Optics
Integration of 2D Materials in Radial van der Waals Heterostructure Metasurfaces
Connor Heimig, Jonas Biechteler, Cristina Cruciano et al. (+12 more)
Two-dimensional
semiconductors, such as monolayer transition metal dichalcogenides (TMDC), exhibit strong excitonic transitions at room temperature and offer a unique platform for exploring light-matter interactions in nanoscale photonic systems. In this work, we demonstrate a compact and polarization-invariant photonic metasurface, fabricated from hexagonal boron-nitride (hBN) and based on radial bound states in the continuum (BIC), which are formed by radially distributed pairs of structurally asymmetric resonators. The metasurface employs multiple
symmetry-breaking perturbations to support high quality-(Q-)factor resonances within a footprint smaller than 8 x 8 $\mu m^2$ - one-sixth of the area of previous approaches. Compared to established hBN metasurface designs, the radial geometry furthermore achieves significantly higher Q-factors with a reduced footprint. By integrating the hBN photonic structure with a WS$_2$ monolayer, we observe enhanced photoluminescence when its resonance is spectrally aligned with the
exciton resonance, accompanied by signatures of discrete momentum-space patterns that identify the orbital-angular-momentum-carrying ring eigenmodes. These features persist over a wide range of excitation powers and show minimal linewidth broadening, indicating robust and spatially modulated
exciton-
photon coupling. This work establishes a scalable approach for generating hybrid photonic-excitonic states with momentum-space structure, offering new opportunities for
exciton localization, valley emission, spatially programmable light-matter interaction in two-dimensional material platforms and compact luminescent devices based on 2D material-integrated metasurfaces.
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All authors Connor Heimig, Jonas Biechteler, Cristina Cruciano, Armando Genco, Thomas Weber, Michael Hirler, Dmytro Gryb, Alexander A. Antonov, Leonardo de S. Menezes, Gianluca Valentini, Cristian Manzoni, Giulio Cerullo, Stefan A. Maier, Luca Sortino, Andreas Tittl
Fluid Dynamics · Cross-listed from cond-mat.soft
Simulations of inertial liquid-lens coalescence with the pseudopotential lattice Boltzmann method
Qingguang Xie and Jens Harting
The coalescence of
liquid lenses is relevant in various applications, including inkjet printing and fog harvesting. However, the dynamics of
liquid-lens coalescence have been relatively underexplored, particularly in the case of
liquid lenses with larger contact angles. We numerically investigate the coalescence of low-viscosity
liquid lenses by means of the pseudopotential multi-component
lattice Boltzmann method over a wide range of contact angles. In two-dimensional simulations, our numerical results on the growth of the bridge height are in quantitative agreement with experimental measurements for small contact angles. In addition, by comparing our simulation results with a theoretical approach based on the thin-sheet equations for
liquid lenses, we find that the thin-sheet equations accurately capture the bridge-growth dynamics up to contact angles of approximately $\theta < 40^{\circ}$. For the three-dimensional case, the growth of the bridge radius is independent of the
equilibrium contact angle of the
liquid lenses at the initial stage of growth. The dependency between the growth of the bridge height and the bridge radius exhibits a non-linear to linear transition.
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Atomic Physics · Cross-listed from quant-ph
Superradiant decay in non-Markovian Waveguide Quantum Electrodynamics
Rosa Lucia Capurso, Giuseppe Calajó, Simone Montangero et al. (+5 more)
An array of initially excited emitters coupled to a one-dimensional waveguide exhibits superradiant decay under the Born-Markov approximation, manifested as a coherent burst of
photons in the output field. In this work, we employ tensor-network methods to investigate its non-Markovian dynamics induced by finite time delays in
photon exchange among the emitters. We find that the superradiant burst breaks into a structured train of correlated
photons, each intensity peak corresponding to a specific
photon number. We quantify the emitter-
photon and emitter-emitter
entanglement generated during this process and show that the latter emerges in the long-time limit, as part of the excitation becomes trapped within the emitters' singlet subspace. We finally consider the decay of the system's most radiant state, the symmetric Dicke state, and show that time delay can lead to decay rates exceeding those predicted by the Markovian approximation.
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All authors Rosa Lucia Capurso, Giuseppe Calajó, Simone Montangero, Saverio Pascazio, Francesco V. Pepe, Maria Maffei, Giuseppe Magnifico, Paolo Facchi
Chemical Physics · Cross-listed from quant-ph
Excited state preparation on a quantum computer through adiabatic light-matter coupling
Hugh G. A. Burton and Maria-Andreea Filip
Quantum computing has the potential to transform simulations of
quantum many-body problems at the heart of electronic structure theory. Efficient
quantum algorithms to compute the eigenstates of fermionic
Hamiltonians, such as
quantum phase estimation, rely critically on high-accuracy initial state preparation. While several state preparation
algorithms have been proposed for fermionic ground states, the preparation of excited states remains a major challenge, limiting the applicability of
quantum algorithms to photochemistry and photophysics. In this contribution, we describe a physically motivated adiabatic state preparation technique for low-lying bright excited states using the explicit coupling between electrons and
photons. Our approach systematically converges to the first bright excited state and can target different
symmetry sectors by changing the
photon polarization. We demonstrate the preparation of high-fidelity excited states for the Hubbard model and methylene molecule across a range of correlation regimes, and perform a successful hardware implementation for a model
Hamiltonian.
