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Physics Physics

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Monday 12 October

Applied Physics

Supercell-enabled inverse design of boundary-state dispersion for group-velocity control in pseudomagnetic phononic crystals

Hua-Yang Chen, Nan-Xin Yu, Zhen-Hui Qin et al. (+2 more)

Topological boundary states offer a platform for guided-wave propagation within bulk bandgaps. Their propagation characteristics depend not only on the spectral extent of the states but also on their dispersion within the operating bandwidth. A nearly linear dispersion provides more uniform group velocity across frequency, whereas a reduced slope enables slow-wave propagation. Despite advances in inverse design of prescribed dispersion relations and topological dispersion tuning, application-oriented characteristics of boundary-state dispersion have rarely been direct inverse-design objectives. Here, we develop a supercell-enabled, surrogate-assisted framework for the inverse design of boundary-state dispersion in a phononic crystal with pseudomagnetic fields. Area-preserving anisotropic deformation shifts the local Dirac points, and independent variation of 21 rows within the supercell provides distributed control over the boundary-state branch. A constraint-preserving surrogate reconstructs the dispersion from an initial frequency and positive frequency increments, with first- and second-difference losses introduced to capture local slope and curvature. Coupled with a genetic algorithm, the framework targets two prescribed-bandwidth design tasks: highly linear dispersion and slow-wave dispersion. For the former, finite-element verification yields relative group-velocity variations of 0.142% to 1.183% over prescribed bandwidths of 4% to 6%. For slow-wave designs, mean group velocities of 190 to 385 m/s are obtained over bandwidths of 0.2% to 0.8% while satisfying a 5% group-velocity variation limit. These results establish boundary-state dispersion as a direct inverse-design target beyond conventional bandwidth optimization, opening a flexible route to group-velocity control through application-oriented dispersion engineering over prescribed relative bandwidths.
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All authors Hua-Yang Chen, Nan-Xin Yu, Zhen-Hui Qin, Yan-Feng Chen, Si-Yuan Yu

Atomic Physics

An Aubry-André localized optical lattice clock

Shuo Ma, Anke Stöltzel, Bennett Christensen et al. (+5 more)

Optical lattice clocks in space could improve satellite navigation systems and enable new tests of fundamental physics, including gravitational-wave detection and tests of relativity. In microgravity, atoms can tunnel to degenerate neighboring lattice sites, broadening the clock transition and limiting coherence times. We demonstrate Aubry-André localization in a bichromatic optical lattice formed by two magic-wavelength lasers, suppressing tunneling in a horizontal lattice clock emulating free fall. We recover a Fourier-limited clock Rabi spectrum and measure Ramsey coherence times of up to 2 s in shallow lattices, with up to 100-fold improvement over the delocalized regime. These results establish Aubry-André localization as a practical route to controlling tunneling in future space-based clocks, and open a path to operating terrestrial clocks at shallower lattice depths.
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All authors Shuo Ma, Anke Stöltzel, Bennett Christensen, Zhuoli Ding, Dhruva Ganapathy, Andrew Householder, Genyue Liu, Shimon Kolkowitz

Plasma Physics

Dynamic Optimization Workflow for Gyrokinetic Stability and Transport

E.A. Belli, J. Larson, J. Candy et al. (+1 more)

A dynamic optimization workflow for linear gyrokinetic stability that couples the gyrokinetic code CGYRO with the libEnsemble framework for ensemble management has been developed. The workflow is designed to efficiently explore multi-dimensional plasma parameter spaces and to address the practical challenges of simulation-driven optimization in fusion plasma physics, including expensive objective evaluations, unavailable gradient information, non-smoothness arising from transitions between dominant instability branches, and simultaneous multi-core simulations on high-performance computing resources. Rather than optimizing the growth rate at a single wavenumber, the objective maximizes the growth rate-to-wavenumber ratio over the ion-scale spectrum. A derivative-free, model-based optimization algorithm employs manifold sampling to handle the nonsmooth composite structure of the objective function. The approach is applied to the optimization of local magnetic flux-surface shaping parameters, with an equilibrium baseline drawn from DIII-D negative triangularity experimental parameters. The workflow is general and extensible and provides a foundation for future optimization studies involving nonlinear transport objectives and higher-dimensional parameter spaces.
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All authors E.A. Belli, J. Larson, J. Candy, I. Sfiligoi

