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Physics Physics

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Friday 9 October

Instrumentation and Detectors

Beam Spill Structure Measurements at CEE Using the T0 Detector

Shuaike Lv, D. Hu, Dayu Yin et al. (+29 more)

The Cooling Storage Ring External-target Experiment (CEE) at the Heavy Ion Research Facility in Lanzhou uses a slow-extracted heavy-ion beam whose temporal profile directly affects detector occupancy. The existing CEE T0 detector, originally designed to provide the event start time, is extended in this work to measure the spill structure immediately upstream of the fixed target. The detector consists of a 0.8-mm-thick EJ-200 plastic scintillator read out by 16 silicon photomultipliers at each end. The SiPM signals are conditioned and discriminated by the NINO front-end electronics, and the resulting timing signals are recorded using an FPGA-based time-to-digital converter. In laboratory tests, with the laser trigger set to its maximum available repetition frequency of 100~MHz, the recorded T0 hit-rate distribution was concentrated around 100~MHz. During the June 2024 beam test with 350-MeV/u Kr ions, the accelerator team varied several operating settings, including RF-KO, sextupole, and feedback settings, while T0 independently recorded the corresponding spill profiles. An empirical representation was used to extract the rise, plateau, and fall durations from the stored profiles. The plateau duration was used as the primary comparison metric while accelerator stability and RF-system load were also considered. The measurements clearly distinguished the spill-profile responses associated with different accelerator settings. The spill obtained with the selected settings exhibited a more nearly rectangular envelope than the initial profile, although residual fluctuations and a terminal spike remained. These results demonstrate that the existing CEE T0 detector can provide independent beam-structure information during accelerator tuning.
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All authors Shuaike Lv, D. Hu, Dayu Yin, Haoqian Xu, Herun Yang, He Zhao, JiaJun Qin, Junfeng Yang, Juan Ruan, Jie Li, Ke Sun, Lei Zhao, Lijun Mao, M. Shao, Min Li, Meitang Tang, Shiya Huang, Tong Liu, Tingting Hou, Weilong Li, Wenwen Ge, XiangLun Wei, Yuhong Yu, Yingjie Zhou, Yiding Zhao, Yapeng Zhang, Zhoubo He, Zhijie Li, Zixuan He, Ziyang Wei, Zhiyu Sun, Zhengyang Sun

Optics

Matrix-free numerical continuation of the Ikeda map in passive Kerr resonators

Yuandi Xu, Pengxiang Wang, Jianxing Pan et al. (+2 more)

We introduce a matrix-free numerical continuation framework for the Ikeda map of coherently driven passive Kerr resonators. The framework traces solution branches and evaluates their linear stability without explicitly assembling the full Jacobian matrices, using a unified formulation for states that recur after one or multiple round trips. We resolve stable and unstable branches of modulation-instability patterns and temporal cavity solitons, and uncover period-doubled homogeneous states and associated multi-round-trip dynamics that are absent from the standard Lugiato-Lefever equation (LLE). For period-1 states, the Ikeda map and the LLE show close agreement in the high-finesse regime, while their branch-existence intervals increasingly deviate as the finesse decreases. These results demonstrate that direct continuation of the unaveraged round-trip map can capture nonlinear states and multi-round-trip dynamics beyond the mean-field description, providing a systematic framework for bifurcation and stability analysis of Kerr resonators without relying on the averaged LLE.
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All authors Yuandi Xu, Pengxiang Wang, Jianxing Pan, Haisu Zhang, Gang Xu

Chemical Physics

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

Qiao Gao, Yang Huang, Enze Hou et al. (+3 more)

Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schrödinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring separate calculations to map out a potential energy surface. We introduce NucleoNet, a nuclear-aware determinant ansatz in which electrons and nuclei form a unified particle set propagated through a shared electron–nucleus backbone that jointly evolves one-particle and two-particle representations. Nuclei carry learned representations that evolve alongside the electronic representations and condition orbital construction, while antisymmetry is imposed only by electronic determinants. On equilibrium small-molecule benchmarks, NucleoNet achieves accuracy comparable to established neural-wavefunction baselines with substantially fewer parameters. For each molecular system, a single model optimized jointly across geometries describes the N$_2$ dissociation curve, Li displacement in LiCN, and the ethane and formamide minimum-energy paths for bond dissociation. Sparse-geometry training generalizes to unseen geometries for N$_2$ and LiCN without further optimization, and N$_2$ ablations show that nuclear streams substantially improve this interpolation. These results show that a compact wavefunction with jointly evolved nuclear representations can describe the geometries of a molecular system with a single set of parameters.
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All authors Qiao Gao, Yang Huang, Enze Hou, Yuzhi Liu, Jingrun Chen, Han Wang

Optics

Silent breakdown of the perturbative polarization expansion in the terahertz regime

Yao Lu, Jiawei Zhang, Qiang Wu et al. (+1 more)

Nonlinear optics expands the polarization in powers of the drive and retains only the lowest-order terms. This truncation is safe at optical frequencies, where the convergence field lies far above the damage threshold. In the terahertz regime, phonon polaritons enhance the nonlinear susceptibilities by orders of magnitude, lowering the convergence field into the sub-damage range and opening a window in which the expansion fails while the crystal remains recoverable. We term this window silent breakdown. At resonance, the nearest singularities of the driven lattice mode are a complex-conjugate pair of branch points below the multistability threshold, so the response remains smooth and single-valued even though the $\chi^{\left( n \right)}$ description fails. The convergence field scales as $Q^{-3/2}$, so sharpening a resonance enhances the nonlinearity but narrows the range over which the expansion applies. It follows from the mode shift $S$ alone, $E_{\mathrm{conv}}=E_{\mathrm{ref}}(3\sqrt{3}QS)^{-1/2}$, giving 2$\sim$9 MV/cm for $\mathrm{LiNbO}_{3}$. Since resonant enhancement is central to terahertz nonlinear optics, this boundary defines the field range over which susceptibility-based descriptions are quantitatively reliable.
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All authors Yao Lu, Jiawei Zhang, Qiang Wu, Jingjun Xu