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Instrumentation and Detectors
Timing resolution from beam tests on thin LGADs down to 16.6 ps
Robert Stephen White, Marco Ferrero, Valentina Sola et al. (+14 more)
The paper reports on the timing resolution achieved with Low-Gain Avalanche Diodes (LGADs), optimised for extreme-fluence conditions, at the DESY Test Beam Facility using 4~GeV/c electrons. The LGADs adopt an $n$-in-$p$ technology with a $p^{+}$-type boron gain implant, co-implanted with carbon to mitigate acceptor deactivation due to irradiation. The substrate thickness of the sensors varies from 20 to 45~\micron, with an active area spanning from 0.75~$\times$~0.75 to 1.28~$\times$~1.28~mm$^{2}$. The experimental setup consisted of a 45~\micron-thick trigger sensor with an active area of 3.6~$\times$~3.6~mm$^{2}$, two device-under-test (DUT) planes, and a Photonis micro-channel plate photomultiplier tube (MCP) as a time reference. Data taking was performed at the ambient temperature of the facility, at 18$^{\circ}$C. The gain was measured between 7 and 40 across all non-irradiated sensors in the study. The timing resolution was calculated from a Gaussian fitting of the difference in times of arrival of a particle at the DUT and the MCP, using the constant fraction discriminator technique. A timing resolution of 26.4~\ps was achieved in 45~\micron-thick sensors, down to 16.6~\ps in 20~\micron-thick sensors, in the non-irradiation study. The combination of two 20~\micron-thick LGADs reached a timing resolution of 12.2~\ps. A set of 30~\micron-thick sensors irradiated with
neutrons at fluences between 0.4~$\times$~10$^{15}$ and 2.5~$\times$~10$^{15}$~\mevneut were tested in the beam. These irradiated sensors achieved a gain between 7 and 30 using a similar apparatus but cooled with solidified CO$_{2}$ to -42$^{\circ}$C. A timing resolution of 20~\ps was obtained in these irradiated sensors.
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All authors Robert Stephen White, Marco Ferrero, Valentina Sola, Anna Rita Altamura, Roberta Arcidiacono, Maurizio Boscardin, Nicolo Cartglia, Matteo Centis Vignali, Tommaso Croci, Matteo Durando, Simone Galletto, Alessandro Fondacci, Leonardo Lanteri, Luca Menzio, Francesco Moscatelli, Giovanni Paternoster, Federico Siviero
Atomic Physics
Relativistic recoil as a key to the fine-structure puzzle in muonic $^{90}\text{Zr}$
Konstantin A. Beyer, Igor A. Valuev, Zoia A. Mandrykina et al. (+2 more)
The long-standing fine-structure anomaly in muonic $^{90}$Zr is resolved through a rigorous treatment of the relativistic-
recoil effect. From a fit of ab initio QED calculations of the muonic $^{90}$Zr spectrum to precision measurements performed four decades ago, we extract a significantly more precise root-mean-square (rms) charge radius with 6-fold improvement in quality of the fit. A 2-parameter
Fermi (2pF) distribution is assumed to model the
nuclear charge density and yields a best-fit value of rms charge radius of $r_\text{rms}[^{90}\text{Zr}]=4.2732(7)$ fm ($\chi^2 /{\text{DoF}} = 0.995$), in agreement with the previous muonic spectroscopy value, but a factor $6$ more precise, and 3$\sigma$ larger than the accepted literature value. Additionally, the same analysis has been performed for $^{120}$Sn, where the extracted value of $r_\text{rms}[^{120}\text{Sn}]=4.6518(34)$ fm ($\chi^2 /{\text{DoF}} = 0.88$) is consistent with the accepted value. These results confirm our assumption that the muonic fine-structure puzzle arose from an incomplete treatment of QED effects and their uncertainties.
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All authors Konstantin A. Beyer, Igor A. Valuev, Zoia A. Mandrykina, Zewen Sun, Natalia S. Oreshkina
Applied Physics
Multi-Objective Tweezers in Scattering Media
Tristan Nerson, Jakob Hüpfl, Clément Ferise et al. (+5 more)
Radiation forces and torques enable the manipulation of objects with acoustic and electromagnetic waves. Yet, harnessing them in complex scattering media remains a formidable challenge, especially when multiple objects must be controlled under competing objectives. Here, we demonstrate that sound or light can be shaped to tailor momentum transfer to multiple objects simultaneously in a complex scattering medium. For a single object, our theory yields the maximal achievable force or torque; for multiple objects, it produces Pareto-optimal actuation and exact bounds on the simultaneous realization of incompatible objectives. This opens new applications for wave tweezers, enabling selective and precise manipulation of objects within complex media, ranging from the handling of
cells, organoids, or microrobots, to targeted drug delivery in biological media.