Physics and Society

Behavioural Sociology of Humans and Machines

Taha Yasseri, Hao Cui and Nico Mutzner

Artificial intelligence (AI) increasingly participates in communication, decision-making, and collective action, making the social organisation of human-machine interaction central to Hybrid Intelligence (HI). This chapter reviews the behavioural sociology of humans and machines through an HI lens, examining how interaction and feedback shape the combination of human and artificial capabilities. Drawing on sociology, social psychology, human-computer interaction, organisational studies, collective intelligence, and science and technology studies, it traces processes from individual social perception to cooperation in hybrid groups and organisational and institutional arrangements. Particular attention is given to how machine participation alters familiar mechanisms: social categorisation attaches expectations to designed identities, reciprocity may involve unequal social obligations, and algorithmic recommendations acquire authority through organisational practices. The chapter distinguishes observable machine behaviour from human interpretations of intention, understanding, and moral responsibility. It also considers how behavioural effects can extend beyond direct human-machine encounters, while separating established findings from proposed longer-term consequences. It argues that the benefits of HI depend on how roles, incentives, reliance, and accountability are organised, and identifies priorities for empirical research on these evolving sociotechnical arrangements.
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Computational Physics · Cross-listed from cs.LG

Structure-preserving fidelity diagnostics for partial differential equations

Kyriakos Flouris

Machine learning surrogates of partial differential equations (PDEs) are commonly trained on data generated by numerical solvers. Therefore, the training data obey not the PDE but the modified equation of the numerical scheme, and the test loss, computed on data from the same solver, cannot distinguish a model that learned the physics from one that learned the discretization error. Here, we show that the difference is nevertheless visible in the trained model. We propose a suite of structure-preserving fidelity diagnostics, simple scores that measure whether a learned model respects one known property of the physics, such as a conserved quantity, a symmetry or a Hamiltonian structure, without access to the training data or the governing equation. Over five systems, the diagnostics track the error with respect to the true dynamics with a rank correlation of up to $0.94$, while the test loss is uncorrelated or anti-correlated with it. For the Burgers, Korteweg-de Vries and Navier-Stokes equations, the diagnostics separate numerical schemes that the test loss ranks as equivalent, including schemes of the same order. order.
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Optics · Cross-listed from gr-qc

Mathematical framework for simulation of non-perturbative cavity optomechanics with plate-like dielectrics

Andrew Laeuger and Nancy Aggarwal

Cavity optomechanical models typically treat a sensor either as a perturbative polarizable object in a pre-existing optical field, or solve the field at a fixed equilibrium and linearize in a fixed optical-mode basis. Neither self-consistently determines the spatially resolved cavity field, the optical force, the mechanical equilibrium, and the back-action when a finite-thickness dielectric plate intercepts the entire optical mode and restructures the intracavity field. In this companion paper to [1], we develop that treatment by extending the vacuum two-photon formalism to finite-thickness planar dielectrics, incorporating their Fresnel reflection, internal propagation, optical forces, and mechanical back-action directly into the field equations. We derive the carrier and sideband transfer functions needed to describe the coupled optical-mechanical system, including optomechanical cooling and the response to external perturbations. The result is a systematic starting point for calculating detector response and noise budgets for cavity experiments with intracavity dielectrics, including gravitational wave detectors deploying levitated sensors.
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Applied Physics · Cross-listed from quant-ph

Imaging the topological sign of a Dirac mass with spin-polarized tunneling

Payman Kazemikhah, Ruolun Zhang, Byunghoon Kim et al. (+5 more)

A Dirac mass is characterized not only by its magnitude but also by its sign, which influences the Berry curvature, Hall response, and the propagation direction of chiral boundary modes. However, local spectroscopy of magnetic topological surfaces has so far accessed only the gap magnitude $|M|$. Here we show how the topological sign of $M$ can be determined locally. We first establish the sign information that spin-resolved tunneling can access and the reference required to obtain an absolute Hall sign. Although spin–orbital entanglement generally obscures the relation between measured spin and Dirac pseudospin, crystalline rotation symmetry alleviates this ambiguity at the band edge. On a $C_3$ axis, an axial tip couples to a fully spin-polarized state whose tunneling asymmetry directly reveals the sign of the occupied-band Berry curvature, independent of orbital composition. We further show that the zeroth Landau level provides an independent mass-sign measurement with an unpolarized tip, while opposite domains and their chiral domain-wall modes provide self-consistency checks. These results pave the way for imaging the locations where the topological character changes within a material, rather than merely identifying where a spectral gap appears.
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All authors Payman Kazemikhah, Ruolun Zhang, Byunghoon Kim, Xiangyu Luo, Zachary Ducorsky, Yu-Peng Zhu, Md. Shafayat Hossain, M. Zahid Hasan

