Overall, we provide a general, quantitative description of WNV infection spreading in Italy which could represent a supportive tool to test different environmental scenarios and could be useful to devise strategies for decision makers to monitor disease vector dynamics and to control consequent virus
diffusion.
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All authors Andrea Fontana, Simone Tambascia, Ciro Di Carluccio, Andrea Esposito, Bernardo Spagnolo, Andrea M. Chiariello
Physics and Society · Cross-listed from q-fin.RM
Optimal Order of Multi-Agent and General Many-Body Systems
Jake J. Xia
This paper develops a general framework for analyzing multi-
agent systems with feedback loops between
agents actions and collective observations. The framework is built on two fundamental
agent-level variables: power, which measures
agent influence on collective outcomes, and response functions, which determine how
agents react to observations. We derive how macroscopic properties, including total power, useful power,
entropy, order, fragility, and mobility, emerge from these two variables of heterogeneous
agents. To study the trade off between growth and resilience, we introduce a system-level utility function parameterized by a risk-appetite coefficient and derive an optimal degree of order that balances productivity, stability, and adaptability. The analysis suggests that stronger synchronization can increase collective output but may also increase systemic fragility and reduce mobility. We further argue that order,
entropy, information, and useful energy are task-dependent and system-relative concepts whose meanings depend on the objectives of the system. By measuring and designing
agent power distributions and response functions, it may be possible to better understand, predict, and optimize collective behavior and identify the conditions under which collective intelligence and optimal order emerge.
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Computational Physics · Cross-listed from cs.LG
Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks
Zongmin Yu and Liu Yang
Mathematicians understand a PDE solution through mathematical structures rather than tables of computed values. Historically, this has been the product of mathematical analysis, carried out by hand for each problem individually. Neither numerical simulation nor neural networks produce those structures directly. We propose Agentic Symbolic Search (ASYS), a prior-guided framework in which an
agent translates PDE theory, public problem constraints, and accumulated search experience into testable differentiable symbolic programs. The mathematical forms are refined under evolutionary search, while their continuous parameters are fit by gradient-based optimization. This makes the search an automated form of inductive-bias injection rather than blind symbolic regression. For problems with known analytical forms, ASYS recovers these forms naturally; for other problems, ASYS constructs analytical approximations which can guide mathematicians toward further analysis. In our experiments, across five problems spanning bounded dynamics, finite-time blow-up, and free-boundary focusing, ASYS produces interpretable representations, including a
geometric interface formula for Allen-Cahn 2D dynamics and a nine-parameter contraction law for Keller-Segel chemotactic blow-up, in settings where no closed-form description was previously available. ASYS shows the possibility of a new paradigm for characterizing PDE solutions, beyond handcrafted analytical solutions, mesh-based numerical solutions, and neural network approximations.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Polaronic hybridization of atoms, dimers and trimers in a Bose-Einstein condensate
Carsten Robens, Arthur Christianen, Alexander Y. Chuang et al. (+4 more)
The Bose polaron problem of an
impurity immersed in a Bose-Einstein condensate (BEC) has been predicted to feature strong correlations arising from bound states of multiple
bosons with the
impurity. While direct experimental evidence has so far remained elusive, here we observe clear signatures of three-body correlations in Bose polarons. We perform radiofrequency spectroscopy on $^{40}$K
impurities in a BEC of $^{23}$Na and identify polaronic hybrid states that can be understood as superpositions of the bare atom, a NaK dimer and a Na$_2$K trimer, coupled through coherent particle exchange with the condensate. We show that the main spectroscopic features are captured by a simple three-level model without free parameters. Our work shows how a condensate environment can coherently hybridize bound states of different composition and mass, reminiscent of
quark-
flavor mixing described by the Cabibbo-Kobayashi-Maskawa (CKM)
matrix in particle physics.
