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Physics Physics

physics Watch Physics

Thursday 25 December

Optics

On-chip quadratically nonlinear photodetector

Yu Zhang, Xiaoqing Chen, Mingwen Zhang et al. (+5 more)

Involving deterministically nonlinear photoresponse in on-chip photodetector is intriguing to develop sophisticated functions in photonic integrated circuits, such as in-sensor computing and optoelectronic mixing, though the corresponding devices are still lack of sufficient investigation. Here, we demonstrate an on-chip quadratically nonlinear photodetector (QNPD) by configuring an InSe p-i-n homojunction on a silicon waveguide. Telecom-band light guiding in the waveguide couples with the InSe evanescently and is frequency up-converted into visible light via InSe's second-harmonic generation (SHG), which is subsequently absorbed by InSe and finally generates photocurrent under the built-in electric field of the p-i-n homojunction. Governed by these sequential processes, the on-chip QNPD presents a quadratic function between photocurrent and optical power. Thanks to the efficient SHG and well-established homojunction in InSe, the QNPD reaches a high normalized responsivity of 37.1 A/W2 and low dark current of 1 pA, representing greatly improved performances among reported nonlinear photodetectors. Benefiting from the extra SHG process, the on-chip QNPD intrinsically incorporates light-light interactions, enabling straightforwardly monitoring all-optically mixing signals electrically. As an example, an array of 16-pixel QNPDs was designed to implement a fully single-shot on-chip autocorrelator without requirement of bulky optics and external cameras, which precisely measures picosecond pulses with high sensitivity of 6.1*10-10 W2.
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All authors Yu Zhang, Xiaoqing Chen, Mingwen Zhang, Yanyan Zhang, Chenyang Zhao, Liang Fang, Jianlin Zhao, Xuetao Gan

Computational Physics

GaDE – GPU-acceleration of time-dependent Dirac Equation for exascale

Johanne Elise Vembe, Marcin Krotkiewski, Magnar Bjørgve et al. (+2 more)

Modern heterogeneous high-performance computing (HPC) systems powered by advanced graphics processing unit (GPU) architectures enable accelerating computing with unprecedented performance and scalability. Here, we present a GPU-accelerated solver for the three-dimensional (3D) time-dependent Dirac equation optimized for distributed HPC systems. The solver named GaDE is designed to simulate the electron dynamics in atoms induced by electromagnetic fields in the relativistic regime. It combines MPI with CUDA/HIP to target both NVIDIA and AMD GPU architectures. We discuss our implementation strategies in which most of the computations are carried out on GPUs, taking advantage of the GPU-aware MPI feature to optimize communication performance. We evaluate GaDE on the pre-exascale supercomputer LUMI, powered by AMD MI250X GPUs and HPE's Slingshot interconnect. Single-GPU performance on NVIDIA A100, GH200, and AMD MI250X shows comparable performance between A100 and MI250X in compute and memory bandwidth, with GH200 delivering higher performance. Weak scaling on LUMI demonstrates exceptional scalability, achieving 85% parallel efficiency across 2048 GPUs, while strong scaling delivers a 16x speedup on 32 GPUs - 50% efficiency for a communication-intensive, time-dependent Dirac equation solver. These results demonstrate GaDE's high scalability, making it suitable for exascale systems and enabling predictive simulations for ultra-intense laser experiments probing relativistic quantum effects.
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All authors Johanne Elise Vembe, Marcin Krotkiewski, Magnar Bjørgve, Morten Førre, Hicham Agueny

Fluid Dynamics

Enhanced numerical models for two-component fluid flow in multiscale porous structures

J. Yang, H. Otomo, Hongli Fan et al. (+6 more)

Multi-component fluid flow simulations in multi-scale porous structures often involve regions that are under-resolved at practical computational resolutions. Accurately capturing the contributions from these unresolved regions is critical. Previous studies proposed a model to account for viscous and capillary forces in under-resolved regions, showing permeability, capillary pressure, and relative permeability comparable to fully resolved high-resolution cases. In this study, we extend the model to handle diverse structures and capture detailed fluid behavior. We introduce controllable surface tension in a pseudo-potential lattice Boltzmann model while keeping interface thickness and spurious currents constant, improving interface dynamics resolution. A method is developed to capture residual fluid components smaller than cell size using local constitutive relations, including absolute and relative permeability and capillary pressure curves. Additionally, a tensorial resistivity model is implemented for heterogeneous structures such as fiber bundles, aligning resistivity along principal axes determined from the Hessian and gradient of local porosity fields. Benchmark tests, including practical rock geometries, validate these enhancements, demonstrating improved transient interface dynamics, accurate capture of irreducible fluid components, and correct directional effects in under-resolved structures.
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All authors J. Yang, H. Otomo, Hongli Fan, Guangyuan Sun, Rafael Salazar-Tio, Ganapathi Raman Balasubramanian, Ashraful Islam, Bernd Crouse, Raoyang Zhang

Geophysics · Cross-listed from astro-ph.EP

Numerical simulation of lunar response to gravitational waves and its 3D topographic effect using the spectral-element method

Lei Zhang, Han Yan, Jinhai Zhang et al. (+1 more)