Computational Physics

KINTORA: a semi-Lagrangian discontinuous Galerkin drift-kinetic, unstructured mesh tokamak solver

Mark F. Adams

Continuum gyrokinetic codes use structured, field-aligned grids on nested flux surfaces; particle-in-cell codes support unstructured meshes and open field lines but carry a statistical noise floor. We present a continuum drift-kinetic solver that occupies neither position: a semi-Lagrangian discontinuous Galerkin discretization on unstructured triangular poloidal planes extruded toroidally, whose parallel advance is a map along the exact field line in the flux-coordinate independent manner, so the mesh is never aligned with the field, with adiabatic electrons and a long-wavelength polarization field solve. Every operator is verified against an independently computed reference, from Landau damping to the geodesic-acoustic root and a linear ion-temperature-gradient eigenvalue problem, every measured rate within 4% except the geodesic-acoustic damping, within 12%. A linear toroidal ion-temperature-gradient demonstration on a canonical-Maxwellian equilibrium recovers a radially coherent mode whose growth rate, 0.065 c_s/a, is stationary in time. The collisionless zonal-flow residual is the one measurement still moving: it rises with poloidal refinement from 0.72 to 0.87 of the model's own orbit-quadrature prediction for the measured observable and has not converged. The solver is fully device resident, and every result is reproduced between CPU and GPU builds and across MPI process counts. The code, the data behind every table and an artifact description are in a public repository.
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Fluid Dynamics

A complete forward projection algorithm through cylindrical and spherical refractive windows

Shijie Zhong, Shiyong Tan, Xiaosi Zhang et al. (+2 more)

Cylindrical or spherical windows often appear in experiments and real applications. Measuring through those windows requires projecting 3D points to pixels, which is different to compute due to refraction. A point may have multiple projected images but cannot be efficiently determine with the existing projection algorithms. Using the ray's conserved momenta, we reduce this projection to closed-form equations with a concave structure. As a result, we can successfully determine whether a 3D point has a single projection or multiple and compute those locations. In simulations, with one single search, we can recover images that iterative methods miss even with many restarts. A wand calibration based on it can reach the precision limit set by image noise, enabling accurate 3D particle tracking in a swirling flow through cylinder windows. This algorithm shows how to predict visibility and multiple images through curved windows, shedding light for 3D pipe flow measurements, accurate underwater reconstruction, and many real applications.
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All authors Shijie Zhong, Shiyong Tan, Xiaosi Zhang, Yanying Li, Rui Ni

Geophysics

Event-Centric Earthquake Monitoring with a Multi-Station Transformer

Haoyu Wang and Weiqiang Zhu

Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete and reliable. Machine learning has improved individual tasks of earthquake monitoring, but processing stations independently discards the consistency of arrivals across the network, and running tasks in sequence fragments the information describing each earthquake. We present EQNet2, a geometry-aware multi-station Transformer that jointly predicts phase arrivals, first-motion polarities, source location and magnitude, and the association of arrivals into events. On a Northern California test dataset and on continuous waveforms from Ridgecrest and the San Francisco Bay Area, EQNet2 recovers more weak arrivals, produces fewer false detections, and associates more picks into located events. Its learned attention links information across stations along time–distance patterns consistent with seismic moveout. As seismic networks grow denser, exploiting multi-station coherence and event-centric prediction offers a path to more complete and reliable catalogs.
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Computational Physics · Cross-listed from math.NA

A Method for Exact Enforcement of Boundary Conditions on General Curved Triangular Domains: Algorithm and Application to Scientific Machine Learning

