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Tuesday 9 June

Medical Physics

Foveated-Imaging Geometry CT Architecture and Seeded Diffusion Model Enabling Global Super-Resolution Reconstruction

Wenxin Mo, Yingxian Xia, Yongle Yan et al. (+3 more)

For X-ray computed tomography (CT), a smaller detector pixel size generally leads to higher scanner spatial resolution, but inevitably increases system cost as well as data overhead in acquisition and processing. To achieve high-resolution (HR) CT imaging in a more resource-efficient manner, we propose a Foveated-Imaging Geometry CT (FIGCT) architecture, which integrates local HR data into an acquisition scheme dominated by low-resolution (LR) measurements. We further develop a Diffusion Probabilistic FIGCT Super-Resolution Reconstruction (DPFSR) framework to generate global HR CT images over the full field of view (FOV). The concept of FIGCT is first established, and its typical configurations are characterized according to the arrangement of HR data. Two key indices, namely the HR data fraction (HDF) and the LR-to-HR detector pixel size ratio (LHR), are introduced to describe the FIGCT geometry. The proposed DPFSR incorporates local HR information into intermediate clean-image estimates in both the projection and image domains during the reverse diffusion process. This additional step not only guides HR image generation from LR data, but also improves data consistency between the clean-image estimates and the originally measured data. Preliminary numerical simulation results on FIGCT show that the proposed architecture provides high-precision CT images within the region of interest (ROI) corresponding to the HR data, while the spatial resolution deteriorates rapidly outside the ROI. With DPFSR, global HR reconstruction is achieved on the AAPM Grand Challenge dataset and swine lung CT data, outperforming existing SR methods in terms of Learned Perceptual Image Patch Similarity (LPIPS), PSNR, and SSIM.
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All authors Wenxin Mo, Yingxian Xia, Yongle Yan, Hao Zhou, Li Zhang, Hewei Gao

Quantum Physics

Camera-enabled scalable homodyne detection of multimode quantum light

Young-Do Yoon, Chan Roh, Geunhee Gwak et al. (+1 more)

Scalability is a key challenge in advancing quantum technologies such as quantum computing, communication, and metrology. Photonic systems offer a promising route to scalability by enabling the deterministic generation of large-scale entangled states. Homodyne detection is an essential quantum measurement to exploit such entangled states, enabling quantum-enhanced measurement, deterministic quantum teleportation, GKP-state breeding, and quantum error correction. Despite the recent progress in generating large-scale quantum states, realizing quantum measurement at scale remains a major challenge. Here we realize scalable and efficient homodyne detection by leveraging a large number of pixels in a charge-coupled-device (CCD) camera. Our approach enables shot-noise-limited quadrature measurements of 60 optical modes simultaneously, while requiring only nanowatt-level local oscillator power per mode – a six-order-of-magnitude reduction compared to conventional methods. The system achieves clearance exceeding 24 dB for all modes with negligible crosstalk. We demonstrate its compatibility with a large-scale quantum state by directly observing squeezing and entanglement in 60 optical modes. Furthermore, we showcase applications in verifying multipartite entanglement and in the conditional preparation of multimode states. This work provides a scalable method for quantum measurement, paving the way for large-scale quantum information processing.
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All authors Young-Do Yoon, Chan Roh, Geunhee Gwak, Young-Sik Ra

Fluid Dynamics

Baroclinic wave dynamics in the Ekman-free rotating rectangular annulus with localized forced plume

Shivam Swarnakar, Ayan Kumar Banerjee, Sridhar Balasubramanian et al. (+1 more)

We report numerical simulations of a rotating rectangular annulus that isolates the Ekman-free bulk of the cylindrical baroclinic annulus, subjected to bi-directional temperature gradients imposed by a uniformly cooled inner wall and a localized forced heated plume at the outer bottom. The finite-volume OpenFOAM solver is employed across combinations of source Richardson number $Ri_0 = 99, 4, 1$ and Rossby number $Ro = 0.3, 0.1, 0.07$. A non-dimensional scaling of the governing equations identifies geostrophic-hydrostatic balance as the leading-order bulk state, a result confirmed a posteriori by the $x$ and $z-$momentum budgets. Baroclinic waves of mode $m=2$ at $Ro=0.3$ transition to $m=3$ as $Ro$ decreases, consistent with the contraction of the Eady deformation radius $L_\rho = NH/f$; Complex Empirical Orthogonal Function (CEOF) analysis characterizes the wave regime and detects a Hopf-bifurcated vacillating state at $Ri_0 = 99,~Ro = 0.1$. The plume morphology, classified through the Morton length scale and source flux-balance parameter, transitions from weak, laterally-swept structures at $Ri_0 = 99$ to sustained columnar plumes traversing the full baroclinic depth at $Ri_0 \leq 4$. The plume entrainment coefficient $\Gamma(z)$ shows opposite rotational sensitivities at low and high $Ri_0$, which we organize through a local plume Rossby number $Ro_p = w/(2\Omega b)$. A mixing-length argument predicts a bulk turbulent heat flux $\overline{u'T'} \propto Ri_0^{-1/2}$, anticipating an order-of-magnitude enhancement from $Ri_0 = 99$ to $Ri_0 = 1$, in agreement with the simulations. A regime map in the $(Ri_0, Ro)$ plane reveals that, within the explored range, the plume-regime and wave-selection problems are approximately separable: $Ri_0$ sets the plume regime while $Ro$ selects the dominant baroclinic wave mode.
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All authors Shivam Swarnakar, Ayan Kumar Banerjee, Sridhar Balasubramanian, Amitabh Bhattacharya

Adaptation and Self-Organizing Systems

Towards Critical Branching Mechanism in Recurrent Neural Networks

Feixiang Ren and Ling Feng

Criticality has been proposed as a key organizing principle in biological neural systems, yet its origin and relevance in artificial neural networks remain unclear. We analyze hidden-state dynamics in trained long short-term memory (LSTM) networks and show that small networks near their optimal training epochs (steps) exhibit scale-free avalanche statistics and branching parameters close to unity, indicative of near-critical dynamics, while larger models remain subcritical. To explain the coexistence of subcritical branching with robust $1/f^{\beta}$ noise, we introduce a mixture branching process framework that links heterogeneous branching dynamics to long-range temporal correlations. These results identify critical-like behavior in LSTMs as an emergent, capacity-dependent dynamical regime.
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High Energy Physics (Experiment)

Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis

Ruobing Jiang, Dawei Fu, Cheng Jiang et al. (+6 more)

Muon collider research spans accelerator physics, detector instrumentation, and high-energy phenomenology, with relevant evidence scattered across a rapidly expanding and heterogeneous body of scientific literature. As high-energy physics (HEP) increasingly explores agent-assisted analysis workflows, efficiently locating, integrating, and verifying scientific evidence becomes an essential capability. While retrieval-augmented generation (RAG) offers a promising framework for scientific question answering, integrating agentic reasoning without compromising retrieval precision remains a key challenge. In this work, we present agentic hybrid RAG, an evidence-grounded RAG framework for muon collider research. The framework combines a hybrid retriever, integrating sparse lexical and dense semantic retrieval, with an agentic reasoning module for query decomposition, evidence expansion, and grounded answer generation. To enable systematic evaluation, we construct the first benchmark for retrieval-augmented scientific question answering in the muon collider domain, comprising a curated literature corpus together with dedicated retrieval and answer-generation benchmarks covering major detector and physics research topics. Extensive evaluation shows that hybrid retrieval provides the strongest retrieval backbone, while agentic reasoning is most effective for controlled evidence expansion and answer synthesis. Built on this principle, agentic hybrid RAG consistently outperforms representative retrieval and RAG baselines in retrieval effectiveness, answer quality, evidence coverage, and factual grounding. Together, the benchmark and framework provide a foundation for evidence-grounded scientific question answering and future HEP analysis agents operating over large-scale scientific literature. Code is available at \href{https://github.com/AItutorialjrb/RAG_muon_JINST}{this URL}.
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All authors Ruobing Jiang, Dawei Fu, Cheng Jiang, Tianyi Yang, Zijian Wang, Youpeng Wu, Yong Ban, Yajun Mao, Qiang Li

Quantum Physics

Nonreciprocal photon bundle emission

Baijun Li, Jing-Xue Liu, Tian-Xiang Lu et al. (+3 more)

Quantum squeezing, a cornerstone of quantum optics and photonics, has played a key role in achieving ultra-precision sensing and realizing nonreciprocal engineering. However, the nonreciprocal multiquanta emission has remained largely unexplored by using directional quantum squeezing. Here, the one-way photon-photon bundle emission in a compound system consisted of two coupled optical resonators and a two-level atom is investigated. It is found that the directional quantum squeezing induces the asymmetric frequency detuning and photon hopping interaction between the two resonators, leading to the directional excitation of the two-photon super-Rabi oscillation. In particular, by harnessing intrinsic dissipation of the system, two types of two-photon bundle emission can be selectively induced for the probe field input from one direction while it is prohibited with the probe from the other direction. This finding bridges the broad fields ranging from nonreciprocal physics to quantum squeezing optics and multiquanta emission control through an all-optical approach, which can enable potential applications in chiral quantum emitters and backscattering-immune photonic communications.
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All authors Baijun Li, Jing-Xue Liu, Tian-Xiang Lu, Le-Man Kuang, Chaohong Lee, Hui Jing

Computational Physics

Flow-based generative models for amortized Bayesian inference in regression and inverse PDE problems

Shaoqian Zhou, Ling Guo and Xuhui Meng

Bayesian inference provides a principled framework for uncertainty quantification in scientific machine learning. However, conventional Bayesian approaches usually require solving a new inference problem for each observation set, causing substantial computational costs that hinder real-time applications like online monitoring and digital twins. Furthermore, inferring over infinite-dimensional function spaces with varying observation sets poses major challenges for existing amortized inference methods. In this work, we propose Flow-ABI, a flow-based generative framework for amortized Bayesian inference in regression and inverse partial differential equation (PDE) problems. It consists of two components: (i) a functional prior model that learns expressive priors from historical data and physical knowledge through flow matching, and (ii) a set-conditioned functional posterior sampler mapping observation sets to functional posterior distributions. The learned posterior model naturally accommodates varying, permutation-invariant observation sets, and generalizes across different observation discretizations. Once trained, Flow-ABI enables near-real-time posterior sampling for previously unseen observations without retraining or iterative optimization. The proposed methodology can be seamlessly integrated with a wide class of scientific machine learning frameworks, including physics-informed neural networks and neural operators, for uncertainty-aware inverse PDE modeling. Experiments demonstrate that Flow-ABI accurately captures both Gaussian and non-Gaussian posterior distributions while achieving over two-order-of-magnitude speedups relative to the gold-standard Bayesian inference method, Hamiltonian Monte Carlo. These results show Flow-ABI is an effective, scalable, and computationally efficient framework for uncertainty quantification in scientific machine learning.
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High Energy Physics (Phenomenology)

Light and Strange Baryons in Medium

T. Massimino, T. Klähn and Z. Papp

We solve the Goldstone-boson-exchange (GBE) relativistic constituent quark model of light and strange baryons by using the Faddeev approach. The model reproduces the vacuum mass spectrum of light and strange baryons below $2$ GeV reasonably well. To test the sensitivity of the model to possible medium-induced effects, we vary the masses of the constituent quarks and the exchange bosons, the confinement strength, and the quark-meson coupling constant. In a parametric study, we consider a set of power law scaling relations for these parameters, including some motivated by constituent quark level current algebra relations. We find that the baryon spectrum is most sensitive to the quark-meson coupling constant, and generally observe a decrease in baryon mass with decreasing constituent quark mass. We qualitatively estimate the impact of these mass shifts on ideal-gas baryon yields and yield ratios. Absolute yields can change significantly already for mass shifts of a few $10$~MeV, whereas yield ratios are strongly modified only when the compared baryons have different constituent-quark-mass dependence.
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Pattern Formation and Solitons