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All authors Carsten Robens, Arthur Christianen, Alexander Y. Chuang, Huan Q. Bui, Yiming Zhang, Richard Schmidt, Martin Zwierlein
Optics
Plasma Etch Process Optimization for Photonic-Grade Diamond-on-Insulator Substrates and Thickness Evaluation using Colorimetry
Tianyin Chen, Alessio Miranda, Leyla Rami et al. (+2 more)
Diamond color-center
qubits integrated with photonic
circuits can be initialized, manipulated, entangled, and read individually with high fidelity, making them attractive for large-scale modular
quantum computers,
quantum networks, and distributed
quantum sensing. However, the limited size of heteroepitaxially grown single-
crystal diamond (SCD) and photonic-grade diamond-on-
insulator (DOI) substrates remains a challenge for integration with existing manufacturing processes. Here, we develop a
plasma etch recipe to thin direct-bonded (100) SCD membranes (<50 $\mu$m) into large-area, thin-film DOI substrates, and demonstrate free-standing photonic chiplets fabricated from the resulting DOI. The ICP-RIE recipe preserves diamond bonding, provides sufficient micromasking and surface-quality control, and enables thin-film DOI manufacture. We thin a 10 $\mu$m diamond plate bonded to SiO$_2$/Si and obtain a photonic-grade DOI substrate with diamond thickness $\leq$300 nm. The DOI film is around 300 nm thick over 0.5 $\times$ 0.5 mm$^2$, with surface roughness < 0.5 nm, while the bonding interface remains intact. Diamond photonic chiplets are fabricated on this DOI substrate using a standard two-step lithography process, without complex thin-film transfer, under-etching, or pedestal formation. We also present a colorimetric study of diamond visibility on SiO$_2$ and quantify color differences across thicknesses in common colorimetric spaces. This analysis enables automatic diamond-thickness extrapolation from standard optical microscope images with 5 nm resolution, in good agreement with white-light interferometry (WLI) measurements. The DOI substrate and colorimetric thickness-evaluation method provide an effective fabrication platform and reliable validation route for scalable manufacturing of diamond nanophotonic devices, opening a path toward large-scale integrated
quantum systems.
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All authors Tianyin Chen, Alessio Miranda, Leyla Rami, Ryoichi Ishihara, Salahuddin Nur
Medical Physics
Efficient and Accurate Image Reconstruction for Geometric-Inconsistent Multispectral CT with Ray-Dependent Energy Spectra
Ziqiang Zhang and Chong Chen
In practical multispectral computed
tomography (MSCT), the scanning
geometric parameters under different X-ray energy spectra are often inconsistent, and the distributions of the energy spectra are even ray-dependent. However, existing
algorithms cannot effectively and accurately solve the associated image reconstruction problem. To address this limitation, using the proposed aggregated energy spectra, we approximate the Jacobian
matrix of the nonlinear forward operator at certain special points (e.g., the zero point) as a block product of a diagonal
matrix composed of projection
matrices and a very small-scale
matrix, and then based on this
matrix with a special structure, propose an efficient and accurate image reconstruction
algorithm tailored for
geometric-inconsistent MSCT with ray-dependent energy spectra. Under appropriate conditions, we establish the convergence theory for the proposed
algorithm. Furthermore, numerical experiments using both noiseless and noisy projection data are conducted to verify the performance of the proposed
algorithm, which demonstrate that the efficiency and accuracy of this
algorithm are much higher than existing
algorithms, offering the flexibility and scalability to accommodate various MSCT imaging configurations.