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All authors Tristan Nerson, Jakob Hüpfl, Clément Ferise, David Globosits, Marlene Hudler, Matthieu Malléjac, Stefan Rotter, Romain Fleury
Instrumentation and Detectors · Cross-listed from hep-ex
An interpretable unsupervised representation learning for high precision measurement in particle physics
Xing-Jian Lv, De-Xing Miao, Zi-Jun Xu et al. (+1 more)
Unsupervised learning has been widely applied to various tasks in particle physics. However, existing models lack precise control over their learned representations, limiting physical interpretability and hindering their use for accurate measurements. We propose the Histogram AutoEncoder (HistoAE), an unsupervised representation learning network featuring a custom histogram-based loss that enforces a physically structured latent space. Applied to silicon microstrip detectors, HistoAE learns an interpretable two-dimensional latent space corresponding to the particle's charge and impact position. After simple post-processing, it achieves a charge resolution of $0.25\,e$ and a position resolution of $3\,\mu\mathrm{m}$ on beam-test data, comparable to the conventional approach. These results demonstrate that unsupervised deep learning models can enable physically meaningful and quantitatively precise measurements. Moreover, the generative capacity of HistoAE enables straightforward extensions to fast detector simulations.
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All authors Xing-Jian Lv, De-Xing Miao, Zi-Jun Xu, Jian-Chun Wang
Physics and Society
Edge-based reputation promotes cooperation in simplicial complexes
Chunpeng Du, Fei Fang, Alfonso de Miguel-Arribas et al. (+4 more)
Understanding how cooperation emerges and persists is a central challenge in the evolutionary dynamics of social and biological systems. Most prior studies have examined cooperation through pairwise interactions, yet real-world interactions often involve groups and higher-order structures. Reputation is a key mechanism for guiding strategic behavior in such contexts, but its role in higher-order networks remains underexplored. In this study, we introduce an edge-based reputation mechanism, incorporating both direct and indirect reputation, to investigate the
evolution of cooperation in simplicial complexes. Our results show that coupling reputation mechanisms with higher-order network structures strongly promotes cooperation, with direct reputation exerting a stronger influence than indirect reputation. Moreover, we reveal a nonlinear interplay between network
topology and reputation mechanisms, highlighting how multi-level structures shape collective outcomes. These findings provide a novel theoretical framework for understanding cooperation in complex social systems.
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All authors Chunpeng Du, Fei Fang, Alfonso de Miguel-Arribas, Yikang Lu, Yanan Wang, Xin Pan, Yamir Moreno
Optics
Photoinduced giant modulation of terahertz nonlinearity from metasurfaces
Chen Wang, Yong Tan, Yongzheng Wen et al. (+5 more)
Active control of optical nonlinearity is essential for advancing next-generation electronics and photonics, including high-speed wireless communications, optical information processing, and nonlinear signal manipulation. However, achieving tunable nonlinearity at terahertz (THz) frequencies faces significant challenges due to the lack of materials that combine high nonlinear responses with strong sensitivity to external stimuli in this spectral regime. Here, we show
giant modulation of THz nonlinearity by optically tailoring the valley degree of freedom in
semiconductor-based metasurfaces. Mediated by the resonant behaviors of metasurfaces, photoexcited electrons transition into different valleys in the conduction band in response to the driving THz field, with the transition rate controlled by light intensity. Since THz nonlinearities vary significantly with electron dynamics in different valleys, various nonlinear effects-such as nonlinear transmission and generation-can be efficiently enhanced and modulated within a single metasurface using weak optical pumping. With optical energy as low as a few picojoules, we achieve on-off switching of THz third harmonic generation with a modulation depth exceeding 20000%, along with effective tunability of its nonperturbative behaviors. Our approach breaks new ground in active THz devices fully compatible with
semiconductor industry standards, indicating a promising building block for ultrafast THz signal processing, all-optical computing, and nonlinear optical elements.
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All authors Chen Wang, Yong Tan, Yongzheng Wen, Shiqiang Zhao, Kaixin Yu, Renfei Zhang, Jingbo Sun, Ji Zhou
Computational Physics · Cross-listed from quant-ph
Optimal Control for Rydberg multi-qubit operations
Hossein Abedi, Mohammadsadegh Khazali and Klaus Mølmer
Quantum computing
algorithms can be decomposed into a universal set of elementary one- and two-
qubit gates. Different physical implementations of
quantum computing, however, employ interactions that permit direct conditional dynamics on multiple
qubits in a single step. In this work, we leverage
quantum optimal control techniques to design single continuous
laser pulses that implement multi-
qubit controlled-phase, -NOT and -swap (Fredkin) gates on Rydberg atom
quantum processors. The identification of robust multi-
qubit operations leads to reduced operation time and less decoherence, and the control field provides continuous protection of the atoms from environmental noise. Notably, we find that the controlled-swap (Fredkin) gate, implemented using this approach achieves 99.74% fidelity while accounting for imperfections such as spontaneous emission,
laser fluctuations, and Doppler dephasing.
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Chemical Physics · Cross-listed from cond-mat.soft
Entangled-to-packed crossover in nonlinear extensional rheology of entangled polymers
Yin Wang, Lin-Feng Wu, Yi-Bo Shao et al. (+1 more)
Significant challenges exist in the nonlinear extensional rheology of entangled
polymers. With simulations, we show that the key to understanding this problem is to recognize the existence and importance of a strain-induced crossover from the entangled state to a packed state. This crossover, following the saturation of primitive chain stretch, takes place with massive release of entanglements via convective flow and progressive chain
alignment. After the crossover, the disentangled, fully-aligned chain segments pack similarly to the random packing of rods. Meanwhile, the system enters the steady state. In this state, the tube model, built on the concept of
entanglement, fails, while the stress can be quantitatively calculated by combining the intra-chain conformational contribution and a frictional contribution from the inter-chain separation along flow.