Mean-field models for morphogenetic processes in physiological contexts

D. Hernández, Alejandro Valdés López and E. C. Herrera-Hernández

This work introduces a biophysical formalism to describe the spatiotemporal evolution of the chemical profile in tissues, with the novelty of modeling tissue compartmentalization and the mechanism by which cells maintain the system far from thermodynamic equilibrium via production and/or degradation of substances. The models were derived from conservation laws, chemical kinetic theory, and geometric constraints, while considering fundamental properties of tissues to connect theoretical modeling with experimental observations. In a morphogenetic context, each morphogen is described by two coupled reaction-diffusion equations, representing intra- and extracellular dynamics, linked through membrane transport processes such as nonlinear, cross, and anomalous diffusion. We explore the models' morphogenetic potential through diffusion-driven instabilities and discuss how natural tissue heterogeneities influence Turing instabilities and self-organized phenomena. The mathematical structure reveals that two-morphogen systems can produce Turing patterns with multiple characteristic length scales, while the system's dimensionality enables chaotic behavior in well-mixed dynamics. Moreover, due to domain coupling, Turing instabilities are allowed for single-morphogen systems. We used Schnakenberg kinetics to demonstrate that Turing patterns arise even when the activator diffuses faster than the inhibitor (d$<$1), thereby expanding the parameter space for pattern formation. Our results suggest that tissue spatial structure has important consequences for Turing instability mechanisms, in some cases weakening the usual conditions for its emergence while widening the possible patterns it can produce. The proposed framework offers a minimal mathematical basis to explore emergent dynamics in biological and synthetic contexts, with potential applications in developmental biology and tissue engineering.
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Materials Science

Neural electron backscatter diffraction

I-Tzu Huang and Marat I. Latypov

At the mesoscale, the state of a material is described by continuous fields. In polycrystals, crystallographic orientation and defect content vary continuously within grains, and grain boundaries trace continuous curves. Like other spatially resolved characterization methods, electron backscatter diffraction (EBSD) records this continuum on a discrete grid. Every subsequent analysis inherits the grid, whether it is classical Hough indexing or pattern-based machine learning. We introduce neural EBSD, which treats a scan as a continuous and differentiable field of Kikuchi diffraction intensity over specimen and detector coordinates. Two formulations are explored: a joint network over all four coordinates, and a factorized representation that combines continuous specimen-domain coefficient fields with learned detector-domain basis patterns. The factorized formulation exhibits higher accuracy: for recrystallized and additively manufactured Ni-base superalloys, it reconstructs 900,000 Kikuchi patterns per map with mean errors below 1% of the maximum intensity, while reducing data storage nearly 750-fold relative to the raw patterns. Since the learned field is continuous, patterns can be queried at any specimen position. Trained only on a quarter of the scan points, the model recovers withheld patterns whose indexed orientations fall within 4 deg. of reference at 97% of positions in the recrystallized alloy. Analytical spatial derivatives of the differentiable representation provide a diffraction gradient that localizes grain boundaries continuously, free of indexing, disorientation thresholds, and staircase artifacts. The gradient field also exposes intragranular heterogeneity, including dislocation-cell substructure in the as-built alloy.
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Disordered Systems and Neural Networks

Magnetic HIP-NN for spin dynamics in disordered itinerant magnets

Supriyo Ghosh, Yunhao Fan, Sheng Zhang et al. (+2 more)

We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disordered itinerant magnets. The resulting magnetic HIP-NN (mHIP-NN) incorporates rotationally invariant spin correlations directly into hierarchical message-passing layers, enabling the network to learn emergent magnetic energy landscapes and effective local fields from coupled geometric-spin environments while preserving spin-rotation symmetry. As a benchmark application, we consider structurally disordered itinerant $s$-$d$ exchange models in which the effective magnetic forces arise dynamically from the instantaneous electronic structure and are computationally prohibitive to evaluate using conventional exact-diagonalization-based approaches. We show that mHIP-NN accurately reproduces the local torques governing Landau-Lifshitz-Gilbert dynamics and faithfully captures the nonequilibrium evolution of spatial spin correlations following thermal quenches. Our results establish symmetry-aware hierarchical message-passing networks as an efficient and scalable framework for large-scale simulations of frustrated itinerant spin systems and nonequilibrium magnetic dynamics. More broadly, because the learned energy functional remains fully differentiable with respect to both atomic coordinates and spin variables, the framework also provides a natural foundation for spin-dependent interatomic potentials and coupled atom-spin dynamics.
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All authors Supriyo Ghosh, Yunhao Fan, Sheng Zhang, Kipton Barros, Gia-Wei Chern

Nuclear Theory

Neural-network solution of subtracted three-body Faddeev integral equations near the Efimov limit

Lucas A. Souza

We apply a deep-neural-network (DNN) ansatz to the symmetrized spectator vector of the subtracted three-body Faddeev integral equation for identical bosons near the Efimov limit. The network is trained by minimizing the residual of the discretized integral equation, while the positive binding scale associated with the three-body energy is treated as a trainable parameter. Deterministic diagonalization of the same discretized kernel is used only as an a posteriori numerical benchmark. As preliminary validation, the neural-solver strategy is tested on the analytically solvable hydrogen radial problem. At unitarity, the DNN reproduces the Efimov ground-state binding scale with a DNN–deterministic deviation of $0.022\%$, while the first excited state is recovered to $0.002\%$. The deterministic solver recovers the universal Efimov scaling ratio $e^{2\pi/s_0}\simeq 515.03$, and the neural method traces the bound-state branches as a function of the inverse scattering length $1/a$ by continuation from the unitary solution. These results indicate that DNN-based residual minimization can provide a compact and differentiable representation of a renormalized few-body integral-equation solution in a regime governed by discrete scale invariance.
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Nuclear Theory

Global polarization of $\Lambda$, $\Xi^{-}$, and $\Omega^{-}$ hyperons in Au+Au collisions at RHIC BES-II energies

Gen-Hui Li, Cong Yi, Xiang-Yu Wu et al. (+2 more)

We investigate the global spin polarization of $\Lambda$ hyperons and the multi-strange hyperons $\Xi^{-}$ and $\Omega^{-}$ in Au+Au collisions across the RHIC Beam Energy Scan II (BES-II) energy range, $\sqrt{s_{NN}}=7.7$–$27$ GeV. The polarization is computed using the modified Cooper–Frye formula, which includes contributions from thermal vorticity, the thermal shear tensor, and the gradient of the baryon chemical potential, combined with the (3+1)-dimensional viscous hydrodynamic framework CLVisc with SMASH initial conditions. We present the global polarization as a function of collision energy, centrality, transverse momentum, and rapidity. We find that the global polarization of $\Omega^{-}$ is systematically larger than those of $\Lambda$ and $\Xi^{-}$ because of its larger spin quantum number, but it remains below the central value of the recent STAR measurement. This discrepancy may suggest that additional mechanisms, such as spin correlations among strange quarks inside the $\Omega^{-}$, could contribute to the observed $\Omega^{-}$ polarization. We also find that the global-polarization splitting between hyperons and anti-hyperons increases toward lower collision energies and is dominated by the chemical-potential-gradient contribution.
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All authors Gen-Hui Li, Cong Yi, Xiang-Yu Wu, Shi Pu, Guang-You Qin