Optics · Cross-listed from quant-ph

Quantum Information-Theoretic Analysis of Optical Fiber Sensing via Stimulated Brillouin Scattering

Kiran Adhikari and Janis Nötzel

Stimulated Brillouin scattering (SBS) is a powerful mechanism for distributed sensing in optical fibers, as the Brillouin response is highly sensitive to local material parameters. Assuming the input probe is a quantum state, we develop a quantum-information-theoretic framework for SBS-based fiber sensing and intrusion detection. Starting from a linearized opto-acoustic Hamiltonian, we derive an effective input-output relation for a short fiber segment. We then model each fiber segment as a single-mode bosonic Gaussian channel and the entire fiber as a cascade of quantum channels. We then compute the quantum Fisher information for general SBS-dependent parameters and identify optimal measurement settings to achieve the quantum limit, comparing them with receiver-constrained strategies such as heterodyne detection and photon counting. In addition, we propose an adaptive approach that achieves first-order asymptotic optimality and provides a novel second-order correction to address preliminary miscalibration. As an application, we apply the framework to the fiber-tapping detection problem.
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Instrumentation and Detectors

CNN-based Position Reconstruction for the T0 Detector in the HIRFL-CSR External-target Experiment

Shuai-Ke Lv, D Hu, M Shao et al. (+2 more)

The T0 detector provides the event start time for the HIRFL-CSR external-target experiment, and its multi-channel readout signals also contain 2D position information of incident particles. In the CEE experiment, the dedicated beam-profile detector has a rate capability of only ~1 kHz, far below the beam event rate of up to 1 MHz, so it cannot provide real-time beam-spot monitoring under high-rate data-taking or an event time reference. To fill this gap, this work studies a CNN-based position reconstruction method that enables the T0 detector to provide both start time and beam-spot information. The over-threshold times and time-over-threshold (TOT) values from 32 SiPM channels are reorganized into a geometry-preserving input tensor. Time features are corrected by event-wise mean-time subtraction, and TOT features are normalized by an event-wise trimmed mean before feature standardization. A customized CNN directly regresses the incident coordinates. Training and validation samples are built with Geant4 simulation and waveform digitization, and the method is evaluated using a pulsed-laser scanning data set with known position labels. On independent test sets, the residual standard deviations for uniformly distributed planar simulation data are about 1.11 and 1.04 mm in x and y. For the discrete laser-scanning geometry, the resolutions are about 2.36 and 1.34 mm in simulation and 2.42 and 1.41 mm in experiment. The trained model is applied to CEE zero-field beam data collected at the Institute of Modern Physics, Lanzhou, in January 2026, and a 2D beam-spot distribution is reconstructed. These results show the T0 detector's potential as a high-rate beam-spot monitor while retaining its role as an event start-time detector.
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All authors Shuai-Ke Lv, D Hu, M Shao, Yingjie Zhou, Yiding Zhao

Instrumentation and Detectors

Application and Optimization of the GET Electronics System for Double-Sided Silicon Strip Detector Readout

Jiawei Bian, Zaihong Yang, Qite Li et al. (+7 more)

Double-sided silicon strip detectors (DSSDs) are widely used for charged-particle detection in nuclear reaction experiments. However, the large number of readout channels associated with DSSDs places stringent demands on front-end electronics and data acquisition systems, particularly in experiments employing large detector arrays. In this work, we investigate the application and optimization of the Generic Electronics for Time Projection Chambers (GET) system for multi-channel DSSD readout. Through optimization of the DC-blocking capacitors on the custom-designed interface board and waveform-based energy extraction algorithms, the readout system achieves an energy resolution of {\sigma} = 22 keV for 5.49 MeV alpha particles. Its performance is further validated in a representative nuclear physics experiment employing charged-particle telescopes. The results demonstrate that the GET system provides a versatile and scalable waveform-sampling solution for multi-channel DSSD readout.
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All authors Jiawei Bian, Zaihong Yang, Qite Li, Zeyu Du, Xuan Wang, Ningtao Zhang, Pengjie Li, Zhongtao Shen, Kaijie Zhou, Cheng Wang

Computational Physics

Alter-excitons in Altermagnets: Symmetry, Splitting and Dispersion

Yuanfan Xiong, Liangyu Wang, Xuan Wu et al. (+2 more)

Magnetism provides a fertile platform for discovering emergent quantum phenomena governed by symmetry. Recent developments of spin-space-group (SSG) theory have revealed altermagnets as a distinct class of nonrelativistic collinear magnets with unconventional spin-dependent electronic structures. Here, we demonstrate that altermagnetic symmetry imposes fundamentally different constraints on electronic and excitonic excitations. Although the quasiparticle and two-body Hamiltonians possess identical SSG symmetries, we find that symmetry-protected degeneracies present in the electronic band structure can be intrinsically lifted in the exciton spectrum at specific momenta. This splitting arises solely from the many-body nature of excitons and does not require spin-orbit coupling or noncollinear magnetic order. Combining symmetry analysis with many-body perturbation theory, we predict a novel class of spin-split excitonic quasiparticles, termed alter-excitons, and reveal their characteristic dispersions in altermagnetic systems. Our results establish excitons as a sensitive probe of hidden spin-space symmetries and uncover a new route toward symmetry-engineered many-body phenomena in collinear magnets.
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All authors Yuanfan Xiong, Liangyu Wang, Xuan Wu, Wei Hu, Jinlong Yang