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Optics
Attosecond Path Qubits in High-Harmonic Generation: Classical Dephasing and Trace-Out Decoherence
A. Marchisio, C. Granados, M. F. Ciappina et al. (+1 more)
High-harmonic generation (HHG) is governed by interference between electron trajectories. We propose that the dominant short and long trajectories define an experimentally addressable two-level subsystem: an attosecond path
qubit (APQ). We formulate a trajectory-resolved density
matrix to identify two distinct coherence-loss mechanisms: classical dephasing from ensemble averaging and
quantum decoherence arising from the trace-out of unobserved degrees of freedom. By investigating shot-to-shot fluctuations and unresolved transverse momentum, we demonstrate that while dephasing suppresses coherence through averaging, the “trace-out” channel produces mixed states even for fixed driving parameters. We explore how these mechanisms modify APQ purity and show that mode selection and conditioning provide operational routes to isolate them. These results establish a reduced-state framework for diagnosing coherence loss in HHG and for engineering trajectory-based
quantum states in attosecond interferometry.
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All authors A. Marchisio, C. Granados, M. F. Ciappina, O. Cohen
Applied Physics
Determination of the intrinsic mechanical quality factor in high-stress silicon nitride resonators
Geena Benga, Vincent Dumont, Wei Wang et al. (+6 more)
Recent advances in silicon nitride nanomechanical resonators have pushed mechanical quality factors to ultra-high values by combining stress-induced dissipation dilution with mode-shape engineering. Neither mechanism alters the intrinsic quality factor $Q_{\mathrm{intr}}$. Targeting the intrinsic loss itself therefore remains an untapped route to even higher $Q$. Doing so first requires reliable quantification of $Q_{\mathrm{intr}}$, which has proven challenging. Here we present a robust methodology that quantifies $Q_{\mathrm{intr}}$ by combining automated mode identification with systematic ringdown measurements over a large number of mechanical modes. Applied to high-stress silicon nitride membranes, it reveals a systematic dependence of $Q_{\mathrm{intr}}$ on thickness that cannot be described using established models, particularly in the ultra-thin limit. We account for this trend with a phenomenological model that incorporates a thickness-dependent loss channel. Together, our method and model open a route toward a microscopic understanding of intrinsic dissipation and toward directly mitigating its loss channels.
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All authors Geena Benga, Vincent Dumont, Wei Wang, Nicola Cavalleri, Ariane Giesriegl, Silvan Schmid, Christian L. Degen, Antonius Armanious, Alexander Eichler
Fluid Dynamics
Planar Lagrangian transport and scalar-gradient organization in a turbulent reacting shear layer
Sriram P. Kalathoor and Joseph C. Oefelein
We analyze planar Lagrangian transport and scalar-gradient organization in a supersonic, reacting hydrogen-air temporal mixing layer using time-resolved mid-plane data from a three-dimensional direct numerical simulation. The analysis combines forward/backward finite-time Lyapunov exponent (FTLE) fields, operational FTLE-ridge skeletons, Cauchy-Green deformation measures, shear-LCS metrics, and planar
hyperbolic geodesic-LCS extraction to examine how finite-time stretching structures the reacting shear layer. The time-resolved FTLE ridges identify repelling and attracting finite-time transport skeletons in the constrained two-dimensional slice, from which ridge geometry, intersection occupancy, persistence, and scalar-conditioned transport are quantified.
Hyperbolic geodesic LCS are extracted from Cauchy-Green tensors reconstructed from planar flow maps as strainlines seeded at high-$\lambda_{\max}$ normal maxima, providing a variational counterpart to the operational FTLE-ridge skeleton. We then relate the transport skeleton to temperature, mixture fraction, and a reaction intermediate. The results show localized forward/backward ridge overlap, strong scalar-gradient enrichment, finite-time geodesic LCS that occupy the same high-strain transport skeleton, residual direction-dependent separation from a time- and cross-stream-stratified null model, and scalar-response lags that remain compact relative to decorrelation and FTLE-integration scales. Together, these results provide a transport-oriented characterization of coherent structures and their role in mid-plane mixing within a compressible reacting shear flow.