Computational Physics

A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data

Junoh Jung, David Lenz, Emil Constantinescu et al. (+1 more)

Continuous approximations of flow data are useful for downstream analysis, differentiation, and visualization, but purely data-driven reconstructions do not, in general, preserve the governing physics. This limitation becomes particularly important when input data are physically inconsistent, whether due to low-fidelity discretizations or unmodeled discrepancies. In such cases, reconstructed fields may exhibit inaccurate PDE residuals, violated balance laws, or unreliable derived quantities. To address this, we propose a physics-informed B-spline framework that embeds physical constraints directly into the reconstruction process. The method constructs compact, continuously differentiable representations of discrete fields using tensor-product B-splines and determines spline control points by solving an optimization problem balancing data fidelity with residuals of the governing PDEs, alongside initial and boundary conditions. Leveraging exact analytical derivatives of the B-spline basis enables efficient and accurate evaluation of physical residuals without storing full-resolution fields. We refer to this approach as physics-informed multivariate functional approximation (PI-MFA). Numerical studies on the 1D convection-diffusion, 2D coupled Burgers, and 2D incompressible Navier-Stokes equations show PI-MFA reduces PDE residuals and improves global balance-law consistency. Compared with standard and regularized MFA, PI-MFA produces more physically faithful reconstructions and, for physically inconsistent data, lower approximation errors, while offering computational advantages over tested physics-informed neural networks. Overall, PI-MFA preserves the compactness, local support, and exact differentiability of classical spline spaces while producing reliable continuous flow fields for scientific analysis and visualization.
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All authors Junoh Jung, David Lenz, Emil Constantinescu, Tom Peterka

General Relativity and Quantum Cosmology

Tuning A Rotating Black Hole Spectrum with Dark Matter Halo: Quasibound States, Scalar Cloud, Black Hole Bomb and Superradiant Scattering

David Senjaya

We investigate the spectral dynamics of a rotating black hole embedded in a Dehnen $(1,4,\gamma)$ dark matter halo, where quasibound states and superradiant scattering jointly characterize the physical response of the system. Starting from an exact Schwarzschild–Dehnen solution, we construct its rotating counterpart via the Newman–Janis algorithm, yielding a consistent axisymmetric geometry that incorporates the influence of a structured halo. The Dehnen profile, through its inner slope parameter $\gamma$, introduces a controlled deformation of both the near-horizon and asymptotic regions of the spacetime. Using the analytical asymptotic matching method, we derive the quasibound-state spectrum and show that the real part of the frequency retains a hydrogen-like structure, but is systematically shifted by the halo through the effective mass scale $\rho_0 r_0^3/(\gamma-3)$. In particular, denser, more extended, and more cuspy halos enhance the binding energy, lower the critical mass required for the onset of instability, and typically suppress the growth rate of the black hole bomb. In the scattering sector, we obtain an analytic expression for the superradiant amplification factor and find that the same halo properties that strengthen binding effects also tend to narrow the superradiant window. These results demonstrate that quasibound states and superradiant scattering are complementary manifestations of a unified spectral structure, with the Dehnen halo acting as an environmental tuner that imprints its properties directly onto both the resonance spectrum and the energy-extraction channels of the rotating black hole.
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Statistical Mechanics · Cross-listed from cs.LG

Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks

Parviz Haggi-Mani and Irina Rish

The analogy between deep neural network forward passes and renormalization group (RG) flows has been repeatedly noted in the literature, but existing treatments remain qualitative: depth is described as a coarse-graining scale, attention is likened to a partition function, and representations are said to flow toward fixed points. No existing work has defined a measurable RG order parameter, tested it under controlled variation of the input distribution, or made quantitative predictions that are empirically verified. We study the simplest architecture for which the analogy is tractable: a pure MLP residual stack trained on masked token prediction over synthetic Markov chain sequences with known spectral properties. We report three findings. (i) The effective rank of the residual stream decreases monotonically with depth after training, consistent with progressive integration of irrelevant degrees of freedom. (ii) This rank collapse is selective: it occurs for chains with short correlation length approximately 1 but is absent for chains with long correlation length approximately 7, measured at the position level to control for mean-pooling artifacts. The network preserves exactly the degrees of freedom relevant to the prediction task, the content of the RG relevance criterion. (iii) Inter-layer kernel drift is concentrated at one or two specific transitions, with the remainder of the network near a fixed point, consistent with a discrete fixed-point plateau. Together these findings constitute the first quantitative, position-level evidence that MLP residual networks implement a selective coarse-graining procedure governed by the spectral structure of the input distribution.
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Quantum Physics

Nonreciprocal Photon Blockade in an Asymmetric Cavity

Shao-Xiong Wu, Jin-Na Fan, Dan Yan et al. (+3 more)

We propose a scheme to realize tunable and strong nonreciprocal photon blockade (PB) in an asymmetric Fabry-Pérot cavity. The setup consists of a single-mode optical cavity trapping a two-level atom, with the cavity coherently driven by a laser and the atom pumped by an auxiliary control field of the same frequency. By engineering quantum interference between multiple excitation pathways by adjusting the amplitude and relative phase of the control laser, we identify two distinct optimal control conditions that enable directional suppression of two-photon states. Under optimal control conditions, strong nonreciprocal PB is achieved, with a nonreciprocal ratio exceeding 30 dB over a broad operational bandwidth. The proposed protocol requires only standard coherent laser sources and is compatible with current cavity QED experimental setups, offering a practical and scalable platform for nonreciprocal quantum photonics.
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All authors Shao-Xiong Wu, Jin-Na Fan, Dan Yan, Cheng-Hua Bai, Qiannan Wu, Mengwei Li

Quantum Physics

Optomechanical system with tunable dissipative and dispersive couplings

Quansen Wang, Yuefan Wu, Doudou Wang et al. (+4 more)