Computational Physics

All-electron RT-TDDFT with Exact Exchange based on Numeric Atom-centered Orbitals for Condensed Matter

Jianhang Xu, Christopher Shepard and Yosuke Kanai

Real-time time-dependent density functional theory (RT-TDDFT) has become a widely used first-principles method for studying excited-state and nonequilibrium electron dynamics. Hybrid exchange-correlation (XC) functionals, which incorporate a fraction of nonlocal exact exchange, are particularly important for accurately describing processes such as charge transfer and hot-carrier dynamics. However, the high computational cost which accompanies exact exchange, as required in hybrid XC functionals, has largely restricted RT-TDDFT simulations of condensed matter systems to using semi-local XC functionals. In this work, we present an all-electron implementation of exact exchange for RT-TDDFT based on numeric atom-centered orbitals, extending an efficient ground-state resolution-of-identity (RI) framework to the complex, time-dependent Hamiltonians required for real-time propagation. We validate the implementation against an established RI approach, for an isolated water molecule, finding exact agreement for both valence and core (K-edge) excitations. We also demonstrate the method on two extended periodic systems, crystalline silicon and silicon carbide. For silicon, the HSE06 spectrum reproduces previously reported real-time TDDFT results and agrees with experiment to within approximately 0.2~eV, consistent with plane-wave pseudopotential implementations. For silicon carbide, we compute valence and X-ray absorption spectra at the silicon $L$-edge and carbon $K$-edge. These results establish an efficient, all-electron route to hybrid-XC functional RT-TDDFT for both molecular and condensed matter systems.
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Optics

Topologically steerable ultra-flat electro-optic combs via non-Hermitian frequency lattices

Weiwei Liu, Xiaolong Su, Bing Wang et al. (+7 more)

Electro-optic (EO) frequency combs convert microwave signals to the optical domain with deterministic repetition rates, enabling applications in optical telecommunications, microwave photonics, and LiDAR. However, generating flat EO combs has remained a long-standing challenge due to exponential decay of the spectral envelope inherent to bidirectional frequency coupling within conventional schemes. Here we reveal the inherent correspondence between flat-comb generation and the topological energy bands of non-Hermitian nonreciprocal frequency lattices, and experimentally realize ultra-flat combs with topologically controlled spectral flow. We derive the exact analytical condition for flat-comb generation, and find that it maps precisely onto the complex energy band, thereby establishing a direct connection between comb-envelope engineering and non-Hermitian topology. Experimentally, we observe an ultra-flat comb whose frequency evolution direction is governed by the topological winding and remains robust against detuning and phase fluctuations. This scheme can be further extended to long-range coupling and multi-lattice architectures, underscoring its versatility for flat dual- and multi-comb generation. Our work establishes a topologically steerable approach for achieving ultra-flat EO combs, paving the way for chip-scale spectral engineering via non-Hermitian topology.
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All authors Weiwei Liu, Xiaolong Su, Bing Wang, Yongjie Wang, Pang Fu, Wenxian Zhang, Ruiqian Cheng, Songpan Hong, Qingbin Zhang, Peixiang Lu

Computational Physics · Cross-listed from math.NA

Riemannian Hessian analysis of Sobolev gradient flows for the Gross–Pitaevskii ground state problem

Shixin Zheng and Xiangxiong Zhang

Sobolev gradient flows for the Gross–Pitaevskii ground state problem are Riemannian gradient descent methods on the sphere under a metric induced by a discrete elliptic operator. We analyze their convergence through the condition number of the Riemannian Hessian at the ground state. Bounds of the Rayleigh quotient of the Riemannian Hessian give a uniform analysis of these methods for different metrics. The uniform analysis applies to the classical metrics, such as the shifted Laplacian and the energy-adaptive metrics. Inspired by this analysis, we also consider three new metrics. One of them is a Thomas–Fermi metric, whose condition number is bounded uniformly in the interaction strength for the one-dimensional harmonic trap and whose iteration count does not grow in the Thomas–Fermi regime in the numerical tests. The performance suggested by our analysis is consistent with three-dimensional numerical tests with up to one billion unknowns, for both Riemannian gradient descent and Riemannian conjugate gradient methods, whose practical performance is also related to the Hessian condition number. The Thomas–Fermi flow needs the fewest iterations, while the metrics inverted exactly by tensor-product solvers are the fastest in computational time on one GPU card.
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Thursday 8 October