Suchuan Dong and Yuchuan Zhang

We present a method for enforcing exactly Dirichlet, Neumann, and Robin conditions on general triangular domains with arbitrarily curved boundaries. The algorithmic constructions developed here complements those in a previous work for general quadrilateral domains. Our method combines (i) the exact mapping between a general curved triangular domain and the standard triangle, (ii) TFC constrained expressions, and (iii) Barnhill transfinite interpolations, to provide explicit closed-form trial ansatze that mathematically satisfy the imposed boundary conditions exactly. Corner compatibility induced by the boundary conditions is pivotal, and enforcing exactly the imposed boundary conditions hinges on the exact enforcement of the induced compatibility constraints. The corner compatibility associated with the acute vertices of the standard triangle and the coupling between gradient components when the hypotenuse corresponds to a Neumann/Robin boundary pose particular challenges. Two strategies are critical to resolving these issues, by reformulating conditional corner constraints into equivalent unconditional forms and by introducing a separate TFC-style auxiliary edge construction together with an associated linear solve on the hypotenuse. We analyze the corner compatibility constraints and present formulations for the general form of functions that exactly satisfy the imposed Dirichlet, Neumann, or Robin conditions on curved triangular domains. The proposed method has been implemented using extreme learning machine type randomized neural networks for solving PDE problems. We present ample numerical experiments with linear/nonlinear PDE problems on a variety of domains with complex boundary geometries. Numerical results show that the current method has enforced the Dirichlet, Neumann, and Robin conditions on curved domain boundaries to the machine precision.
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Instrumentation and Detectors

FINESSE: A Versatile Beamline for Fundamental Interactions with Neutrons at the European Spallation SourcE

J. Amaral, J. Barrow, M. Bartis et al. (+71 more)

FINESSE (A Versatile Beamline for Fundamental Interactions with Neutrons at the European Spallation SourcE) is a proposed versatile beamline for fundamental interactions with neutrons at the European Spallation Source. Building on the HIBEAM design studies, FINESSE extends the original concept into a dual-guide beamline capable of operating either in a high-flux configuration or in a low-background configuration within a single ESS beam port. The high-flux branch is optimized for rare-process searches, including neutron–antineutron oscillations, neutron–sterile-neutron transitions, axion-like-particle searches, neutron charge measurements, and rare neutron decays. The low-background branch is designed for precision measurements requiring strong suppression of prompt and line-of-sight backgrounds, with a cryogenic neutron electric-dipole-moment experiment used as a benchmark case. A baseline exchangeable-guide concept has been developed in which only short guide sections inside the ESS bunker are exchanged, while the upstream extraction system and chopper positions remain fixed. A staged implementation, Short FINESSE, would use the upstream section of the high-flux beamline together with a compact magnetically controlled experimental region, enabling an early physics and R&D programme before completion of the full facility. This paper summarizes the scientific case, beamline concept, guide simulations, chopper and timing system, magnetic-field control, detector developments, and staged implementation strategy. FINESSE would establish a long-term particle-physics infrastructure at ESS, providing a beamline for both discovery-driven searches and precision neutron measurements throughout the lifetime of the facility.
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All authors J. Amaral, J. Barrow, M. Bartis, D. H. Beck, P. M. Bentley, J. Bijnens, G. Brooijmans, L. J. Broussard, A. Burgman, G. Cibinetto, G. Croci, D. D. Di Julio, P. Fierlinger, B. W. Filippone, C. Gatto, H. Gisbert, H. Gogoi, G. Gorini, A. Gutierrez-Rodriguez, A. Koberinski, M. Kaczmarek, M. Silarski, M. O'Riordan, A. Heinz, J. Henderson, M. Holl, H. T. Johansson, B. Jonsson, H. Henriksson, L. Jonsson, Y. Kamyshkov, M. P. Kuhl, P. Kinhult, R. Kolevatov, A. Kozela, W. T. Lee, K. K. H. Leung, T.-J. Liang, G. Luzin, B. Meirose, D. Milstead, R. Mohapatra, G. Muhrer, A. Muraro, A. Nepomuceno, N. Okada, A. Oskarsson, T. Palasz, R. Pasechnik, V. Larsson, L. B. Persson, O. Putignano, J. Ramsey, C. Snow, L. Werselius, B. Rataj, A. Rodriguez-Sanchez, A. Saunders, V. Santoro, F. Scioscioli, R. Shrock, D. Silvermyr, H. Sina, W. M. Snow, Y. V. Stadnik, L. Tchelidze, N. Tsapatsaris, A. Tureanu, A. Vilela Pereira, M. Wolke, J. Womersley, S. Xu, A. R. Young, K. Osterberg

Computational Physics

Wisevolve Potential Engine: Machine-Learning Molecular Dynamics at 100 Million Atom-Steps per Second on a Single GPU