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Atomic Physics · Cross-listed from quant-ph
Observation of alignment tensor effects in metastability-exchange collisions with highly polarized 3He ensembles
Yida Sha, Kaiwen Yi, Xingqing Jin et al. (+2 more)
Highly polarized 3He ensembles prepared by metastability-exchange optical pumping (MEOP) have been widely used in precision measurements and fundamental physics. Metastability-exchange (ME) collisions, serving as the basis of MEOP, are traditionally described in terms of atomic orientation, while the significant contributions of metastable
alignment tensor at high polarization remain unexplored. In this work, we develop a linearized model under mean-field approximation to investigate
alignment tensor effects in highly polarized 3He , which originate from the metastable F = 3/2
manifold and are revealed through ME-induced relaxation and frequency shift. By means of free-induction-decay (FID) measurements, a pronounced dependence on
nuclear polarization is experimentally observed in the response of the ground-state-metastable hybrid 3He ensembles to the external
magnetic field. Furthermore, after obtaining the characteristics of tensor-induced phenomena, we demonstrate good agreement between the experiment and the theory. This work advances the understanding of
nuclear spin dynamics in highly polarized 3He using MEOP. It further provides applications in systematic error correction of high-accuracy magnetometry, as well as in optimal protocol for the generation of
nuclear spin-squeezed states.
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All authors Yida Sha, Kaiwen Yi, Xingqing Jin, Matteo Fadel, Xiang Peng
Geophysics · Cross-listed from cs.LG
Constrained hybrid modelling to predict microbial dynamics and organic matter turnover in soil systems
Paul Collart, Juergen Gall, Andrea Schnepf et al. (+2 more)
Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate
climate change and environmental threats. Representing microbial dynamics in process-based soil models is therefore critical to predict carbon cycling in soils, albeit highly challenging to inform from data. One promising approach to improve their parametrisation is the integration of genomic data, yet modelling the complex and unknown relationship between genomes and the processes the microbes are driving is an unsolved problem. In this work, we present the first hybrid modeling framework for deriving biokinetic parameter values of a process-based soil organic matter turnover model from metagenome-inferred functional traits based on DNA sequencing data. Our model predicts biokinetic parameters of the process-based model from genomic trait data with a neural network and integrates constraints from ecological theory and literature to ensure realistic behavior, even of non-observed state variables. We evaluate our method on synthetic genomic trait
datasets of varying complexity and on real data, showing that our approach improves performance over multiple baselines and learns the dynamics of unmeasurable components of the process-based model effectively, even for small training
datasets.
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All authors Paul Collart, Juergen Gall, Andrea Schnepf, Holger Pagel, Lars Doorenbos
Atomic Physics · Cross-listed from quant-ph
Effective Faraday interaction between light and Helium-3 nuclear spins in a multi-pass cell
Kaiwen Yi, Yida Sha, Zejia Lin et al. (+2 more)
Helium-3
nuclear spins form an exceptionally stable
quantum system with extremely long coherence time, offering exciting opportunities for
quantum technologies. In particular,
nuclear spin-squeezed states promise enhanced precision for sensing tasks and tests of new physics. A central challenge for all these applications is the realization of a controllable light-
nuclear spin interface. Here we experimentally demonstrate such an interface by exploiting metastability-exchange collisions in a low-pressure helium-3 gas
cell at room temperature. A
radio-frequency discharge produces a small population of metastable atoms that both enables efficient optical pumping and mediates an effective Faraday interaction between the collective
nuclear spin and an optical probe. We quantitatively characterize the strength of this interaction as a function of the
nuclear polarization, applied
magnetic field, and probe-beam parameters. Moreover, we show that using a multi-pass
cell enhances this interaction by effectively increasing the optical depth. Extrapolating to a tenfold increase of the probe power used in the present experiment, we project a measurement-induced squeezing rate of 0.52 s$^{-1}$. Our results provide a practical pathway for optical access to helium-3
nuclear spins and open prospects for generating long-lived, macroscopic
nuclear spin-squeezed states for
quantum metrology.