The Moon has been regarded as a natural Weber bar capable of amplifying gravitational waves (GWs) for detecting events across a wide range of frequencies. However, accurately determining the amplification effects remains challenging due to the absence of 3D numerical simulation methods. In this study, we develop a high-order 3D finite element method (spectral-element method, SEM) to numerically simulate the lunar response to GWs below 20 mHz. We verify the accuracy of our method by comparing the resonant peaks of our results with those from semi-analytical solutions and find that the frequency deviation is less than 3% for the first peak at about 1 mHz and less than 0.8% for the subsequent peaks up to 10 mHz. Using this method, we evaluate the amplification of GW signals due to 3D topographic effects of the Moon, and we find enhancements at a series of specific frequency components. These results highlight the non-negligible effect of surface topography on the lunar response to GWs, as a fundamental factor that holds significant implications across both global and regional analyses. Our work paves the way for a comprehensive evaluation of the Moon's resonant response to GWs, helpful for the strategic planning of lunar GW detections.
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All authors Lei Zhang, Han Yan, Jinhai Zhang, Xian Chen

Chemical Physics · Cross-listed from quant-ph

Spectroscopic Search for Topological Protection in Open Quantum Hardware: The Dissipative Mixed Hodge Module Approach

Prasoon Saurabh

Standard spectroscopic protocols model the dynamics of open quantum systems as a superposition of isolated, exponentially decaying eigenmodes. This paradigm fails fundamentally at Exceptional Points, where the eigenbasis collapses and the response becomes dominated by non-diagonalizable Jordan blocks. We resolve this ambiguity by introducing a geometric framework based on Dissipative Mixed Hodge Modules (DMHM). By replacing the scalar linewidth with a topological Weight Filtration, we derive “Weight Filtered Spectroscopy” (WFS)–a protocol that spatially separates decay channels based on the nilpotency rank of the Liouvillian. We demonstrate that WFS acts as a dissipative x-ray, quantifying dissipative leakage in molecular polaritons and certifying topological isolation in Non-Hermitian Aharonov-Bohm rings. This establishes that topological protection persists as an algebraic invariant even when the spectral gap is closed.
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Medical Physics · Cross-listed from eess.IV

Enabling Ultra-Fast Cardiovascular Imaging Across Heterogeneous Clinical Environments with A Generalist Foundation Model and Multimodal Database

Zi Wang, Mingkai Huang, Zhang Shi et al. (+61 more)

Multimodal cardiovascular magnetic resonance (CMR) imaging provides comprehensive and non-invasive insights into cardiovascular disease (CVD) diagnosis and underlying mechanisms. Despite decades of advancements, its widespread clinical adoption remains constrained by prolonged scan times, inconsistent image quality, and heterogeneity across medical environments. This underscores the urgent need for a generalist reconstruction foundation model for ultra-fast CMR imaging, one formulated for physics-constrained inverse problems in the sensor (k-space) domain, capable of adapting across diverse imaging scenarios and serving as the essential substrate for all downstream analyses. To enable this goal, we curate MMCMR-427K, the largest and most comprehensive multimodal CMR k-space database to date, comprising 427,465 multi-coil k-space data paired with structured metadata across 13 international centers, 12 CMR modalities, 15 scanners spanning four field strengths, and 17 CVD categories in populations across three continents. Building on this unprecedented resource, we introduce CardioMM, a generalist reconstruction foundation model capable of dynamically adapting to heterogeneous fast CMR imaging scenarios. CardioMM unifies semantic contextual understanding with physics-informed data consistency to deliver robust reconstructions across varied scanners, protocols, and patient presentations. Comprehensive evaluations demonstrate that CardioMM achieves state-of-the-art performance across internal centers and exhibits strong zero-shot generalization to unseen external settings. Importantly, CardioMM supports acceleration up to 24x, providing the first evidence that such extreme acquisition speed can preserve key cardiac phenotypes, quantitative myocardial biomarkers, and diagnostic image quality without compromising clinical integrity.
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All authors Zi Wang, Mingkai Huang, Zhang Shi, Hongjie Hu, Lan Lan, Hui Zhang, Yan Li, Xi Hu, Qing Lu, Zongming Zhu, Qiong Yao, Yuxiang Dai, Fanwen Wang, Yinzhe Wu, Jun Lyu, Qianqian Gao, Guangming Xu, Zhenxuan Zhang, Haosen Zhang, Qing Li, Guangming Wang, Tianxing He, Lizhen Lan, Siyue Li, Le Xue, Mengting Sun, Yuntong Lyu, Junpu Hu, Jiayu Zhu, Rizwan Ahmad, Zhengyu Bu, Xianling Qian, Guanke Cai, Ruiyu Cao, Weirui Cai, Chang Xu, Yuyang Ren, Feidan Yu, Siying Ma, Ziqiang Xu, Xinran Chen, Sha Hua, Daniel Kim, Yajing Zhang, Chen Ouyang, Wenjia Bai, Jing Qin, Yucheng Yang, Daniel Rueckert, He Wang, Qian Tao, Claudia Prieto, Michael Markl, Alistair Young, Lianming Wu, Shuo Wang, Chen Qin, Mengsu Zeng, Xihong Hu, Haibo Xu, Xiaobo Qu, Hao Li, Guang Yang, Chengyan Wang

Fluid Dynamics

Maximum droplet volume on cylindrical surfaces

Yi Zhang, Apurav Tambe and Zhao Pan

The maximum volume ($\Omega$) of a droplet that can remain attached to a horizontal fiber defines the stability limit of droplet-fiber interactions, phenomena common in nature and critical to diverse engineering applications. Existing predictive models for $\Omega$ show limitations in accurately capturing the dependence of $\Omega$ on fiber size and wettability. To address this gap, we systematically investigate $\Omega$ on a horizontal fiber through numerical simulations and experiments. A comprehensive semi-empirical model for $\Omega$ is developed and validated against both experimental measurements and reference simulations. This model establishes a new scaling under which the normalized maximum droplet volume depends solely on the contact angle and remains valid across a wide spectrum from a sub-millimeter thin fiber to the flat-surface limit, regardless of the diverse morphologies that droplets exhibit.
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Fluid Dynamics

LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow

Ze Tao, Ke Xu and Fujun Liu

Physics-Informed Neural Networks (PINNs) have demonstrated considerable success in solving complex fluid dynamics problems. However, their performance often deteriorates in regimes characterized by steep gradients, intricate boundary conditions, and stringent physical constraints, leading to convergence failures and numerical instabilities. To overcome these limitations, we propose a hybrid framework that integrates Long Short-Term Memory (LSTM) networks into the PINN architecture, enhancing its ability to capture spatial correlations in the steady-state velocity field of a two-dimensional charged fluid under an external electric field. Our results demonstrate that the LSTM-enhanced PINN model significantly outperforms conventional Multilayer Perceptron (MLP)-based PINNs in terms of convergence rate, numerical stability, and predictive accuracy. This innovative approach offers improved computational efficiency and reliability for modeling electrohydrodynamic flows, providing new insights and strategies for applications in microfluidics and nanofluidics.
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Optics

Quadratic Band Touching and Nontrivial Winding Reveal Generalized Angular Momentum Conservation

Yihan Wang, Domenico Bongiovanni, Dario Jukić et al. (+7 more)

Angular momentum conservation stands as one of the most fundamental and robust laws of physics. In discrete lattices, however, its realization can deviate markedly from the continuous case, especially in the presence of nontrivial momentum-space band touchings. Here, we investigate angular momentum conservation associated with quadratic band-touching points (QBTPs) in two-dimensional lattices. We show that, unlike in graphene lattices hosting linear band-touching points (LBTPs), the conventional angular momentum is no longer conserved near QBTPs. Instead, we identify a generalized total angular momentum (GTAM) that remains conserved for both LBTPs and QBTPs, inherently determined by the topological winding number at the band-touching point (BTP). Using a photonic Kagome lattice, we experimentally demonstrate GTAM conservation through pseudospin-orbital angular momentum conversion. Furthermore, we show that this conservation principle extends to a broad class of discrete lattices with arbitrary pseudospin textures and higher-order winding numbers. These results reveal a fundamental link between pseudospin, angular momentum, and topology, establishing a unified framework for angular-momentum dynamics in discrete systems.
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All authors Yihan Wang, Domenico Bongiovanni, Dario Jukić, Sihong Lei, Zhichan Hu, Daohong Song, Jingjun Xu, Roberto Morandotti, Hrvoje Buljan, Zhigang Chen

Accelerator Physics

Theoretical and experimental studies of energy modulation to demodulation in seeded free-electron lasers

Hanxiang Yang, Nanshun Huang, Zipeng Liu et al. (+9 more)

Laser manipulation plays a critical role in precisely tailoring relativistic electron beams through energy modulation, enabling the generation of coherent, intense, and ultrashort radiation in accelerator-based light sources such as synchrotron radiation facilities and free-electron lasers (FELs). However, laser-induced energy modulation inevitably degrades electron beam quality by increasing the energy spread, thereby limiting high-repetition-rate operation. Here, we investigate energy modulation and demodulation in a seeded FEL using two modulators separated by a tunable phase shifter. Analytical analysis and three-dimensional simulations show that a $\pi$ phase delay can nearly reverse the laser-beam interaction and substantially suppress the residual modulation. Diagnostics based on coherent undulator radiation and time-resolved measurements are established to characterize weak residual modulation, and a dedicated demodulation undulator is designed for controlled studies. Preliminary experiments performed at the Shanghai soft X-ray FEL facility using the existing seeding beamline demonstrate laser-induced energy-modulation suppression. Together with the analytical and numerical studies, these results establish a practical framework for investigating the transition from energy modulation to demodulation in seeded FELs, with potential applications in high-repetition-rate, fully coherent X-ray sources with improved preservation of electron beam quality.
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All authors Hanxiang Yang, Nanshun Huang, Zipeng Liu, Zhangfeng Gao, Shengbin Ye, Wencai Cheng, Shudong Zhou, Jinya Chen, Cheng Yu, Wei Zhang, Tao Liu, Haixiao Deng

Optics

Incorporating rank-free coupling and external field via an incoherent modulated spatial photonic Ising machine

Ze Zheng, Yuegang Li, Hang Xu et al. (+3 more)

Spatial photonic Ising machines offer a novel optical platform for optimization and spin-model simulation, but existing diffraction-based schemes rely on auxiliary spins or multiplexing to encode high-rank couplings and external fields, reducing either speed or spin count. We demonstrate an amplitude-only, rank-free spatial photonic Ising machine in which arbitrary Ising Hamiltonians are encoded as Hadamard products on aligned amplitude and binary spatial modulators and read out by a single-pixel intensity measurement. The machine directly programs fully connected 797-spin Ising models with external fields at nearly 9-bit precision and operates at a constant iteration rate of ~200 Hz. By removing zero-valued product terms, the same architecture scales to sparse problems and experimentally solves a Max-Cut instance on a 424,108-vertex Mobius ladder graph. We also observe the phase transition of the Sherrington-Kirkpatrick model, demonstrating programmable optical simulation beyond low-rank couplings. These results establish amplitude modulation as a scalable route to programmable photonic Ising machines.
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All authors Ze Zheng, Yuegang Li, Hang Xu, Jingzheng Huang, Tailong Xiao, Guihua Zeng