We demonstrate an optomechanical system with tunable dissipative and dispersive couplings using a Fabry-Perot cavity and a string mechanical resonator. By varying the diameter and material of the mechanical resonator, and the relative location between the mechanical resonator and the cavity, the relative strengths of dissipative and dispersive coupling could be tuned continuously from dissipation-dominated regime to dispersion-dominated regime. In our experiments, the dissipative-to-dispersive coupling ratios of 1.3 and 0.6 are achieved by using two different mechanical resonators, corresponding to a transition from dissipation-dominated to dispersion-dominated optomechanical system. Theoretically, the coupling ratio could be tuned from 25 to 0.02 by optimizing the mechanical resonator, spanning over three orders of magnitude. These two distinct coupling regimes are achieved with the same experimental platform. The capability to freely adjust the coupling ratio provides a versatile platform for exploring quantum effects of massive mechanical resonators and quantum-limited measurements.
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All authors Quansen Wang, Yuefan Wu, Doudou Wang, Genyuan Xu, Jiawei Liang, Qiang Zhang, Yongmin Li

Plasma Physics

DKEKAN: A single-parameterized KAN surrogate for Drift Kinetic Equation Toward Fast Neoclassical Toroidal Viscosity Torque Modeling in Tokamaks

Jinpeng Huang, Xingting Yan, Mingyu Zhang et al. (+5 more)

The neoclassical toroidal viscosity (NTV) torque is a critical driver of toroidal rotation in tokamaks, profoundly influencing plasma stability and performance. Consequently, incorporating NTV effects is essential for modern integrated modeling frameworks that aim to self-consistently unify multiple physical processes. However, the high computational cost of NTV modeling precludes its self-consistent integration within such frameworks. This bottleneck arises because NTV calculation requires solving its governing equation–the drift kinetic equation (DKE)–in high-dimensional phase space. To address this issue, this study develops DKEKAN, a single-parameterized Kolmogorov-Arnold Network (SKAN) surrogate for solving DKE, to realize fast NTV modeling in tokamaks. The research process consists of the following steps: Firstly, a large dataset mapping DKE equation parameters to solutions is generated based on first-principle simulations under plasma parameters of the Experimental Advanced Superconducting Tokamak (EAST); Secondly, a surrogate model for solving DKE is developed based on the SKAN framework, which also incorporates a modular expert network design; Finally, the DKEKAN surrogate model is integrated with the NTV modeling framework to realize fast NTV calculation. With its physics-grouped expert layer and SKAN backbone, DKEKAN outperforms the tested MLP, KAN, and neural-operator baselines in overall prediction accuracy, while reducing the standalone DKE-solving time from 35.85s to 3.74s, corresponding to a speedup of approximately 9.6x, and reducing the total coupled NTVTOK runtime from 38.24s to 5.58s, corresponding to an overall speedup of approximately 6.9x. This work effectively overcomes the computational bottleneck in NTV simulations, thus supporting further integrated modeling that incorporates NTV effects.
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All authors Jinpeng Huang, Xingting Yan, Mingyu Zhang, Nana Bao, Zixuan Song, Yuetao Meng, Weiyong Zhou, Youwen Sun

Quantum Physics

Recon?figurable measurement scheme compatible with MDI-QKD and BB84 protocols using single-laser sideband generation

Jaesung Lim, Yonggi Jo, Nam Hun Park et al. (+2 more)

A quantum network can be operated efficiently by switching between measurement-device independent quantum key distribution (MDI-QKD) and BB84 according to the trust level of an intermediate node, since MDI-QKD is immune to all detector side-channel attacks while generally yielding a lower secret-key rate. Here, we present a proof-of-principle demonstration of a recon?figurable measurement scheme compatible with both protocols using a single laser. Two mutually independent weak coherent states (WCSs) are generated from a shared continuous-wave laser via electro-optic phase modulation and subsequent ?first-order sideband fi?ltering. Channel indistinguishability is verifi?ed using two complementary Hong-Ou-Mandel (HOM) measurements: time-resolved coincidence measurements and polarization mismatch scans, both yielding HOM visibilities close to the theoretical limit of 0.5 for WCSs. The measurement module performs a partial Bell-state measurement for MDI-QKD, while rotating a single half-wave plate to 22.5 degree reconfi?gures the same module into a dual-basis polarization analyzer for BB84. The quantum bit error rates measured in both confi?gurations agree well with theoretical expectations, verifying the operational feasibility of the reconfi?gurable receiver at the physical layer. By eliminating the need for active frequency locking while retaining independent sideband tunability, this approach provides a practical foundation for flexible and cost-effective quantum networks.
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All authors Jaesung Lim, Yonggi Jo, Nam Hun Park, Zaeill Kim, Yong Sup Ihn

Superconductivity

Vortex pinning of Ba$_{0.62}$K$_{0.38}$BiO$_3$ investigated by magneto-optical Kerr-effect and magnetization measurements

Soichiro Yamane, Sota Nakamura, Atsutoshi Ikeda et al. (+8 more)

Vortex pinning plays a crucial role in determining properties of type-II superconductors. For example, it governs the irreversible magnetic response as well as dissipation caused by vortex motion. Here, we study vortex pinning in the three-dimensional oxide superconductor Ba1-xKxBiO3 using ultra-high-resolution magneto-optical Kerr effect (MOKE) and detailed magnetization measurements. We find that the zero-field MOKE signal in the superconducting state exhibits a pronounced magnetic-history dependence. This behavior closely resembles the remanent magnetization caused by trapped vortices. Furthermore, we demonstrate that the observed evolution of the MOKE signals is well described by Bean's critical-state model for trapped vortices. Our results establish MOKE as a viable optical and mesoscopic probe of vortex pinning in type-II superconductors, providing a new complementary approach to investigate mixed-state phenomena. We also find that the training-field dependence of the MOKE is linear near zero training field, without any anomalies indicative of spontaneous time-reversal-symmetry breaking in an unconventional superconducting state. Our study defines a clear protocol to distinguish vortex-induced MOKE responses from those associated with a time-reversal-symmetry broken superconducting order parameter.
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All authors Soichiro Yamane, Sota Nakamura, Atsutoshi Ikeda, Dayu Zhai, Siddarth Gorregattu, Xing He, Sudarshan Sharma, Yipeng Cai, Yasutomo J. Uemura, Martin Greven, Shingo Yonezawa