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All authors Yin Wang, Lin-Feng Wu, Yi-Bo Shao, Zhe Wang
Chemical Physics
Two-dimensional fluorescence spectroscopy with quantum entangled photons: Idler-referenced timing without pump detection
Yuta Fujihashi and Akihito Ishizaki
Entangled
photons have attracted increasing interest as resources for developing time-resolved spectroscopic techniques. Theoretical studies suggest that their non-classical correlations enable time-resolved spectroscopy with monochromatic pumping and can selectively isolate specific Liouville pathways in nonlinear optical signals. In an earlier study, we proposed a fluorescence detection scheme that could, in principle, be implemented using existing single-
photon detectors [Y. Fujihashi et al., Sci. Adv. 12, eaed7026 (2026)]. In that design, the time origin was defined by detecting the arrival of the pulsed
laser used to pump the nonlinear
crystal for spontaneous parametric down-conversion, a requirement that made the overall experiment cumbersome. This study theoretically examines an alternative protocol that defines the reference time based on the arrival of idler
photons. We demonstrate that this idler-referenced scheme functions effectively when the entangled
photons exhibit either negative or negligible frequency correlations. Eliminating the pump-timing channel simplifies the optical layout and lowers the experimental barrier to realizing time-resolved two-dimensional fluorescence spectroscopy with entangled
photons. Although the
photons may exhibit frequency correlations in isolation, their frequency-time degrees of freedom can behave as effectively uncorrelated when considered over the full measurement timescale. Therefore, fully exploiting non-classical correlations requires an entangled
photon source whose temporal characteristics are carefully matched to the overall timescale of the experiment.
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Optics
2D MoS$_2$/Au interfaces for enhanced opto-electronic response with sub-bandgap photons
Jinlei Wu, Wei Huang, Ahsan Ali et al. (+5 more)
Monolayer MoS$_2$ is a direct band gap
semiconductor with potential applications in optoelectronics and photonics. MoS$_2$ also has a large optical nonlinearity. However, the atomic thickness of the monolayer limits the strength of the measured functional signals, such as the photocurrent or photoluminescence, in optoelectronic devices. Here, we show that photocurrent in monolayer MoS$_2$ can be induced by sub-band gap
photons by depositing Au nanoparticles on it. In this system, the nonlinear light-matter interaction in Au nanoparticles enhanced by the localized surface plasmons results in the generation of supercontinuum, which is reabsorbed by MoS$_2$ due to efficient resonant energy transfer. Au nanoparticle assisted photocurrent is more than an order of magnitude larger than two-
photon photocurrent in monolayer MoS$_2$. Optimization of the shape, size and composition of the nanoparticle has the potential to enhance the photocurrent significantly with the prospect of applications in the detection of NIR
photons, and related technologies including optical telecommunication.
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All authors Jinlei Wu, Wei Huang, Ahsan Ali, Yuhong Chen, Elissaios Stavrou, Zuoti Xie, Jinhui Zhong, Khadga Jung Karki
Chemical Physics · Cross-listed from quant-ph
Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework
Ashish Kumar Patra, Anurag K. S. V., Sai Shankar P. et al. (+4 more)
The accurate treatment of electron correlation in extended molecular systems remains computationally challenging using classical electronic structure methods. Hybrid
quantum-classical
algorithms offer a potential route to overcome these limitations; however, their practical deployment on existing
quantum computers requires strategies that both reduce problem size and mitigate hardware noise. In this work, we investigate ground-state energy calculations of ligand-like molecules using Sample-based
Quantum Diagonalization (SQD) within the Density
Matrix Embedding Theory (DMET) framework, focusing on low-
symmetry systems with diverse bonding motifs that exhibit subsystem-dependent variations in fragment-environment
entanglement. These
entanglement-based variations directly influence bath orbital construction,
impurity sizes, and the structure of the embedded
Hamiltonians, posing nontrivial challenges for both
embedding and
quantum sampling. By combining DMET fragmentation with SQD-based construction of reduced configuration spaces through
quantum sampling and iterative configuration recovery, we perform
quantum simulations on IBM's Eagle R3 (IBM Sherbrooke) and IBM's Heron R3 (IBM Boston) superconducting
quantum hardware thereby, showing that the
entanglement structure across
embedding subsystems plays a central role in determining the efficiency and accuracy of the simulations. Despite these complexities, we show that the DMET-SQD framework yields ground-state energies in strong agreement with DMET-FCI
benchmarks, achieving chemical accuracy (1 kcal/mol) across all systems studied. These results demonstrate that SQD-based
quantum simulations can be robustly extended to low-
symmetry, chemically realistic, industry relevant molecules, and highlight the importance of
entanglement-aware
embedding strategies for scalable
quantum electronic structure calculations.