Optics

Broadband tunable microwave photonic radar for simultaneous detection of human respiration, heartbeat, and speech with deep learning-based speech recognition

Lei Gao, Dingding Liang, Jiawei Gao et al. (+4 more)

Multimodal vital sign monitoring and speech detection hold significant importance in medical health, public safety, and other fields. This study proposes a broadband tunable microwave photonic radar system that can simultaneously monitor respiration, heartbeat, and speech. The system works by generating broadband radar signals to detect subtle skin displacements caused by these physiological activities. It then utilizes phase variations in radar echo signals to extract and reconstruct the corresponding physiological signals. In order to enhance the processing capability for speech signals, a convolutional neural network with a dual-channel feature fusion model is incorporated, enabling high-precision speech recognition. In addition, the system's frequency-tunable characteristic allows it to flexibly switch frequency bands to adapt to different working environments, greatly improving its practicality and environmental adaptability. In concept-verification experiments, speech signals were reconstructed and recognized in the Ku, K, and Ka bands, achieving recognition accuracies of 97.20%, 98.07%, and 97.43%, respectively. The system's capability to detect multimodal vital signs was also thoroughly validated using a respiratory and heartbeat simulator. During a 20-second monitoring period, while accurately reconstructing speech, the maximum average error counts for respiratory and heartbeat monitoring were 0.39 and 0.87, respectively, proving its reliability and effectiveness in multimodal vital sign monitoring.
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All authors Lei Gao, Dingding Liang, Jiawei Gao, Chulun Lin, Zhiqiang Huang, Taixia Shi, Yang Chen

Biological Physics

Why orb-weaving spiders use leg crouching behavior in vibration sensing of prey on a web: A physical mechanism from robophysical modeling

Eugene H. Lin, Yishun Zhou, Hsin-Yi Hung et al. (+3 more)

Orb-weaving spiders primarily sense leg vibrations to detect and locate prey caught on their wheel-shaped webs. Biological experiments and computational modeling elucidated the physics of how these spiders use long-timescale web-building behaviors, which occur before prey capture, to modulate vibration sensing of prey by controlling web geometry, materials, and tension distribution. By contrast, the physics of how spiders use short-timescale leg behaviors to modulate vibration sensing on a web during prey capture is less known. This is in part due to challenges in biological experiments (e.g., having little control over spider behavior, difficulty measuring the whole spider-web-prey system vibrations) and theoretical/computation modeling (e.g., close-form equations intractable for a complex web, high computation cost for simulating vibrations with behaving animals). Here, we use robophysical modeling as a complementary approach to address these challenges and study how dynamic leg crouching behavior common in orb-weaving spiders contributes to vibration sensing of prey on a web. We created a robophysical model consisting of a spider robot that can dynamically crouch its legs and sense its leg vibrations and a prey robot that can shake both on a horizontal physical wheel-shaped web. We discovered a physical mechanism that can allow the spider to use leg crouching to detect prey presence and distance. Our work demonstrated that robophysical modeling is a useful approach for discovering physical mechanisms of how spiders use short-time scale leg behaviors to enhance vibration sensing of objects on a web and providing new biological hypotheses.
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All authors Eugene H. Lin, Yishun Zhou, Hsin-Yi Hung, Luke Moon, Andrew Gordus, Chen Li

Fluid Dynamics

Under pressure: poroelastic regulation of flow in espresso brewing

Radost Waszkiewicz, Franciszek Myck, Łukasz Białas et al. (+4 more)

The sensory richness of coffee is widely recognised and arises from the complex chemistry and immersion in cultural practices of coffee preparation. In contrast, the physical complexity of espresso has received less attention. The multiphase reactive flow through a dissolving, elastic porous medium remains challenging to describe. Using a controlled experimental setup based on a café-grade espresso machine, we demonstrate that the interplay between elasticity and porosity governs the long-time flow rate during espresso extraction and, consequently, the concentration of solubles in the final beverage. We introduce a minimal model that captures the resulting non-linear pressure-flow relationship and propose a methodology capable of reproducing the time-dependent behaviour of the espresso brewing process. Finally, we show that dissolution dynamics play a central role in determining the temporal evolution of flow during extraction.
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All authors Radost Waszkiewicz, Franciszek Myck, Łukasz Białas, Maria Puciata-Mroczynska, Michał Dzikowski, Piotr Szymczak, Maciej Lisicki

Chemical Physics

Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations

Bohan Zhang, Biyuan Liu, Penghua Ying et al. (+6 more)

Graphene oxide (GO) exhibits rich chemical heterogeneity that strongly influences its structural, thermal, and mechanical properties, yet quantitatively linking reduction chemistry to heat transport remains challenging. In this work, we develop a machine-learned neuroevolution potential (NEP) trained on an existing density functional theory dataset (Angew.\ Chem.\ Int.\ Ed., 63 , e202410088 (2024)), achieving reasonable accuracy at a computational cost much lower than the existing machine-learned and empirical potentials. Leveraging this potential, we perform large-scale molecular dynamics (MD) simulations to model the thermal reduction of GO across realistic structural domains. Using the homogeneous nonequilibrium MD method with a proper quantum-statistical correction scheme, we find that reduced GO exhibits strongly suppressed thermal conductivities, ranging from a few to tens of Wm$^{-1}$K$^{-1}$, substantially lower than pristine GO without defects and far below graphene. Moreover, the thermal conductivity of reduced GO increases moderately with increasing OH/O ratio, except at the highest oxidation level (O/C=0.5) where this trend inverts, while decreasing significantly with increasing O/C ratio, a trend strongly correlated with the fraction of recovered graphene-like structures. Our work provides a computationally tractable and predictive atomistic machine learning framework for exploring how chemical structure governs heat transport in heterogeneous carbon materials.
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All authors Bohan Zhang, Biyuan Liu, Penghua Ying, Zherui Chen, Yanzhou Wang, Yonglin Zhang, Haikuan Dong, Jinglei Yang, Zheyong Fan