Fluid Dynamics

CFD Forge: A Physics-Constrained Agentic Framework for Autonomous CFD

Aakash Shailesh Oza, Ryan F. Johnson and Amir Barati Farimani

Setting up, running, and assessing a computational fluid dynamics (CFD) simulation requires substantial manual effort, from meshing and solver setup to diagnosis and interpretation, and repeating it across design variants slows engineering iteration. We present CFD Forge, an agentic framework built around OpenFOAM that carries an engineering request through case construction, meshing, solver execution, diagnostics, and reporting. A large language model interprets the request, reads the simulation evidence, identifies likely causes of problems, and proposes actions from a closed set. Automation is only useful if its results can be trusted, so acceptance is not left to the model: deterministic checks, registered for each type of flow, measure explicit criteria and decide. We demonstrate the framework on a converging-diverging nozzle and a two-dimensional forward-facing step, including geometry and operating changes requested in text. A turbulent cube, run outside the agent loop, shows why the checks matter: the solver completed and the mean drag settled, but the lateral-force oscillations were still growing, so a retrospective assessment withheld acceptance. To test this separation, we replayed 36 recorded simulation states, some with deliberately altered diagnostics, through the same model in two setups. Judging from the evidence without the check results, the model accepted 12 of the 13 detectable faults in all three repeats; with CFD Forge's checks deciding, none was accepted. The two setups also differ in prompting, and a fixed rule reached the same verdicts, so the model's value lies in the open-ended work rather than the verdict. Case files, recorded model calls, and decisions are retained for audit. A model that does the work, paired with checks that decide what is accepted, is a step toward automated CFD that can be trusted in high-throughput design and active-learning loops.
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Data Analysis and Statistics · Cross-listed from q-bio.NC

Energy, Information, and the Limits of Neuropsychiatric Classification

Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi et al. (+15 more)

Importance: The search for reliable neuroimaging biomarkers of mental disorders has remained elusive despite decades of research. Why clinically consequential disorders yield weak neuroimaging separability remains unresolved. Drawing on statistical thermodynamics and the metabolic costs of neural coding, we propose that psychiatric disorders produce constrained deformations of a largely shared repertoire of brain activity patterns, limiting the class-specific information available for classification. Observations: We develop a framework linking physical resource constraints, landscape deformation, distributional separation, and attainable classification, and examine its components using human neuroimaging, synthetic data, and an exactly specified physical model. In resting-state fMRI from 1952 FOR2107 participants (823 with major depressive disorder; 1129 healthy controls), held-out energy-landscape estimates and nonlinear classifiers agreed across 13 comparisons (r = 0.996). Similar results emerged at both FOR2107 centers and in 900 SRPBS participants spanning multiple psychiatric diagnoses. In synthetic data, classification improved with distributional separation and approached the optimum. In an equilibrium three-well model, statistical energy equaled physical potential energy scaled by inverse temperature. Modest deformation redistributed state occupancy while optimal snapshot classification remained weak. A literature map found insufficiently comparable evidence to test the human metabolic constraint. Conclusions: Clinically consequential differences may arise within a largely shared neural repertoire. The thermodynamic framework connects physical deformation to classification under specified assumptions; its metabolic implementation in humans remains untested. Testing this mechanism requires joint measurements of brain activity, bodily physiology, and metabolism.
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All authors Farbod Khodadadi, Mina Kheirkhah, Bita Shariatpanahi, Sarah Alizadeh, Madleen Stenger, Amaia Zurinaga Gutierrez, Shakiba Bakhshian, José C. García Alanis, Nils R. Winter, FOR2107 Consortium, Frank Bremmer, Stefan G. Hofmann, Udo Dannlowski, Tilo Kircher, Sharmili Edwin Thanarajah, Igor Nenadić, Tim Hahn, Hamidreza Jamalabadi

Fluid Dynamics · Cross-listed from math.HO

Philip L. Roe, the British Scientist. The Cranfield Years and Beyond

Eleuterio F. Toro

Philip L. Roe, the renowned British scientist, passed away on April 26, 2026, at the age of 87, while Professor Emeritus at the University of Michigan. My association with Phil Roe began in 1984, when he joined the Cranfield Institute of Technology, UK, as Professor of Computational Fluid Dynamics, while I was a postdoctoral research fellow at the same institution. For more than four decades, his incisive ideas, physical principles and distinctive approach to research on numerical methods for solving hyperbolic partial differential equations have had a lasting influence on my own approach to research in the field. Here I recount some personal memories and anecdotes emerging from the rewarding experience of interacting with Phil during the Cranfield years and beyond. I also review, quite succinctly in places, some of the main research topics that he pursued throughout much of his academic career. These notes are intended as a modest homage to Roe's impact on the subject, as well as to his influence on the careers of generations of scientists in both academia and industry.
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Optics

Phantom Zernike Wavefront Sensor: Phase Contrast with Engineered Focal Plane Reference Speckles

Benjamin Courtney-Barrer

We introduce the Phantom Zernike Wavefront Sensor (phantom ZWFS), an engineered reference extension of phase-contrast wavefront sensing in which controlled pupil plane modulation generates off-axis coherent speckle structures that undergo localized focal plane phase shifts. These references may replace the central ZWFS reference when the on-axis PSF core is required for another optical function, such as photonic science coupling, or may augment a conventional central ZWFS response to provide additional wavefront information. Using a monochromatic scalar model, we derive the resulting carrier structure and show how the engineered references can be recovered through spatial demodulation and incorporated into calibrated reconstruction. A simple four-reference realization demonstrates that the reference geometry redistributes photon sensitivity across spatial frequency magnitude and orientation, producing mode dependent changes in linearity and cross-talk rather than a uniform improvement over a classical ZWFS. We then consider differential piston on a seven segment aperture as an application specific example. A hybrid sensor retaining the central ZWFS phase region and adding six engineered references that match the segment geometry gives similar precision near the phased state but substantially improved performance at larger piston excursions. Under the adopted photon-noise-limited benchmark, the hybrid reduces the nonlinear reconstruction error and provides better conditioned optical encoding of the piston state than an idealized vector ZWFS, allowing more precise piston estimation from the same detected photon flux at larger piston excursions.
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Computational Physics

Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization

S. Ares de Parga, A. Cornejo, J. A. Hernández et al. (+1 more)

Approximating the effective anisotropic hyperelastic response of a representative volume element (RVE) requires constitutive expressiveness, mechanical consistency, and computational efficiency. This work develops a polyconvex physics-augmented neural network (PANN) construction based on paired directional features whose orientations, stretch powers, and determinant exponents adapt to the RVE response. The features are composed with convex, componentwise nondecreasing cores, realized as an input-convex neural network (ICNN) or an input-convex Kolmogorov-Arnold network (ICKAN). The complete energies satisfy objectivity, an exactly stress-free reference, two-dimensional polyconvexity, and prescribed growth conditions by construction, and nonnegative energy is certified separately for the selected models. On a multicavity and a single-cavity periodic cell, these energies are compared with an Unconstrained energy, trained identically without the curvature and growth constraints, and with three hyperreduced projection-based reduced-order models (HPROMs), which serve as physics-based references. On the first cell, learning the features lowers the stress error sevenfold, and six features suffice on both. The constrained energies remain below one percent in stress error and in the displacement error of a tensile-coupon FE$^2$ application with a full-order reference, whereas the Unconstrained energy and the HPROMs are more accurate inside the data. Beyond the data, however, the Unconstrained energy loses rank-one convexity, and under confined compression it stops below half of the 500 MPa that the constrained energies reach. These results quantify what the guarantees cost in accuracy and what they secure beyond the data.
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All authors S. Ares de Parga, A. Cornejo, J. A. Hernández, R. Rossi

Physics and Society · Cross-listed from cs.SI

Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes

Phillip W. Kornberg and Kevin S. Xu

The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads to a teammate scoring. Attributing credit solely to the player who makes the final pass before a goal fails to account for the contributions of multiple players in the buildup to the goal. Consequently, traditional statistics provide only a limited view of player involvement. In this paper, we use multivariate Hawkes processes (MHPs) to model timestamped sequences of passes and shots, which form continuous-time networks. MHPs provide a branching mechanism where an event (a pass or shot) can be generated as a child of a previous event, which allows us to quantify each pass's contribution to a subsequent pass or shot. This approach distributes credit across multiple passes within an attacking sequence, making it applicable across a wide range of situations. Building on this framework, we propose expected goal involvements (xGI), a metric that measures a player's involvement in creating goal scoring opportunities based on their passes. This metric identifies players who play key roles in creating goal scoring opportunities even if they do not make the final pass, offering a more comprehensive measure of attacking involvement and player performance.
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Medical Physics

An anatomically grounded human-scale dynamic model of contraction-driven lymphatic pumping

Chia-Feng Chang, Ayemon Baraka, Egor Sidorov et al. (+1 more)

Lymphangions, the valved muscular segments of lymphatic vessels, pump lymph against an adverse pressure gradient. Current models resolve this pumping in one vessel, a short chain or a regional network, and whole-body human models are passive. We extend the passive whole-body network of a companion static study to the active regime on the same anatomical graph, with intrinsic contraction, dynamic valves and a respiration-gated venous outlet on every vessel. Without fitting the waveform or the contraction amplitude, the network settles into a bounded pumping cycle at the lymphovenous junction, at a clinical pressure of 7.5 to 12.3 mmHg and a drainage of 35 to 50 mm^3/s. Respiration adds a swing of 1 to 2.5 mmHg at the junction and contraction about 1 mmHg inside the duct. Pressure generation is limited more by load than by drive, and no single contraction amplitude fits every vessel. Raising the subclavian venous pressure from 3 to 15 mmHg lifts the deep cervical node from 8.6 to 21.1 mmHg at the weakest drive, a transmission of 1.04 that falls to 0.78 at the strongest, so contraction buffers cervical drainage against venous pressure. The same solver gives the human cervical outlet pressure that cerebrospinal-fluid drainage models prescribe.
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All authors Chia-Feng Chang, Ayemon Baraka, Egor Sidorov, Sabee Molloi

Atmospheric and Oceanic Physics

Parametric wave spectrum models for prediction of Stokes drift profiles

W. Erick Rogers, Yalin Fan, Luis Zamudio et al. (+1 more)

In this report, 15 variants of nine parametric models for non-directional ocean wave spectra are reviewed. They are compared against observation-based spectra created by taking spectra measured by moored buoys, binning by wind speed, and averaging. Surface Stokes drift (SSD) and Stokes drift profiles are computed using the model and observation-based spectra, and these are compared. This 'parametric wave model' strategy for estimating Stokes drift is contrasted with other methods for estimating Stokes drift, such as using spectra from a dynamical wave model. Advantages and shortcomings are reviewed. One practical application for Stokes drift profiles is in ocean circulation modeling. Here, the information is used to compute wave-enhanced mixing ('Langmuir turbulence'). We present examples of such cases, in the context of a global ocean model, tested in two modes (coupled and uncoupled) and with two types of forcing (Stokes drift approximated using wind speed and Stokes drift computed using a full ocean wave model). A more comprehensive review of the uncoupled tests with the full ocean wave model can be found in a separate report, Fan et al. (2026). We evaluate simple Stokes drift models that use wind speed and/or wind stress but do not require a dynamical wave model. We find some are sufficiently accurate for routine prediction of SSD. A subset of these models can also predict Stokes drift profiles (via parametric spectra), but none are accurate in this role. The SSD values computed from a very large drifting buoy dataset are compared with the total drift calculated using positional change of the same buoys. The SSD is roughly 25% of the total drift, consistent with expectations based on 'rules of thumb'. However, the scatter is large, indicating that such rules have predictive skill only in the mean sense.
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All authors W. Erick Rogers, Yalin Fan, Luis Zamudio, Elizabeth M. Douglass