Computational Physics · Cross-listed from cs.LG

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

Tyler King and Timothee Leleu

Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailored to a single hardware family, limiting cross-platform comparison and generalization. We introduce Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic, LLM-guided NAS framework that integrates language models as evolutionary operators to co-optimize accuracy and inference energy. By exposing hardware as a swappable backend with per-platform energy models, physical constraints, and non-ideality simulators, UH-NAS enables fair system-level comparisons across various backends without modifying the search algorithm. Tested on optical MZI hardware, UH-NAS discovers more diverse, robust architectures than conventional baselines while outperforming existing LLM-to-NAS approaches. Additional ablations on architecture robustness under non-idealities and the role of system prompts highlight the importance of architecture-hardware co-design for emerging computing platforms.
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High Energy Astrophysical Phenomena

Constraints on axion-like particles from ultra-high-energy observations of M87 with the HAWC observatory

R. Alfaro, C. Alvarez, A. Andrés et al. (+95 more)

In this work, we perform an indirect search for axion-like particles (ALPs) through their hypothesized mixing with photons in the presence of magnetic fields. ALPs are a well-motivated dark-matter candidate class, and the photon-ALP conversion mechanism provides a unique channel to constrain their mass and coupling constant using very-high-energy gamma-ray observations. The photon-ALP mixing could alter the observed gamma-ray spectrum from extragalactic sources by effectively reducing the apparent attenuation due to extragalactic-background-light absorption. We analyze 7.5 years of data from the High Altitude Water Cherenkov (HAWC) Observatory, targeting the nearby radio galaxy M87. This source is located within the Virgo cluster and is an ideal environment for photon-ALP conversion due to its low redshift and the large-scale, strongly magnetized medium of the cluster. We find no evidence for a photon-ALP conversion signal and, consequently, set constraints on the ALP mass and photon-ALP coupling constant with emission from M87 which are consistent with previous results. Our analysis places competitive constraints on the ALP parameter space, defining an exclusion region in the mass range of approximately $10^{-8}$ to $10^{-6}$ eV for coupling constants above $5\times10^{-12}$ GeV$^{-1}$, complementing previous constraints from other gamma-ray observatories.
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All authors R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J.C. Arteaga-Velázquez, D. Avila Rojas, H.A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, K.S. Caballero-Mora, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, J. Cotzomi, P. Desiati, E. De la Fuente, N. Di Lalla, R. Diaz Hernandez, M.A. DuVernois, J.C. Díaz-Vélez, T. Ergin, C. Espinoza, N. Fraija, S. Fraija, J.A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M.M. González, J.A. González, J.A. Goodman, J. Gyeong, J.P. Harding, I. Herzog, D. Huang, F. Hueyotl-Zahuantitla, A. Iriarte, S. Kaufmann, D. Kieda, A. Lara, W.H. Lee, J. Lee, H. León Vargas, J.T. Linnemann, A.L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, H. Martínez-Huerta, J.A. Matthews, P. Miranda-Romagnoli, P.E. Mirón-Enriquez, J.A. Morales-Soto, E. Moreno, M. Mostafá, M. Najafi, A. Nayerhoda, L. Nellen, M.U. Nisa, R. Noriega-Papaqui, L. Olivera-Nieto, N. Omodei, M. Osorio-Archila, E. Ponce, Y. Pérez Araujo, E.G. Pérez-Pérez, A. Pratts, C.D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, D. Salazar-Gallegos, A. Sandoval, M. Schneider, J. Serna-Franco, A.J. Smith, Y. Son, R.W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I.J. Watson, H. Wu, S. Yu, X. Zhang, H. Zhou, C. de León

Optics

Unidirectional-like Edge Transport Induced by Non-Hermitian Skin Effects

Junyi Rao, Jinting Ding, Qinghua Guo et al. (+2 more)

Non-Hermitian skin effects (NHSEs) enable dramatic boundary accumulation of waves, yet their experimental realization typically demands engineered nonreciprocity or spatially patterned loss. Here we demonstrate theoretically and experimentally that uniform loss provides a simple and previously overlooked mechanism for enforcing unidirectional-like edge transport in photonic crystals (PhCs) that breaks time-reversal symmetry in the presence of nonchiral edge states. Using a core cladding geometry where domains share identical Chern numbers but possess distinct bulk polarizations, we show that uniform loss activates NHSEs that reshape the spectral topology of edge bands, giving rise to point gap windings that dictate a one way propagation. Near field measurements confirm that loss converts intrinsically bidirectional interface states into a unidirectional-like circulation around the entire domain wall, showing excellent agreement with theory. Our results establish uniform loss as a universal and structurally simple route for achieving unidirectional-like wave transport.
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All authors Junyi Rao, Jinting Ding, Qinghua Guo, Jun He, Xiang Ni

Plasma Physics

First Principles Magnetohydrodynamical Theory for the Expanding Box Model: Effects on Expansion-Induced Alfvén Wave Reflection

Sebastián Saldivia, Nicolás Villarroel-Sepúlveda, Sebastián Echeverría-Veas et al. (+2 more)

The Expanding Box Model (EBM) has been widely employed to simulate multiscale plasma phenomena in the expanding solar wind by transforming the MHD equations to a co-moving, non-inertial frame. However, traditional formulations have suffered from historical ambiguity regarding the physical separation between the co-moving and inertial reference frames, primarily arising from a classical approximation of an invariant magnetic field between them. To resolve this inconsistency, we reformulate the EBM from first principles using a fully covariant approach. Here, we model the expanding solar wind frame as an anisotropic expanding spacetime metric, allowing us to incorporate radial acceleration profiles and differential transverse expansion, ensuring that all physical fields are correctly transformed by expansion. We demonstrate that asymmetries identified in previous EBM-MHD literature are direct consequences of neglecting the tensorial scaling of the magnetic field. Our covariant treatment eliminates these residues, restoring symmetry in the co-moving frame. Projecting our system back into the inertial frame clarifies the distinction between local plasma dynamics and plasma expansion, revealing the anisotropy of the Parker spiral as a geometric projection. Furthermore, linear wave analysis using Elsässer variables reveals that plasma expansion induces a low-frequency cutoff and geometric damping. Numerical integration demonstrates that expansion drives the reflection of Alfvén waves, generating counter-propagating modes primarily at low frequencies relative to the expansion rate, while high frequencies converge to the WKB approximation. This provides a consistent foundation for simulations, establishing that expansion can serve as a source of counter-propagating waves necessary to drive solar wind turbulence at low frequencies.
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All authors Sebastián Saldivia, Nicolás Villarroel-Sepúlveda, Sebastián Echeverría-Veas, Felipe A. Asenjo, Pablo S. Moya