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All authors Ashish Kumar Patra, Anurag K. S. V., Sai Shankar P., Ruchika Bhat, Raghavendra V., Rahul Maitra, Jaiganesh G
Applied Physics · Cross-listed from eess.SY
Smart Traffic Systems: A Comprehensive Review of Recent Advancements, Technologies, and Challenges
Arindom Chakraborty, Mehedi Hasan, Amzad Hossain et al. (+1 more)
With an ever-growing urban population, the need for transportation is increasing at an alarming rate. Thus, the massive increase in the number of vehicles is creating traffic congestion which creates various environmental, societal, and economic problems. To tackle traffic-related issues, several Smart Traffic Systems (STS) have been proposed and implemented. As a result, a comprehensive review of STS has become necessary. The main objective of this paper is to provide an overview and a thorough review of the existing STSs in terms of various technological approaches, traffic detection technologies using different sensors, various networking/communication tools, and their pros and cons. The paper also provides information on major STS services. In addition, challenges related to modern STS are identified. Therefore, the taxonomy of STSs provided in this paper will aid researchers, urban planners, and policymakers to recognize and install the best-suited STSs for their settings.
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All authors Arindom Chakraborty, Mehedi Hasan, Amzad Hossain, Meratun Junnut Anee
Medical Physics · Cross-listed from cs.CV
Autonomous labeling of surgical resection margins using a foundation model
Xilin Yang, Musa Aydin, Yuhong Lu et al. (+9 more)
Assessing resection margins is central to pathological specimen evaluation and has profound implications for patient outcomes. Current practice employs physical inking, which is applied variably, and cautery artifacts can obscure the true margin on histological sections. We present a virtual inking network (VIN) that autonomously localizes the
surgical cut surface on whole-slide images, reducing reliance on inks and standardizing margin-focused review. VIN uses a frozen foundation model as the feature extractor and a compact two-layer multilayer perceptron trained for patch-level classification of cautery-consistent features. The
dataset comprised 120 hematoxylin and eosin (H&E) stained slides from 12 human tonsil tissue blocks, resulting in ~2 TB of uncompressed raw image data, where a board-certified pathologist provided boundary annotations. In blind testing with 20 slides from previously unseen blocks, VIN produced coherent margin overlays that qualitatively aligned with expert annotations across serial sections. Quantitatively, region-level accuracy was ~73.3% across the test set, with errors largely confined to limited areas that did not disrupt continuity of the whole-slide margin map. These results indicate that VIN captures cautery-related histomorphology and can provide a reproducible, ink-free margin delineation suitable for integration into routine digital pathology workflows and for downstream measurement of margin distances.
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All authors Xilin Yang, Musa Aydin, Yuhong Lu, Sahan Yoruc Selcuk, Bijie Bai, Yijie Zhang, Andrew Birkeland, Katjana Ehrlich, Julien Bec, Laura Marcu, Nir Pillar, Aydogan Ozcan
Optics
Optical spin precession
Abanoub Mikhail, Maxim Mazanov, Ilya Deiry et al. (+3 more)
Period-averaged electromagnetic
spin angular momentum is a well-established quantity for monochromatic fields, governing phenomena such as light-matter interactions with
chiral particles and
spin-orbit coupling effects. In contrast, the
spin angular momentum of non-monochromatic fields remains unexplored. Here, we extend the concept of optical
spin to the domain of non-monochromatic electromagnetic fields. Through this formulation, we uncover the precessional dynamics of electromagnetic
spin in specific polychromatic configurations, including the superposition of circularly and linearly polarized plane waves propagating orthogonally at different frequencies, as well as fields generated by a precessing
magnetic dipole. We discover that the dynamics of the electromagnetic
spin in these cases obeys a Landau-Lifshitz-like equation establishing a profound parallel between dynamics of magnetization and photonic
spin.
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All authors Abanoub Mikhail, Maxim Mazanov, Ilya Deiry, Mingzhao Song, Ivan Iorsh, Andrey Bogdanov
Geophysics
Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models
Yuma Matsumoto, Taro Yaoyama, Sangwon Lee et al. (+2 more)
In probabilistic seismic hazard analysis (PSHA), the exceedance probability of a ground-motion intensity measure (IM) is typically evaluated. However, in recent years, dynamic response analyses using ground-motion time histories as input have been increasingly common in seismic design and risk assessment, and thus there is a growing demand for representing seismic hazard in terms of ground-motion waveforms. In this study, we propose a novel PSHA framework, referred to as waveform-based PSHA, that enables the direct evaluation of the probability distribution of ground-motion waveforms by introducing ground-motion models (GMMs) based on deep generative models (ground-motion generative models; GMGMs) into the PSHA framework. In waveform-based PSHA, seismic hazard is represented, in a Monte Carlo sense, as a set of ground-motion waveforms. We propose the formulation of such a PSHA framework as well as an
algorithm for performing the required Monte Carlo simulations. Three different GMGMs based on generative
adversarial networks (GANs) are constructed. After verifying the performance of each GMGM, hazard evaluations using the proposed method are conducted for two numerical examples: one assuming a hypothetical area source and the other assuming an actual site and source faults in Japan. We demonstrate that seismic hazard can be represented as a set of ground-motion waveforms, and that the IM-based hazard obtained from these waveforms is consistent with the results of conventional PSHA using GMMs. Finally, nonlinear dynamic response analyses of a building model are performed using the evaluated seismic hazard as input, and it is shown that exceedance probabilities of engineering demand parameters (EDPs) as well as hazard disaggregation with respect to EDPs can be carried out in a straightforward manner within the proposed framework.