Classical Physics

Asymptotically exact dimension reduction of functionally graded anisotropic rods

Khanh Chau Le

This study utilizes the variational-asymptotic method to establish a one-dimensional theory for functionally graded rods characterized by general anisotropy from the three-dimensional elasticity theory. A distinctive feature of this dimension reduction procedure is the numerical solution of dual cross-sectional problems, which provide rigorous upper and lower bounds for the average transverse energy density. By employing the Prager-Synge identity, we derive an error estimate in the energetic norm to establish the asymptotic exactness of the model. This estimate is extended to the dynamic regime for low-frequency vibrations. Furthermore, the dynamic validity of the theory is confirmed by comparing the one-dimensional dispersion relations with exact analytical three-dimensional solutions for wave propagation in composite rods. The results show that the developed one-dimensional model captures the long-wave asymptotic behavior of the three-dimensional elastic body with high fidelity. Numerical benchmarks indicate that while the naive rod theory incurs errors up to $20\%$ in deflection predictions, the current VAM framework reduces this discrepancy to below $3\%$, with log-log convergence studies confirming the theoretical $O(h/L)$ accuracy.
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Data Analysis and Statistics · Cross-listed from quant-ph

Quantum Nondecimated Wavelet Transform: Theory, Circuits, and Applications

Brani Vidakovic

The nondecimated or translation-invariant wavelet transform (NDWT) is a central tool in classical multiscale signal analysis, valued for its stability, redundancy, and shift invariance. This paper develops two complementary quantum formulations of the NDWT that embed these classical properties coherently into quantum computation. The first formulation is based on the epsilon-decimated interpretation of the NDWT and realizes all circularly shifted wavelet transforms simultaneously by promoting the shift index to a quantum register and applying controlled circular shifts followed by a wavelet analysis unitary. The resulting construction yields an explicit, fully unitary quantum representation of redundant wavelet coefficients and supports coherent postprocessing, including quantum shrinkage via ancilla-driven completely positive trace preserving maps. The second formulation is based on the Hadamard test and uses diagonal phase operators to probe scale-shift wavelet structure through interference, providing direct access to shift-invariant energy scalograms and multiscale spectra without explicit coefficient reconstruction. Together, these two approaches demonstrate that redundancy and translation invariance can be exploited rather than avoided in the quantum setting. Applications to denoising, feature extraction, and spectral scaling illustrate how quantum NDWTs provide a flexible and physically meaningful foundation for multiscale quantum signal processing.
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Fluid Dynamics

Topological guidance of a self-propelled particle

Ethan Andersson and Valeri Frumkin

Topological phenomena typically govern the behavior of delocalized waves, giving rise to robust transport in electronic, photonic, and mechanical systems. Whether similar principles can directly control the motion of a localized particle, particularly one dynamically coupled to the field that guides it, has remained largely unexplored. Here we show that topology can govern the dynamics of a self-guided particle. Using a walking droplet whose motion is coupled to a self-generated wave field, we demonstrate that structuring the wave environment enables band-gap mediated particle exclusion, edge-guided transport, and chirality-dependent orbital dynamics arising from an emergent gauge structure. Unlike conventional topological systems, where topology constrains wave propagation alone, the present system allows global geometric structure to act directly on particle trajectories. These results extend topological control from waves to particles and establish a route toward directing matter through global geometric design rather than local forcing.
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Instrumentation and Detectors · Cross-listed from hep-ph

Probing Internal Conversion and Dark-Matter-Induced De-excitation of 180mTa with a gamma-ray TES Array

A. Gando, K. Ichimura, K. Ishidoshiro et al. (+5 more)

We propose and evaluate a source-as-detector search for the de-excitation of the long-lived isomer $^{180\mathrm{m}}\mathrm{Ta}$ in natural tantalum (Ta), using a $\gamma$-ray transition-edge-sensor (TES) array. We exploit two capabilities not available in conventional high-purity germanium (HPGe) searches: (i) near-unity containment of low-energy secondaries (internal-conversion electrons and characteristic X rays), as well as the nuclear recoil, enabling a calorimetric, event-by-event measurement of the total energy deposited in the absorber; and (ii) a delayed-coincidence tag based on the subsequent ${}^{180}$Ta electron-capture (EC) decay to ${}^{180}$Hf. We evaluate the $3\sigma$ discovery reach for internal conversion (IC) and for dark-matter-induced de-excitation in two benchmark scenarios: a strongly interacting dark-matter (DM) subcomponent and inelastic DM with off-diagonal couplings. Using a background model based on intrinsic radioactivity in the Ta absorber and realistic detector performance, we show that arrays with $N_{\mathrm{TES}}=256$ and $1{,}000$ pixels can reach the theoretically expected IC half-life within $2.6$ yr and $0.66$ yr, respectively. For an array with $N_{\mathrm{TES}}=10^4$ and a five-year exposure, the projected sensitivity to DM-induced de-excitation surpasses limits inferred from HPGe non-observations of ${}^{180\mathrm{m}}$Ta and probes regions of parameter space not covered by current direct-detection experiments.
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All authors A. Gando, K. Ichimura, K. Ishidoshiro, T. Kikuchi, T. Kishimoto, A. Takeuchi, R. Sato, R. Smith