Adaptation and Self-Organizing Systems

Self-propulsion in the 1D swarmalator model

Kevin P. O'Keeffe

We study the 1D swarmalator model augmented with self-propulsion. Each swarmalator swims along the ring at a speed $v_0\sin\theta_i$ fixed by its orientation $\theta_i$. Self-propulsion unfolds the static states of the ordinary model into traveling, breathing, split-wave, and chaotic states. Several of these states admit analytic reductions: an exact drifting two-cluster branch with a closed-form stability spectrum, and a four-cluster split-wave ansatz whose active pair reduces, in a constant-orientation approximation, to an Adler equation. Our numerical evidence suggests that the transition to chaos under broad random initial conditions is not caused by local destabilization of the ordered cluster branches, but by basin reorganization among coexisting attractors. The resulting states may serve as qualitative signatures for confined active oscillator arrays.
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High Energy Physics (Experiment)

Measurement of the branching fraction of $D_{s}^{*+}\to e^{+}e^{-}D_{s}^{+}$

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+704 more)

The branching fraction of the electromagnetic Dalitz decay $D^{*+}_{s}\to e^{+}e^{-}D^{+}_{s}$ is measured with an $e^{+}e^{-}$ collision data sample collected by the BESIII experiment at center-of-mass energies between 4.128 and 4.226 $\mathrm{GeV}$, corresponding to a total integrated luminosity of 7.33 $\mathrm{fb}^{-1}$. The measurement yields the branching fraction ${\mathcal{B}(D^{*+}_{s}\to e^{+}e^{-}D^{+}_{s})=(7.28\pm0.61_{\mathrm{stat}}\pm0.31_{\mathrm{syst}})\times10^{-3}}$. The result is consistent with the previous one, with a 2.5-fold improvement in precision. This provides an important input for constraining the parameters of theoretical models and for determining the absolute branching fractions of $D^{*+}_{s}\to \pi^{0}D^{+}_{s}$ and $D^{*+}_{s}\to \gamma D^{+}_{s}$, measured with a relative method.
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All authors BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, Y. Ban, H.-R. Bao, X. L. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, S. X. Du, X. L. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, G. F. Fan, J. J. Fan, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Q. Fang, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, L. Feng, Q. X. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. Gao, Y. N. Gao, Y. N. Gao, Y. Y. Gao, Z. Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, J. D. Gong, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. D. Gu, M. H. Gu, C. Y. Guan, A. Q. Guo, J. N. Guo, L. B. Guo, M. J. Guo, R. P. Guo, X. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, T. T. Han, F. Hanisch, K. D. Hao, X. Q. Hao, F. A. Harris, C. Z. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, Z. M. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, P. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, N. Hüsken, N. in der Wiesche, J. Jackson, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. J. Jiang, X. S. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, L. C. L. Jin, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, X. L. Kang, X. S. Kang, B. C. Ke, V. Khachatryan, A. Khoukaz, O. B. Kolcu, B. Kopf, L. Kröger, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, Q. Lan, W. N. Lan, T. T. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, C. K. Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. L. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. Li, K. L. Li, L. J. Li, Lei Li, M. H. Li, M. R. Li, P. L. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, Shanshan Li, T. Li, T. Y. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. K. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. Li, Y. G. Li, Y. P. Li, Z. H. Li, Z. J. Li, Z. X. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. B. Liao, M. H. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, L. Q. Lin, T. Lin, B. J. Liu, B. X. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. J. Liu, K. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, W. M. Liu, W. T. Liu, X. Liu, X. K. Liu, X. L. Liu, X. Y. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, Z. Y. Liu, X. C. Lou, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. H. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, J. S. Luo, M. X. Luo, T. Luo, X. L. Luo, Z. Y. Lv, X. R. Lyu, Y. F. Lyu, Y. H. Lyu, F. C. Ma, H. L. Ma, Heng Ma, J. L. Ma, L. L. Ma, L. R. Ma, Q. M. Ma, R. Q. Ma, R. Y. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Q. A. Malik, H. X. Mao, Y. J. Mao, Z. P. Mao, S. Marcello, A. Marshall, F. M. Melendi, Y. H. Meng, Z. X. Meng, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, H. Neuwirth, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, C. Normand, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, X. J. Peng, Y. Y. Peng, K. Peters, K. Petridis, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. R. Qi, M. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. H. Qiao, J. J. Qin, J. L. Qin, L. Q. Qin, L. Y. Qin, P. B. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, J. Rademacker, C. F. Redmer, A. Rivetti, M. Rolo, G. Rong, S. S. Rong, F. Rosini, Ch. Rosner, M. Q. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, J. L. Shi, J. Y. Shi, S. Y. Shi, X. Shi, H. L. Song, J. J. Song, M. H. Song, T. Z. Song, W. M. Song, Y. X. Song, Zirong Song, S. Sosio, S. Spataro, S. Stansilaus, F. Stieler, S. S Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, R. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. C. Sun, Y. H. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, J. J. Tang, L. F. Tang, Y. A. Tang, L. Y. Tao, M. Tat, J. X. Teng, J. Y. Tian, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, B. Wang, B. Wang, Bo Wang, C. Wang, C. Wang, Cong Wang, D. Y. Wang, H. J. Wang, H. R. Wang, J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, L. W. Wang, M. Wang, M. Wang, N. Y. Wang, S. Wang, Shun Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, X. N. Wang, Xin Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. J. Wang, Y. L. Wang, Y. N. Wang, Y. N. Wang, Yaqian Wang, Yi Wang, Yuan Wang, Z. Wang, Z. Wang, Z. L. Wang, Z. Q. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, H. R. Wei, F. Weidner, S. P. Wen, U. Wiedner, G. Wilkinson, M. Wolke, J. F. Wu, L. H. Wu, L. J. Wu, Lianjie Wu, S. G. Wu, S. M. Wu, X. W. Wu, Y. J. Wu, Z. Wu, L. Xia, B. H. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, K. J. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, T. D. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, W. H. Yan, W. P. Yan, X. Q. Yan, Y. Y. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, R. J. Yang, Y. Yang, Y. H. Yang, Y. Q. Yang, Y. Z. Yang, Z. P. Yao, M. Ye, M. H. Ye, Z. J. Ye, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, L. W. Yu, T. Yu, X. D. Yu, Y. C. Yu, Y. C. Yu, C. Z. Yuan, H. Yuan, J. Yuan, J. Yuan, L. Yuan, M. K. Yuan, S. H. Yuan, Y. Yuan, C. X. Yue, Ying Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. J. Zeng, Y. J. Zeng, Y. C. Zhai, Y. H. Zhan, S. N. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N. Zhang, P. Zhang, Q. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. P. Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. L. Zhang, Z. X. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Y. Zhang, Zh. Zh. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, L. Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. L. Zhao, Y. P. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, X. R. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, H. Zhou, J. Q. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. X. Zhou, Y. Z. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. X. Zhu, Lin Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, W. Z. Zhu, Y. C. Zhu, Z. A. Zhu, X. Y. Zhuang, J. H. Zou