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All authors Yuma Matsumoto, Taro Yaoyama, Sangwon Lee, Asako Iwaki, Tatsuya Itoi
Medical Physics · Cross-listed from eess.IV
GACELLE: GPU-accelerated tools for model parameter estimation and image reconstruction
Kwok-Shing Chan, Hansol Lee, Yixin Ma et al. (+4 more)
Quantitative MRI (qMRI) offers tissue-specific biomarkers that can be tracked over time or compared across populations; however, its adoption in clinical research is hindered by significant computational demands of parameter estimation. Images acquired at high spatial resolution or requiring fitting multiple parameters often require lengthy processing time, constraining their use in routine pipelines and slowing methodological innovation and clinical translation. We present GACELLE, an open source, GPU-accelerated framework for high-throughput qMRI analysis. GACELLE provides a
stochastic gradient descent optimiser and a
stochastic sampler in MATLAB, enabling fast parameter mapping, improved estimation robustness via spatial regularisation, and uncertainty quantification. GACELLE prioritises accessibility: users only need to provide a forward signal model, while GACELLE's backend manages computational parallelisation, automatic parameter updates, and
memory-batching. The
stochastic solver performs fully vectorised Markov chain Monte Carlo with identical likelihood on CPU and GPU, ensuring reproducibility across hardware. Benchmarking demonstrates up to 451-fold acceleration for the
stochastic gradient descent solver and 14,380-fold acceleration for
stochastic sampling compared to CPU-based estimation, without compromising accuracy. We demonstrated GACELLE's versatility on three representative qMRI models and on an image reconstruction task. Across these applications, GACELLE improves parameter precision, enhances test-retest reproducibility, and reduces noise in quantitative maps. By combining speed, usability and flexibility, GACELLE provides a generalisable optimisation framework for medical image analysis. It lowers the computational barrier for qMRI, paving the way for reproducible biomarker development, large-scale imaging studies, and clinical translation.
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All authors Kwok-Shing Chan, Hansol Lee, Yixin Ma, Berkin Bilgic, Susie Y. Huang, Hong-Hsi Lee, José P. Marques
Computational Physics
Single-pixel imaging via data-driven and deep image prior dual networks
Jing-yi Shi, Jia-qi Song, Peng-cheng Ji et al. (+4 more)
Single-pixel imaging(SPI),especially when integrated with deep neural networks like deep image prior networks (DIP-Net) or data-driven networks (DD-Net), has gained considerable
attention for its capability to generate high-quality reconstructed images, even in the presence of sub-sampling conditions. However, DIP-Net often requires thousands of iterations to achieve high-quality image reconstruction, and DD-Net performs optimally only when the target closely resembles the features present in its training set. To overcome these limitations, we propose a dual-network iterative optimization (SPI-DNIO) framework that combines the strengths of both DD-Net and DIP-Net. It has been demonstrated that this approach can recover high-quality images with fewer iteration steps. Furthermore, to address the challenge of SPI inputs having less effective information at low sampling rates, we have designed a residual block enriched with gradient information, which can convey details to deeper layers, thereby enhancing the deep network's learning capabilities. We have applied these techniques to both indoor experiments with active lighting and outdoor long-range experiments with passive lighting. Our experimental results confirm the exceptional reconstruction capabilities and generalization performance of the SPI-DNIO framework.
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All authors Jing-yi Shi, Jia-qi Song, Peng-cheng Ji, Zi-qing Zhao, Yuan-jin Yu, Ming-fei Li, Ling-an Wu
Optics · Cross-listed from cond-mat.stat-mech
Entropy production and non-Gaussianity of fast processes at weak damping
Mario A. Ciampini, Jakob Rieser, Nikolai Kiesel et al. (+1 more)
We present a method of estimating the rate of
entropy production in underdamped dynamics by decomposing it into contributions originating in different non-
equilibrium effects. Specifically, a non-zero average velocity, a non-thermal width of the velocity distribution, correlations between position and velocity and non-Gaussian velocity statistics represent different ways in which the system can be out of
equilibrium and each give rise to a positive contribution to the overall
entropy production rate. We demonstrate that each contribution can be separately estimated from experimental trajectory data of levitated nano-particles subject to non-linear forces. We find that the majority of the
entropy production rate can be attributed to the first three contributions which can be estimated from the first and second moments of the position and velocity and therefore result in a useful \enquote{Gaussian} estimate for the
entropy production rate.
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All authors Mario A. Ciampini, Jakob Rieser, Nikolai Kiesel, Andreas Dechant
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Scalable Synthesis of Large-Area WS2 Thin Films from Tungsten Precursors by Thermal CVD and Their Application in Schottky-Barrier Solar Cells
Jisun Kim, Yoosuk Kim, Seung-Ho Park et al. (+2 more)
Two-dimensional tungsten disulfide (WS2) is a promising
semiconductor for next-generation optoelectronic and photovoltaic devices, but scalable routes to uniform, large-area films remain challenging. In this study, a systematic thermal chemical vapor deposition (T-CVD) strategy is presented to synthesize centimeter-scale WS2 thin films by sulfurizing tungsten (W) precursors in a controlled sulfur vapor environment. High-purity sputtered W thin films on SiO2/Si and W foils were sulfurized at temperatures between 400 and 1000 C, with Raman spectroscopy identifying 800 C as the optimal growth temperature. Under these conditions, the films exhibit the characteristic E12g (349.7 cm-1) and A1g (416.8 cm-1) modes with narrow full-width at half-maximum values, indicative of high crystallinity and controlled thickness. Optical microscopy, scanning electron microscopy, atomic force microscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy collectively validate the creation of stoichiometric, layered WS2 with significantly enhanced uniformity and diminished roughness when utilizing sputtered W thin films in contrast to W foils. Leveraging this optimized process, WS2 films grown on W foils were transferred onto target substrates, including indium tin
oxide (ITO)-coated glass, using a PMMA-assisted wet-transfer method that preserves structural integrity over large areas. Schottky-barrier
solar cells with an Au/WS2/ITO architecture fabricated from these films deliver a short-
circuit current density of 7.91 mA cm-2, an open-
circuit voltage of 0.495 V, and a power conversion efficiency of 1.45 percent. These results demonstrate that sulfurization of W thin films and foils via T-CVD provides a scalable, substrate-compatible platform for integrating WS2 into practical optoelectronic and low-cost photovoltaic technologies.