Wednesday 24 December

Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

Feedback Driven Convergence, Competition, and Entanglement in Classical Stochastic Processes

Allen Lobo and Saravanan A

We present a dynamical theory of statistical convergence in which the law of large numbers arises from outcome-outcome feedback rather than assumed independence. Defining the convergence field and its derivative, we show that empirical frequencies evolve through coupling, producing competition, finite-m fluctuations, and classical entanglement. Using the Kramers-Moyal expansion, we derive an Ito-Langevin and Fokker-Planck description, reducing in the symmetric regime to a time-dependent Ornstein-Uhlenbeck process. We propose variance-based witnesses that detect outcome-space entanglement in both binary sequences and coupled Brownian trajectories, and confirm entanglement through numerical experiments. Extending the formalism yields multi-outcome feedback dynamics and finite-time cross-diffusion between Brownian particles. The results unify convergence, fluctuation, and entanglement as consequences of a single feedback-driven stochastic principle.
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Geophysics

Rainfall-induced Mass Movement as Self-organization Process

Zhengjing Ma, Gang Mei, Nengxiong Xu et al. (+2 more)

Self-organizing processes shape Earth's surface, creating complex patterns from simple rules in most landforms. Rainfall-induced mass movements dramatically reshape landscapes through rapid sediment transfer, but whether they self-organize remains unknown. Here we decode their organizational principles by treating spatial changes in scar geometries as fingerprints of the movement process. In 65,936 scars worldwide, we discovered three geometric signals from width, sinuosity and curvature converge on shared patterns and identify a slow-to-fast hierarchy characteristic of self-organizing landforms: long-range correlations show width retaining spatial memory while curvature decorrelates quickly; power spectra quantify a 4-3-2 hierarchy (width-sinuosity-curvature) in scaling exponents; and information flow confirms a top-down organization (width-sinuosity-curvature). Although entropy increases toward finer scales, phase-space reconstructions settle on low-dimensional attractors, revealing hidden order. Together, the evidence shows that width establishes flow corridors through slow dynamics, sinuosity mediates momentum and gravity by intermediate adjustments, and curvature responds rapidly to the terrain. We also developed a model based on simple terrain-inertia trade-offs, demonstrating how mass movements maintain large-scale coherence while flexibly navigating obstacles, potentially extending run-out distances. This organizing rule offers a fundamental mechanism for predicting the destructive reach of mass movements, which are intensifying in our warming, wetter world.
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All authors Zhengjing Ma, Gang Mei, Nengxiong Xu, Yongshuang Zhang, Jianbing Peng

Medical Physics · Cross-listed from q-bio.QM

Measuring the time-scale-dependent information flow between maternal and fetal heartbeats during the third trimester

Nicolas B. Garnier, Maria S. Molinet, Marta C. Antonelli et al. (+2 more)

Prenatal maternal stress alters maternal-fetal heart rate coupling, as demonstrated by the Fetal Stress Index derived from bivariate phase-rectified signal averaging. Here, we extend this framework using information-theoretical measures to elucidate underlying mechanisms. In 120 third-trimester pregnancies (58 stressed, 62 control), we computed transfer entropy (TE), entropy rate (ER), and sample entropy (SE) under multiple conditioning paradigms, employing mixed linear models for repeated measures. We identify dual coupling mechanisms at the short-term (0.5 - 2.5 s), but not long-term (2.5 - 5 s) time scales: (1) stress-invariant state-dependent synchronization, with maternal decelerations exerting approximately 60% coupling strength on fetal heart rate complexity - a fundamental coordination conserved across demographics; and (2) stress-sensitive temporal information transfer (TE), showing exploratory associations with maternal cortisol that require replication. A robust sex-by-stress interaction emerged in TE from mixed models, with exploratory female-specific coupling patterns absent in males. Universal acceleration predominance was observed in both maternal and fetal heart rates, stronger in fetuses and independent of sex or stress. We provide insight into the dependence of these findings on the sampling rate of the underlying data, identifying 4 Hz, commonly used for ultrasound-derived fetal heart rate recordings, as the necessary and sufficient sampling rate regime to capture the information flow. Information-theoretical analysis reveals that maternal-fetal coupling operates through complementary pathways with differential stress sensitivity, extending the Fetal Stress Index by elucidating causal foundations. Future studies should explore additional information-theoretical conditional approaches to resolve stress-specific and time-scale-specific differences in information flow.
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All authors Nicolas B. Garnier, Maria S. Molinet, Marta C. Antonelli, Silvia M. Lobmaier, Martin G. Frasch

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

Toward reducing the formation temperature of diopside via wet-chemical synthesis routes using chloride precursors