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All authors Kaiwen Yi, Yida Sha, Zejia Lin, Matteo Fadel, Xiang Peng
Computational Physics · Cross-listed from cs.LG
Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEs
Xiang Rao and Yuxuan Shen
We develop QCPIKAN, the first
quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations (PDEs). Built upon Chebyshev-
polynomial KAN layers and parameterized
quantum circuits, this hybrid framework embeds physical constraints into the training loss to enforce physical consistency. Our theoretical investigations grounded in approximation theory prove that this design accelerates high-frequency error convergence to an exponential rate and effectively mitigates numerical dispersion. We validate the framework across three typical seepage scenarios in porous media, including single-phase flow, component transport and two-phase flow. Compared with existing
quantum-classical physics-informed neural networks, QCPIKAN achieves superior performance in global prediction accuracy, local error control, dynamic
evolution tracking and displacement front localization. This work provides a robust and efficient alternative for solving complex PDEs.
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Fluid Dynamics
Restarts of bursts in turbulence in a log-minimal channel
Zengrong Hao and Javier Jiménez
Recent evidence on the sustainment of wall-normal-velocity bursts in wall-bounded
turbulence challenges the classical streak-dependent picture, suggesting that the problem should be approached relying on no a priori knowledge regarding other flow structures. This paper discusses the restarts of bursts in a log-minimal channel within the framework of a linearised Navier-Stokes system with forcing terms encapsulating the nonlinear effects of all other structures. Two generic issues are addressed. The first concerns the conditions for burst-restart-like solutions for the forced linearised system itself. We formulate optimisation problems to understand the 'minimal requirements' for burst restarting. The solutions illustrate three conceptual periods in a typical restarting process, distinguished by the behaviour of spanwise vorticity structures: breakup, counter-rotating catch-up, and co-rotating catch-up. External forces promote this process by breaking up forward-inclined
vortices and merging co-rotating, catching-up
vortices. A quantity termed linearly available energy (LAE) is accordingly proposed to parameterise the restarting process. The second issue concerns the contributory features to the observed burst restarts in real
turbulence. We show that an essential role of nonlinearity in restarting a burst is to increase a decaying state's LAE to a level sufficient for the onset of the subsequent burst. Flow patterns extracted during the restarting stage exhibit breakup and merging effects, both facilitated by nonlinearity. This suggests that the two effects observed in both the linearised models and real
turbulence are manifestations of real flow structures that cause burst restarts.
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Chemical Physics · Cross-listed from quant-ph
Entanglement structure of the dynamical phases in the sub-Ohmic spin-boson model
Cunxi Gong, Zirui Sheng and Weitang Li
The sub-Ohmic
spin-
boson model exhibits three distinct dynamical regimes in its
spin population dynamics, classified as coherent, incoherent, and pseudo-coherent. Whether these regimes correspond to distinct
spin-bath
entanglement structures remains an open question. Here we address this using tree tensor network states with projector-splitting time
evolution (TTN-TDVP-PS), scanning a broad grid in the sub-Ohmic $(s, \alpha)$ plane. We find that the
spin entanglement entropy $S_\mathrm{spin}(t)$ reaches a stationary plateau on a timescale shorter than the polarization relaxation, enabling construction of a stationary
entropy landscape from the stationary value $S_\mathrm{stable}$. Within this scalar
entropy landscape, the
entropy ridge broadly follows the population-based phase boundary at small $s$, but does not reproduce the two-branch structure at large $s$. The ridge remains single-valued within the incoherent region rather than separately tracking both population-based transitions. The Bloch-sphere representation provides a
geometric interpretation of this behavior. The
entropy plateau corresponds to trajectories settling onto constant-radius shells, with the ridge marking the parameters of smallest stationary Bloch radius. Mode-resolved bath
entanglement shows that low-frequency modes dominate the environmental
entropy scale and that coherent dynamics enhance bath-mode correlations beyond direct
spin–mode correlations. These results establish the stationary
spin entanglement entropy as a physically informative observable that complements population-based classifications of dissipative
quantum dynamics.
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