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All authors Jisun Kim, Yoosuk Kim, Seung-Ho Park, Yong Hun Ko, Chong-Yun Park
Chemical Physics · Cross-listed from quant-ph
Nonlinear Optical Quantum Communication with a Two-Dimensional Perovskite Light Source
Shuyue Feng, Zijian Gan, Camryn J. Gloor et al. (+2 more)
Two-dimensional organic-inorganic hybrid perovskite (2D-OIHP)
quantum wells are emerging as promising light sources for
quantum communication technologies, owing to their ability to generate polarization-encoded optical signals. In this work, we explore how nonlinear optical phenomena can be exploited for
quantum information applications, demonstrating the versatility that arises from resonant coupling among excited states. By tracking changes in the ellipticities of signal
photons on femtosecond timescales in four-wave-mixing experiments, we first establish a method for information encoding based on
exciton spin dynamics and biexciton correlations. Using single-
photon detection, we then implement a proof-of-principle
quantum communication protocol by mapping these polarization states onto binary sequences. While the polarizations of weak coherent pulses are typically manipulated with optical elements in traditional
quantum key distribution approaches, the intrinsic electronic structure and
spin relaxation processes within the 2D-OIHP system determine the characteristics of the signal
photons in our method. As a demonstration, an ASCII message consisting of 56 bits is transmitted through the polarization states of
photons emitted by 2D-OIHP
quantum wells. These results show that the information transmission efficiency depends strongly on contributions from biexciton states, highlighting the potential of
spin-dependent nonlinear optical processes for
quantum communication.
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All authors Shuyue Feng, Zijian Gan, Camryn J. Gloor, Wei You, Andrew M. Moran
Fluid Dynamics
Prediction performance of random reservoirs with different topology for nonlinear dynamical systems with different number of degrees of freedom
Shailendra K. Rathor, Lina Jaurigue, Martin Ziegler et al. (+1 more)
Reservoir computing (RC) is a powerful framework for predicting nonlinear dynamical systems, yet the role of reservoir
topology$-$particularly
symmetry in connectivity and weights$-$remains not adequately understood. This work investigates how the structure of the network influences the performance of RC in four systems of increasing complexity: the Mackey-Glass system with delayed-feedback, two low-dimensional thermal convection models, and a three-dimensional shear flow model exhibiting transition to
turbulence. Using five reservoir
topologies in which connectivity patterns and edge weights are controlled independently, we evaluate both direct- and cross-prediction tasks. The results show that symmetric reservoir networks substantially improve prediction accuracy for the convection-based systems, especially when the input dimension is smaller than the number of degrees of freedom. In contrast, the shear-flow model displays almost no sensitivity to topological
symmetry due to its strongly chaotic high-dimensional dynamics. These findings reveal how structural properties of reservoir networks affect their ability to learn complex dynamics and provide guidance for designing more effective RC architectures.
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All authors Shailendra K. Rathor, Lina Jaurigue, Martin Ziegler, Jörg Schumacher
Optics
Self-supervised prior learning improves structured illumination microscopy resolution
Ze-Hao Wang, Tong-Tian Weng, Long-Kun Shan et al. (+4 more)
Structured illumination microscopy (SIM) is a wide-field super-resolution technique normally limited to roughly twice the diffraction-limited resolution ($\approx 100$–$200$~nm). Surpassing this bound is a classic ill-posed inverse problem: recovering high-frequency structure from band-limited raw data. We introduce SIMFormer, a fully blind SIM reconstruction framework that learns a powerful, data-driven prior directly from raw images via self-supervision. This learned prior regularizes the solution and enables reliable extrapolation beyond the optical transfer function cutoff, yielding an effective resolution of approximately 45~nm. We validate SIMFormer on synthetic data and the BioSR
dataset, where it resolves features such as flattened endoplasmic reticulum lipid
bilayers previously reported to require STORM-level resolution. A self-distilled variant, SIMFormer+, further improves noise robustness while preserving high resolution at extremely low
photon counts. These results show that learned priors can substantially extend SIM resolution and robustness, enabling rapid, large-scale imaging with STORM-level detail.