Medical Physics

An anatomically grounded human-scale steady hydraulic model of the lymphatic network

Chia-Feng Chang, Ayemon Baraka, Egor Sidorov et al. (+1 more)

The lymphatic system returns 2 to 3 L of interstitial fluid per day to the venous circulation and drains the central nervous system. Most models of lymph transport describe one lymphangion or a short chain, so they cannot show how body-wide topology, calibre hierarchy and the venous outlet set pressure and flow. We build a passive steady hydraulic model on an anatomically derived graph of the human lymphatic system, with 1010 vertices and 1126 edges expanded to 89,060 sub-edges. Radii follow a Murray cube law, derived both from minimum work and from wall-shear-stress homeostasis. Pressure and flow follow from nodal hydraulic balance over Hagen-Poiseuille conductances, with an exponential wall law, hysteretic valves, Starling leak, node retention and a Windkessel outlet. Active contraction is off, and only the inflow scale is calibrated. Two quantities that are not fitted land in clinical range at the same operating point. Radii fall inside the vessel-class range, from 1.50 mm in the thoracic duct to 0.16 mm in the cervical collectors, and pressures stay below the 50 mmHg cuff scale on all sub-edges. About 49% of the gross load is removed before the outlet, nearly independent of the outflow scale and of the outlet parameters. On the route that drains the central nervous system, 30% of the superficial cervical load reaches the vein, at 10 to 18 mmHg along a segment that cervical-scale models replace with a prescribed outlet pressure. The model is a passive whole-body baseline for contraction-driven and clinical extensions.
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All authors Chia-Feng Chang, Ayemon Baraka, Egor Sidorov, Sabee Molloi

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

Predicted state-to-state collisional rate coefficients from neural networks

Lisán David Cabrera-González, Carlos Cárdenas and Otoniel Denis-Alpizar

Molecular spectra observed in the interstellar medium are commonly analyzed using non-local thermodynamic equilibrium models, which requires accurate state-to-state collisional rate coefficients. Close-coupling calculations become prohibitively expensive for transitions from highly excited rotational states, therefore available datasets are often truncated at low initial states and temperatures; and since each new system also requires an accurate ab initio potential energy surface, many detected molecules have no collisional data. We employ two machine learning strategies to predict rotational state-to-state rate coefficients of molecules included in the train set beyond the range of initial states and temperatures computed so far, and predicting those of a molecule absent from training and validation sets. Feed-forward neural networks are trained on state-to-state de-excitation rate coefficients of 24 linear molecules colliding with para-H2. Scheme 1 includes all systems in the train+validation set, with 70%, 80%, and 90% initial-state boundaries and a 90% temperature boundary. Scheme 2 is trained on 22 systems described only by transition and molecular properties, and predicts two excluded systems, CS and CF+, whose astrophysical impact is explored with non-LTE calculations. For the 90% boundary, Scheme 1 predicts 99.4% of the test transitions within a factor of 2, and no prediction deviates from the reference value by more than a factor of 4.7. This model was used to complete 18 datasets. Scheme 2 reproduces the rate coefficients of two excluded systems within a factor of 3 for more than 92% of their transitions. Both schemes provide near-instantaneous complements to close-coupling calculations: Scheme 1 extends the range of a molecule already included in the training set with high accuracy, while Scheme 2 gives a reasonable first estimate for a molecule that has not been studied.
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Chemical Physics

Predictive Synthesis of MOFs and COFs

Bin Feng, Reid A. Coyle, Shyam Chand Pal et al. (+3 more)

Predictive synthesis concerns the prospective modeling of how a targeted structure can be realized. The synthesis of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) intrinsically involves a multidimensional space of interdependent variables, posing a challenge for bench chemists to parameterize and predict. In this Perspective, we discuss the evolution from chemical-intuition-driven experimentation toward the integration of artificial intelligence (AI) and machine learning (ML) in the laboratory. By virtue of prior knowledge, uncertainty, and experimental failure in determining what should be attempted next, we envision predictive synthesis progressing from static recipe recommendation toward real-time control and transferable principles of framework formation. In this context, the significance lies in transforming trial-and-error practice into a means of measurable chemical reasoning, thereby accelerating crystalline materials discovery and advancing synthetic chemistry.
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All authors Bin Feng, Reid A. Coyle, Shyam Chand Pal, Peter Walther, Saeun Park, Zhiling Zheng

Computational Physics · Cross-listed from cond-mat.str-el

Homogeneous to multi-scale inhomogeneous complex magnetism in Ba3LnRu2O9 (Ln = Gd, Ho), Revealed by muSR and Machine-Learned Muon-Site Calculations