N. Namvar, E. Salahinejad, A. Saberi et al. (+3 more)

Reducing the formation temperature of single-phase multioxides is one of the classic challenges in ceramic processing, including wet-chemical synthesis routes. Toward pursuing this aim for diopside (MgCaSi2O6), the merit of different sol-gel and coprecipitation processes using the related chloride precursors followed by calcination was compared from the viewpoints of crystallinity and homogeneity. In accordance to the results, the use of the sol-gel techniques, directed with/without an alkaline catalyst, gave rise to the unfavorable creation of multiphase and low-crystallinity structures. Regarding the coprecipitation methods, the one-step addition of a precipitant agent is accompanied by an indirect low-temperature formation of nano-diopside, while a direct crystallization into this phase was explored in the dropwise condition, albeit with a lower crystallinity. Thus, by employing a suitable synthesis processing, it is feasible to take control of a wide range of nanoparticulate diopside-based structures achieved after a low-temperature calcination.
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All authors N. Namvar, E. Salahinejad, A. Saberi, M.J. Baghjeghaz, L. Tayebi, D. Vashaee

Atomic Physics

Atomic clock frequency ratios with fractional uncertainty $\leq 3.2 \times 10^{-18}$

Alexander Aeppli, Willa J. Arthur-Dworschack, Kyle Beloy et al. (+24 more)

We report high-precision frequency ratio measurements between optical atomic clocks based on $^{27}$Al$^+$, $^{171}$Yb, and $^{87}$Sr. With total fractional uncertainties at or below $3.2 \times 10^{-18}$, these measurements meet an important milestone criterion for redefinition of the second in the International System of Units. Discrepancies in $^{87}$Sr ratios at approximately $1\times10^{-16}$ and the Al$^+$/Yb ratio at $1.6\times10^{-17}$ in fractional units compared to our previous measurements underscore the importance of repeated, high-precision comparisons by different laboratories. A key innovation in this work is the use of a common ultrastable reference delivered to all clocks via a 3.6 km phase-stabilized fiber link between two institutions. Derived from a cryogenic single-crystal silicon cavity, this reference improves comparison stability by a factor of 2 to 3 over previous systems, with an optical lattice clock ratio achieving a fractional instability of $1.3 \times 10^{-16}$ at 1 second. By enabling faster comparisons, this stability will improve sensitivity to non-white noise processes and other underlying limits of state-of-the-art optical frequency standards.
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All authors Alexander Aeppli, Willa J. Arthur-Dworschack, Kyle Beloy, Caitlin M. Berry, Tobias Bothwell, Angela Folz, Tara M. Fortier, Tanner Grogan, Youssef S. Hassan, Zoey Z. Hu, David B. Hume, Benjamin D. Hunt, Kyungtae Kim, Amanda Koepke, Dahyeon Lee, David R. Leibrandt, Ben Lewis, Andrew D. Ludlow, Mason C. Marshall, Nicholas V. Nardelli, Harikesh Ranganath, Daniel A. Rodriguez Castillo, Jeffrey A. Sherman, Jacob L. Siegel, Suzanne Thornton, William Warfield, Jun Ye

Medical Physics

Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases

Gyeo-Re Han, Merve Eryilmaz, Artem Goncharov et al. (+12 more)

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 uL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care.
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All authors Gyeo-Re Han, Merve Eryilmaz, Artem Goncharov, Yuzhu Li, Shun Ye, Aoi Tomoeda, Emily Ngo, Margherita Scussat, Xiao Wang, Zixiang Ji, Max Zhang, Jeffrey J. Hsu, Omai B. Garner, Dino Di Carlo, Aydogan Ozcan

Computational Physics · Cross-listed from astro-ph.GA

A Velocity Coupled Radial Acceleration Ansatz for Disk-Galaxy Rotation Curves: Fits to SPARC, Bayesian Inference, and Parameter Identifiability

Nalin Dhiman

Observed rotation curves of disk galaxies remain a sharp empirical probe of the relationship between baryons and dynamics. We study a minimal, explicitly phenomenological alternative to standard halo parameterizations: an additional inward radial acceleration proportional to the local tangential speed, $a_{\vca}(r)=\gamma(r)\,v(r)$, with a saturating coupling $\gamma(r)=\vinf/(r+\rzero)$. Combining this ansatz with the circular-motion condition yields a quadratic equation for $v(r)$ with a closed-form physical branch.We fit this “velocity-coupled acceleration” (\vca) model to $N_\mathrm{gal}=171$ rotation curves from the SPARC sample using the published baryonic decompositions (gas, disk, bulge), and we compare to two commonly used two-parameter halo models (NFW and Burkert) using an identical optimization pipeline and error model.For a fiducial systematic error floor $\sigma_0=5~\mathrm{km\,s^{-1}}$, the \vca model is typically competitive with an NFW halo and performs comparably (though not uniformly better) than a Burkert halo in information-criterion comparisons.We further perform MCMC inference for \vca parameters, quantify posterior predictive coverage, and show that parameter posteriors exhibit a strong $\vinf$–$\rzero$ degeneracy for many galaxies; only $47/171$ galaxies yield well-identified posteriors under simple width-based criteria. We also perform a simple radial holdout cross-validation (outer 30% of radii) and find predictive RMSE comparable to NFW and Burkert under this protocol.Finally..
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Applied Physics · Cross-listed from cond-mat.mtrl-sci

Structure, biomineralization and biodegradation of Ca-Mg oxyfluorosilicates synthesized by inorganic salt coprecipitation