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All authors Ze-Hao Wang, Tong-Tian Weng, Long-Kun Shan, Xiang-Dong Chen, Guang-Can Guo, Fang-Wen Sun, Tian-Long Chen
Optics · Cross-listed from cs.CV
TPCNet: Triple physical constraints for Low-light Image Enhancement
Jing-Yi Shi, Ming-Fei Li and Ling-An Wu
Low-light image enhancement is an essential computer
vision task to improve image contrast and to decrease the effects of color bias and noise. Many existing interpretable deep-learning
algorithms exploit the Retinex theory as the basis of model design. However, previous Retinex-based
algorithms, that consider reflected objects as ideal Lambertian ignore specular reflection in the modeling process and construct the physical constraints in image space, limiting generalization of the model. To address this issue, we preserve the specular reflection coefficient and reformulate the original physical constraints in the imaging process based on the Kubelka-Munk theory, thereby constructing constraint relationship between illumination, reflection, and detection, the so-called triple physical constraints (TPCs)theory. Based on this theory, the physical constraints are constructed in the feature space of the model to obtain the TPC network (TPCNet). Comprehensive quantitative and qualitative
benchmark and ablation experiments confirm that these constraints effectively improve the performance metrics and visual quality without introducing new parameters, and demonstrate that our TPCNet outperforms other state-of-the-art methods on 10
datasets.
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Chemical Physics
A rigorous adiabatic approach to ultracold atom-molecule collisions in a magnetic field
Nathan S. Prins and Timur V. Tscherbul
We extend the rigorous adiabatic coupled-channel formalism to ultracold nonreactive atom-molecule collisions in the presence of an external
magnetic field. The wavefunction of the collision complex is expanded in adiabatic basis states obtained by solving the
eigenvalue problem for the adiabatic
Hamiltonian (the total
Hamiltonian of the collision complex minus the radial kinetic energy) on a grid of atom-molecule distances $R$. The resulting coupled-channel equations are solved using the diabatic-by-sector method. We show that the adiabatic approach provides accurate cross sections for cold and ultracold Mg ($^1$S) + NH ($^3\Sigma^-$) collisions in a
magnetic field with ~2 times fewer channels than the standard diabatic basis. We further develop an efficient $R$-dependent basis truncation protocol (RBT), in which the elements of the log-derivative
matrix are sampled and discarded as it is propagated from small to large $R$. While RBT can be applied in both the adiabatic and diabatic bases, we show that the adiabatic basis can be reduced to just the open channels at long range, leading to an overall computational gain of ~15-30 for the propagation part of the calculation. The gain is particularly significant in situations where substantial errors in the calculated cross sections ($<$50%) can be tolerated or long-range interactions are involved, making the adiabatic basis formulation a promising approach to strongly anisotropic collisions and chemical reactions in the presence of an external
magnetic field.
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Fluid Dynamics
An Efficient and Accurate Surrogate Modeling of Flapping Dynamics in Inverted Elastic Foils using Hypergraph Neural Networks
Aarshana R. Parekh, Rui Gao and Rajeev K. Jaiman
Cantilevered elastic foils can undergo self-induced, large-amplitude flapping when subject to fluid flow, a widely observed phenomenon of fluid-structure interaction, from fluttering leaves or the movement of fish fins. When harnessed in steady currents, these oscillations enable the extraction of kinetic energy from the flow. However, accurately predicting these dynamics requires high-fidelity simulations that are prohibitively expensive to perform across the broad configuration space needed for design optimization. To address this, we develop a novel graph neural network (GNN) surrogate for the inverted foil problem, modeled as an elastically mounted rigid foil undergoing trailing-edge pitching in uniform flow. The coupled fluid-structure dynamics are solved using a Petrov-Galerkin finite element method with an arbitrary Lagrangian-Eulerian formulation, providing high-fidelity data for training and validation. The surrogate uses a rotation-equivariant, quasi-monolithic GNN architecture: structural mesh motion is compressed via proper orthogonal decomposition and advanced through a multilayer perceptron. At the same time, the GNN evolves the flow field consistent with system states. Specifically, this study extends the hypergraph framework to flexible, self-oscillating foils, capturing the nonlinear coupling between
vortex dynamics and structural motion. The GNN surrogate achieves less than 1.5% error in predicting tip displacement and force coefficients over thousands of time steps, while accurately reproducing dominant
vortex-shedding frequencies. The model captures energy transfer metrics within 3% of full-order simulations, demonstrating both accuracy and long-term stability. These results show a new, efficient surrogate for long-horizon prediction of unsteady flow-structure dynamics in energy-harvesting systems.
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Optics · Cross-listed from cond-mat.dis-nn
Evolving disorder in non-Hermitian lattices
I. Komis, E. T. Kokkinakis, K. G. Makris et al. (+1 more)
The impact of disorder on wave transport has been extensively studied in Hermitian systems, where static randomness gives rise to Anderson localization. In non-Hermitian
lattices, static disorder can lead to peculiar transport features, including jumpy wave
evolution. By contrast, much less is known about how transport is modified when the on-site disorder evolves during propagation. Here we address this problem by investigating two pertinent non-Hermitian
lattice models with disorder altered at regular intervals, characterized by a finite disorder period. In
lattices with symmetric couplings and complex on-site disorder, short disorder periods suppress localization and give rise to
diffusion-like spreading, while longer periods allow the emergence of jumps. In Hatano-Nelson
lattices with real on-site disorder, the non-Hermitian skin effect asymptotically dominates regardless of the disorder strength, while the disorder period reshapes the drift velocity and modulates its competition with Anderson localization. These results establish evolving disorder as a novel way of tuning non-Hermitian transport.
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All authors I. Komis, E. T. Kokkinakis, K. G. Makris, E. N. Economou