S. Ghosh, K. Sharma, E. Kushwaha et al. (+7 more)

We report a comprehensive muon spin rotation and relaxation (muSR) study of the hexagonal 6H-perovskites Ba3LnRu2O9 (Ln = Gd, Ho) to investigate the role of rare-earth anisotropy in the magnetic ground state of 4d-4f coupled systems. Ba3HoRu2O9 exhibits two successive magnetic orderings at TN1 = 46 K and TN2 = 10 K, leading to a complex magnetic ground state, whereas Ba3GdRu2O9, with Gd3+ (L = 0) S-state ions, shows primarily single magnetic ordering at TN = 14.5 K. Zero-field (ZF) and longitudinal-field (LF) muSR measurements reveal markedly different magnetic behavior despite their structural similarity. Ba3HoRu2O9 displays a multi-scale inhomogeneous magnetic state characterized by two-component exponential relaxation, broad local-field distributions, incomplete LF decoupling, and persistent spin dynamics. The muSR results identify two characteristic magnetic energy scales near 46 and 10 K, associated with correlated Ru-Ho interactions and quasistatic magnetic ordering, respectively. Redfield analysis further demonstrates strong local magnetic fields and distributed spin fluctuation timescales, confirming magnetic inhomogeneity. Machine-learning-assisted muon-site and dipolar-field calculations show that the broad field distribution naturally arises from muons sensing distinct coexisting magnetic domains at different structural sites. In contrast, Ba3GdRu2O9 exhibits a comparatively homogeneous antiferromagnetic ground state below 14.5 K, with Gaussian relaxation consistent with a narrow static field distribution. These results establish that the rare-earth ion in Ba3LnRu2O9 is a powerful tuning parameter, driving the ground state from homogeneous to complex, multi-scale inhomogeneous behavior through competing 4d-4f magnetic interactions.
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All authors S. Ghosh, K. Sharma, E. Kushwaha, M. Kumar, G. Roy, A. Pandey, F. L. Pratt, S. Cottrell, D. T. Adroja, T. Basu

Biological Physics

Boundary-contact repolarization provides a mechanism for directional guidance of neural crest cell collectives

Aditya Shankar Paspunurwar and Hector Gomez

Neural crest cells (NCCs) provide a well-established system for investigating collective migration during embryonic development and exhibit migratory behaviors with parallels to cancer invasion. Previous studies establish roles for co-attraction, contact inhibition of locomotion, polarity persistence, and tissue confinement in guiding NCC migration. However, it remains unclear whether NCC migration in confined environments occurs through spatial restriction alone or also through a repolarization response induced by contact with boundaries of confinement. By developing a computational model that couples self-generated chemical transport with contact-mediated repolarization and mechanical interactions, we show that cell-boundary contact can reorient cell polarity and sustain directional migration under confinement, whereas no persistent collective direction emerges without confinement. Moreover, suppressing contact-mediated repolarization from non-permissive confinement boundaries while preserving the mechanical interactions prevents sustained directional migration, suggesting that spatial restriction alone is insufficient in the model. Together, these findings establish an experimentally testable mechanism through which self-generated chemical signals and contact with confining tissues jointly regulate collective guidance of NCCs. These insights provide a basis for investigating developmental migration defects and potential strategies to regulate collective tissue invasion.
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Fluid Dynamics

Conjugate heat transfer in turbulent pipe flow with azimuthally non-uniform heating

Jonathan Neuhauser, Davide Gatti and Bettina Frohnapfel

Turbulent conjugate heat transfer (CHT) in pipes with azimuthally non-uniform heating is studied theoretically and numerically with a focus on low-$\mathrm{Pr}$ fluids. While the influence of the solid properties is well documented for uniform heat flux, their effect under azimuthally non-uniform heating has not been explored systematically. We address this gap by combining spectral analysis of the problem – which reduces arbitrary boundary conditions to a discrete set of azimuthal wavenumbers – with well-resolved numerical simulations of thermally and hydraulically fully developed turbulent pipe flow at $\mathrm{Re}_\tau \approx 180$ for $\mathrm{Pr} = \{0.025, 0.71\}$, representative of liquid metals and air. Two principal results emerge: (i) the global Nusselt number is independent of the azimuthal heat-flux distribution and exhibits only a minor dependence on the solid properties, resolving an open question for low-Prandtl-number fluids; (ii) CHT nevertheless strongly affects the azimuthally resolved mean temperature at low $\mathrm{Pr}$, where azimuthal conduction in the solid homogenises the interface heat flux, and governs the level of temperature fluctuations through the effusivity ratio (thermal activity ratio) $K$. Finally, an analysis of the turbulent Prandtl number $\mathrm{Pr}_{t}$ shows a strong anisotropy, particularly near the wall, and a dependence on the azimuthal wavenumber.
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General Physics

Deriving Schwarzschild and Kerr from the Einstein Equivalence Principle

G. Alencar

This manuscript demonstrates that the Schwarzschild and Kerr metrics can be derived fundamentally from the Einstein Equivalence Principle (EEP) without explicit reliance on the Einstein field equations. We show that the EEP implies a gravitational stretching of length in the direction of acceleration, a spatial counterpart to time dilation. In the static case, this effect yields the constraint $g_{00}g_{11}=-1$, from which the Schwarzschild solution is recovered by taking the Newtonian limit. For the stationary case, we employ oblate spheroidal coordinates and construct a physically motivated tetrad to implement the EP, introducing gravity through three unknown functions. By applying the EEP-induced constraints and showing that the principle requires the preservation of the spacetime volume determinant ($g = g_0$), the system is reduced to a single unknown function that yields the Kerr metric. These results suggest that classical tests of General Relativity are, at their core, fundamental validations of the EEP. We further argue that the EEP leads naturally to Unimodular Gravity, implying that the existence of the cosmological constant is a direct consequence of this foundational postulate.
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Optics