Atomic and Molecular Clusters

Rainbow RABBITT as a Probe of Coherent Rabi Dynamics

Vladislav V. Serov and Anatoli S. Kheifets

Attosecond pulse trains interacting with a resonantly dressed atom generate a pronounced intra-sideband phase structure that remains hidden in conventional spectrally integrated RABBITT measurements. Using ab initio time-dependent Schrödinger equation calculations for lithium near the resonant $2s\to2p$ transition, we show that the phase extracted within a single sideband can vary by nearly $\pi$ across its spectral width. The resulting intra-sideband phase dispersion exhibits a characteristic dependence on the IR detuning, pulse duration, intensity, and sideband order. Most strikingly, exact resonant Rabi flopping flattens the intra-sideband phase dispersion, whereas a small detuning generates a pronounced phase modulation despite weaker population transfer. This counterintuitive behavior demonstrates that rainbow RABBITT probes the dynamical phase accumulated by a Rabi-dressed wave packet rather than the instantaneous populations of the participating states. A simple analytical model captures the principal features of the numerical calculations and provides physical insight into the emergence of the intra-sideband phase structure. These results establish intra-sideband phase dispersion as a new interferometric observable for mapping coherent Rabi dynamics.
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Astrophysics of Galaxies

Multiphase images of a powerful supernova-driven wind in the early Universe

Rebecca L. Davies, D. B. Fisher, R. Herrera-Camus et al. (+38 more)

Galactic winds are considered a likely driver of rapid quenching in early massive galaxies, but until now there has been no direct evidence that such systems drive winds powerful enough to meaningfully suppress their star-formation. We present resolved cold gas and ionized gas observations of a powerful supernova-driven wind in a massive galaxy 1.1 billion years after the Big Bang (at $z$=5.3). The outflow, likely triggered by ongoing merger activity, is removing gas at twice the rate of star-formation and could plausibly eject all the cold gas from the galaxy within 100 Myr. Our results suggest that powerful merger-driven outflows may be a key mechanism to produce abundant massive quiescent galaxies in the early Universe when a large fraction of massive galaxies are interacting. The mass and energetics of this distant outflow are consistent with nearby starburst-driven superwinds, suggesting that the efficiency of stellar feedback has remained relatively constant over the last 12 billion years of cosmic history.
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All authors Rebecca L. Davies, D. B. Fisher, R. Herrera-Camus, A. Faisst, J. Spilker, J. González-López, S. Fujimoto, R. Amorín, M. Aravena, R. J. Assef, L. Barcos-Muñoz, M. Boquien, M. Dessauges-Zavadsky, A. Ferrara, N. M. Förster Schreiber, M. Ginolfi, D. Gómez-Espinoza, E. Ibar, R. Ikeda, H. Inami, G. C. Jones, A. Koekemoer, L. L. Lee, J. Li, D. Liu, Z. Liu, I. De Looze, I. Mitsuhashi, J. Molina, A. Nanni, M. Relano, M. Romano, P. Sawant, M. Solimano, L. Sommovigo, K. Tadaki, K. Telikova, H. Übler, V. Villanueva, W. Wang, G. Zamorani

Applied Physics

Fast-Neutron Irradiation Effect in Heteroepitaxial $\beta$-Ga$_2$O$_3$ Schottky Diodes Fabricated on Low-Cost Sapphire Substrates

Saleh Ahmed Khan, Ahmed Ibreljic, Sourav Sarker et al. (+2 more)

In this work, we investigate the response of Ni/$\beta$-Ga$_2$O$_3$ Schottky barrier diodes fabricated on c-plane sapphire to fast-neutron irradiation up to a fluence of $1\times10^{15}$ n$\cdot$cm$^{-2}$. The LPCVD-grown heteroepitaxial structure consists of an unintentionally doped buffer, an n$^{+}$ contact layer, and an n-type drift layer, with mesa isolation realized by plasma-free Ga-assisted LPCVD etching. Prior to irradiation, the devices exhibit a turn-on voltage of 1.20 V, specific on-resistance of 8.43 m$\Omega\cdot$cm$^2$, ideality factor of 1.32, and Schottky barrier height of 1.29 eV. Following irradiation, the devices remain operational, although the forward current decreases, the turn-on voltage increases to 2.40 V, and the barrier height increases to 1.34 eV. Capacitance-voltage measurements reveal a $\sim$50% reduction in net donor concentration, corresponding to a carrier-removal rate of $\sim$105 cm$^{-1}$. Temperature-dependent measurements from 25 to 250 $^\circ$C confirm that thermionic emission remains the dominant transport mechanism and show significant suppression of reverse leakage current after irradiation. The breakdown voltage increases from 101 to 135 V, consistent with neutron-induced donor compensation. TCAD simulations show a more uniform electric-field distribution and reduced field crowding at the Schottky edge after irradiation. These results provide insight into neutron-induced donor compensation in heteroepitaxial $\beta$-Ga$_2$O$_3$ and demonstrate the ability of LPCVD-grown $\beta$-Ga$_2$O$_3$ Schottky diodes on sapphire to maintain stable operation under high-fluence neutron environments relevant to space and nuclear electronics.
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All authors Saleh Ahmed Khan, Ahmed Ibreljic, Sourav Sarker, Stephen Margiotta, Anhar Bhuiyan