E. Salahinejad and M. Jafari Baghjeghaz

In this research, a novel group of Ca-Mg oxyfluorosilicates containing different levels of fluoride substituting for oxide was synthesized by an inorganic salt coprecipitation process followed by calcination/sintering. The effects of the incorporation of fluoride on the resultant structural characteristics, apatite-forming ability and biodegradability were evaluated by X-ray diffraction, transmission electron microscopy, scanning electron microscopy/energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, inductively coupled plasma spectroscopy and pH measurements. According to the results, the samples containing up to 2 mol% F present a single-phase structure of diopside (MgCaSi2O6) doped with F. It was also found that to meet the most biomineralization characteristic, the optimal value of fluoride in the homogeneous samples is 1 mol%. In this regard, on the one hand, the partial incorporation of fluoride into apatite (via forming fluorohydroxyapatite) and, on the other hand, the absence of fluorite (CaF2) as a consumer of Ca in the deposits are responsible for achieving the most apatite-forming ability circumstance controlled by an ion-exchange reaction mechanism. In conclusion, this study reflects the merit of the optimization of fluoride-doping into Ca-Mg silicates for development in biomedicine.
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Medical Physics

Autonomous Uncertainty Quantification for Computational Point-of-care Sensors

Artem Goncharov, Rajesh Ghosh, Hyou-Arm Joung et al. (+2 more)

Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neural network-based diagnostic models are subject to hallucinations and can produce erroneous predictions, posing a risk of misdiagnosis and inaccurate clinical decisions. To address this challenge, here we present an autonomous uncertainty quantification technique developed for POC diagnostics. As our testbed, we used a paper-based, computational vertical flow assay (xVFA) platform developed for rapid POC diagnosis of Lyme disease, the most prevalent tick-borne disease globally. The xVFA platform integrates a disposable paper-based assay, a handheld optical reader and a neural network-based inference algorithm, providing rapid and cost-effective Lyme disease diagnostics in under 20 min using only 20 uL of patient serum. By incorporating a Monte Carlo dropout (MCDO)-based uncertainty quantification approach into the diagnostics pipeline, we identified and excluded erroneous predictions with high uncertainty, significantly improving the sensitivity and reliability of the xVFA in an autonomous manner, without access to the ground truth diagnostic information of patients. Blinded testing using new patient samples demonstrated an increase in diagnostic sensitivity from 88.2% to 95.7%, indicating the effectiveness of MCDO-based uncertainty quantification in enhancing the robustness of neural network-driven computational POC sensing systems.
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All authors Artem Goncharov, Rajesh Ghosh, Hyou-Arm Joung, Dino Di Carlo, Aydogan Ozcan

Fluid Dynamics

Integral modelling of weakly evaporating 3D liquid film with variable substrate heating

Fabio Pino

Analysing the dynamics of phase-changing liquid films is essential for enhancing the performance of thermal management systems. Still, direct simulation of the full governing equations is computationally expensive. To circumvent this limitation, I derived a weighted-integral boundary-layer (WIBL) model under long-wave assumptions, weak evaporation, and strong surface tension, also accounting for variable substrate heating. In the linear regime, the WIBL reproduces growth rates and the cutoff wavenumber of unstable modes with significantly higher accuracy than commonly used Benney-type models for Re<40, as compared to the Orr-Sommerfeld equations. The linear analysis further reveals a threshold separating streamwise- and spanwise-dominated instabilities in hanging films, arising from the competition between Kapitza and Rayleigh-Taylor mechanisms; the WIBL predicts this threshold accurately for small Re and inclination angles. In the nonlinear regime, with substrate heating that varies in both space and time, the WIBL model captures the evolution of free-surface thickness and temperature within approximately 6% of the original Navier-Stokes equations. Three-dimensional simulations show that a condensing film undergoes dry-out due to Kapitza instability, whereas unsteady substrate heating promotes spanwise momentum spreading, modifies wave dynamics, and prevents dry-out. The WIBL model provides a good level of accuracy at a low computational cost, enabling extensive parametric studies, nonlinear stability analyses, and the design of optimal substrate-heating control strategies.
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Plasma Physics

Impurity peaking of SPARC H-modes: a sensitivity study on physics and engineering assumptions

Marco Muraca, Pablo Rodriguez-Fernandez, Joe Hall et al. (+5 more)

In this paper, an overview of the impurity transport for three H-mode plasmas in the upcoming SPARC tokamak has been provided. The simulations have been performed within the ASTRA+STRAHL framework, using FACIT and TGLF-SAT2 to predict, respectively, neoclassical and turbulent core transport, while a neural network trained on EPED simulations has been employed to calculate the pedestal height and width self-consistently. A benchmark with previous simulations at constant impurity fraction has been provided for three H-modes, spanning different plasma current and magnetic field values. For a scenario, additional simulations have been performed to account for uncertainties in the modeling assumptions. The predictions are nearly insensitive to changes in the top of pedestal W concentrations. Varying the Ar pedestal concentration has shown a small effect on the impurity peaking and nearly constant fusion gain values, due to multiple effects on pedestal pressure, main ion dilution and density peaking. The inclusion of rotation in ASTRA simulations has shown minimal impact on confinement and impurity transport predictions. An exploratory study has been provided with a first set of simulations treating D and T separately, experiencing a maximum fusion power at 55-45% DT fuel composition, and an asymmetric distribution with respect to the D concentration. All the results, including sensitivity scans of toroidal velocity and ion temperature and density gradients, highlighted that turbulent impurity transport prevails on the neoclassical component, aligning with previous ITER predictions, and suggesting that next generation devices like SPARC, operating at low collisionality, will experience low W accumulation.
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All authors Marco Muraca, Pablo Rodriguez-Fernandez, Joe Hall, Nathaniel T. Howard, Daniel Fajardo, Giovanni Tardini, Benedikt Zimmermann, Thomas Body