Theory of Disorder-Induced Multimode Coupling in a Fabry-Pérot Cavity

Wei-Kuo Li and Hsing-Ta Chen

Strong light–matter interactions have attracted significant interest in physical chemistry due to the potential for modifying molecular processes by hybridizing with cavity photons and forming polaritonic states. However, for a disordered molecular ensemble within an optical cavity capable of supporting multiple photon modes, theoretical understanding of multimode polaritons remains incomplete, particularly regarding whether and how distinct photon modes are mutually coupled through molecules. In this work, we generalize the collective mode expansion formalism to incorporate multiple photonic modes and show that spatial and orientational disorder in the light–matter coupling strength (originating from dipole orientation fluctuations and photonic field inhomogeneity) can mediate cross-mode coupling. This disorder-induced coupling allows for energy transfer to otherwise unexcited photon modes through a mechanism termed disorder-induced multimode activation (DIMA). Using an effective one-dimensional Fabry–Pérot cavity as a test system, we demonstrate that the cross-mode energy transfer through DIMA can be controlled by changing the molecular layer thickness and the optical properties of the mirrors. Furthermore, we show that DIMA is robust against weak energetic disorder. This mechanistic understanding lays the foundation for a systematic control of multimode photon coupling using disorder-induced effects.
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Medical Physics · Cross-listed from cs.LG

Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

John M. O'Toole, Sean R. Mathieson, Aurel Luca et al. (+4 more)

Background: Discontinuity in neonatal EEG is a critical marker of brain dysfunction but can be difficult to quantify visually. Inter-burst intervals (IBIs) are a key characteristic of discontinuity, yet their definition does not easily translate to AI detection. Objectives: To introduce low-voltage intervals (LVIs), defined exclusively by amplitude and duration, as a context-independent alternative to IBIs, develop an AI detection method for LVIs, and assess their clinical utility as a marker of brain injury. Methods: We developed a ConvNeXt model to detect LVIs and compared its performance with existing methods. We then tested the clinical relevance of detected LVIs across four applications: 1) grading background EEG abnormalities on an open benchmark dataset, 2) classifying clinical hypoxic-ischaemic encephalopathy (HIE) grades and comparing with a normal EEG dataset, 3) seizure prediction, and 4) association with seizure burden. Results: For training and validation, 654 hours of per-channel EEG from 123 neonates were reviewed and 51,162 LVIs were annotated. On held-out validation, ConvNeXt had an AUC of 0.996 and a Matthews correlation coefficient of 0.926. Model-reader agreement exceeded inter-reader agreement ($\Delta \kappa = 0.025$, 95% CI: 0.007 to 0.045, $p<0.001$), attaining expert-level equivalence. On a combined dataset of 444 neonates, percentage LVI showed a strong association with EEG grades (Spearman's $\rho=0.801$), a moderate association with HIE grades ($\rho=0.384$), a weaker association with seizure burden ($\rho=0.244$), and a strong seizure prediction capacity (AUC $=0.872$). Conclusion: AI detection of LVIs achieves expert-level equivalence, whereas quantitative features do not. Automated LVI quantification provides a robust clinical biomarker of neonatal encephalopathy associated with background EEG activity, HIE severity, and seizure risk.
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All authors John M. O'Toole, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, Robert Hogan

Atmospheric and Oceanic Physics

Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX

Iris Linck, John Dennis, John Schreck et al. (+2 more)

Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming expertise. We present LAFT (LLM-Assisted Fortran Translation), an agentic framework for converting Fortran atmospheric physics parameterizations into GPU-accelerated JAX while preserving numerical agreement with the Fortran reference. LAFT coordinates source-code analysis, translation, repair, runtime validation, and performance profiling. It combines a deterministic front end for metadata extraction and bridge generation, structured translation prompts, numerical validation, and profiling-guided feedback to identify inefficient execution patterns. The bridge handles array-layout conversion separately from scientific computation. We evaluate LAFT using the Kessler warm-rain microphysics scheme and four LLMs: Claude, GPT, Gemini, and Qwen. All four validated translations reproduce the Fortran reference outputs to near floating-point precision for the evaluated case, but their GPU performance differs substantially with execution design. The Gemini-generated implementation achieves up to a 357-fold speedup over serial Fortran in cached execution at 1,000,000 atmospheric columns, excluding compilation and host-device transfers. The results demonstrate that numerical agreement alone does not ensure efficient GPU execution and motivate combining numerical validation with hardware-aware performance analysis. LAFT provides a practical workflow for modernizing legacy atmospheric physics code through LLM-assisted translation, deterministic interface generation, automated validation and repair, and reproducible profiling.
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All authors Iris Linck, John Dennis, John Schreck, Karen Stengel, David John Gagne II

Optics · Cross-listed from quant-ph

Optimal linear-optical interferometers for unitary t-designs in a multi-photon Fock space

Robert A. Grinshtein, Ivan V. Dyakonov and Stanislav S. Straupe

We investigate the construction of unitary $t$-designs in a multi-photon Fock space on programmable linear-optical interferometers. Using the frame potential as the optimization objective, we identify architectures that realize approximate unitary 1- and 2-designs with considerably fewer controllable parameters than required for universal linear optics. For example, we find numerical evidence suggesting that for unitary 1-designs, the required number of programmable parameters scales empirically as $2d$ with the number of modes $d$, instead of $O(d^2)$. We benchmark the resulting ensembles using single-photon transition probabilities and average channel fidelities. Furthermore, as a practical result, we develop a photon-number estimation protocol for multi-photon Fock states that leverages these optimized 1- and 2-designs. Our findings demonstrate that shallow interferometers can accurately reproduce Haar-averaged first moments, while second-moment-sensitive observables provide a considerably stricter test for approximate unitary 2-designs.
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