Hekai Bu and Zheyong Fan

General-purpose molecular dynamics software supports diverse interatomic potentials and simulation requirements. Within a simulation stage, the loaded potential model and the required calculations typically remain fixed as the atomic configuration evolves. We present the Wisevolve Potential Engine (WPE), which uses this information from GPUMD to select and reuse a GPU execution policy tailored to the model and simulation stage. GPUMD retains simulation control and ownership of the simulation data. On a single NVIDIA RTX 5090, WPE-enabled GPUMD (GPUMD-WPE) achieves a median throughput of 109 million atom-step/s across 106 neuroevolution potential (NEP) workloads. For a 15.1-million-atom diamond Si system, it sustains 142 million atom-step/s. Relative to GPUMD, geometric-mean speedups are 3.49x across these NEP workloads, 3.61x across 16 workloads using NEP with dynamic charges (qNEP), 1.24x across 28 classical-potential workloads, and 10.5x across 56 path-integral molecular dynamics (PIMD) configurations. End-to-end calculations of water structure, lattice thermal conductivity, and PIMD thermal expansion show close agreement with corresponding GPUMD results and published simulation results. A separate 12.5-million-atom Cu shock-compression simulation demonstrates its applicability to large-scale nonequilibrium dynamics. These results support model- and stage-specific specialization as a practical way to accelerate GPU potential evaluation in general-purpose molecular dynamics workflows.
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Optics

Zeptosecond-level timing-noise floor in optical-to-terahertz photomixing

William F. McGrew, James Greenberg, Scott C. Egbert et al. (+6 more)

Optical generation of terahertz (0.1 to 10\,THz) carriers can, in principle, preserve the spectral purity of an optical reference without the noise penalty associated with electronic frequency multiplication. Here, we identify and suppress the dominant excess-noise mechanisms that limit this conversion in high-power uni-travelling-carrier photodiodes. We characterize amplitude-to-phase conversion as functions of photocurrent and reverse bias and identify operating parameters to minimize the effect. We quantify the white phase noise introduced by optical amplification and show that this contribution can be eliminated by directly driving the photodiode with a high-power optical source. With the excess noise mitigated, we use cross-correlation phase-noise measurements to observe a white phase-noise floor of $-161.0$~dBc/Hz at 263.2~GHz, corresponding to a timing-noise floor of 7.6~zs/$\sqrt{\mathrm{Hz}}$. The remaining floor follows the photocurrent dependence expected from shot noise. Extending the measurement across the full WR-3.4 band, from 220 to 330~GHz, yields timing-noise floors below 10~zs/$\sqrt{\mathrm{Hz}}$ throughout, with a minimum of 6.0~zs/$\sqrt{\mathrm{Hz}}$. These results establish direct photomixing as a route to sub-10-zs/$\sqrt{\mathrm{Hz}}$ white timing-noise floors for continuous-wave sources at terahertz frequencies and identify the noise processes that must be controlled to reach this regime.
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All authors William F. McGrew, James Greenberg, Scott C. Egbert, Brendan M. Heffernan, Y. Lange Simmons, Tatsuya Tanigawa, Kazutoshi Kato, Tadao Nagatsuma, Antoine Rolland

Fluid Dynamics

Preferential concentration in a periodically forced particle-laden turbulent planar jet

Mohammad Ahmad, Pierre Bragança, Christophe Cuvier et al. (+1 more)

Periodic forcing is common in particle-laden jets, yet its influence on turbulence and inertial-particle clustering remains poorly understood. We investigate a turbulent planar jet at two Reynolds numbers, approximately 4950 and 9900 based on nozzle height, subjected to sinusoidal forcing at a frequency three orders of magnitude below the preferred jet instability. Hot-wire anemometry and high-speed particle imaging characterise the carrier turbulence, while Voronoi tessellations quantify preferential concentration of dilute inertial droplets. Despite its low frequency and moderate amplitude, forcing substantially modifies turbulence while leaving the mean-flow development nearly unchanged. The integral scale decreases by up to $40\%$, while forced and unforced jets retain the same time-averaged relationship between the dimensionless dissipation coefficient and Taylor-scale Reynolds number. Within the forcing cycle, velocity fluctuations and the Taylor microscale vary approximately in opposite phase, keeping the Taylor-scale Reynolds number within $\pm10\%$ of its cycle mean despite substantial variations in dissipation and Kolmogorov time scale. Particle clustering is even more sensitive, with forcing increasing mean preferential concentration and producing phase-dependent variations of $21$–$49\%$ of the cycle mean. Clustering peaks when the Kolmogorov time scale is largest, corresponding to the smallest particle Stokes number. Thus, markedly different clustering states occur at similar instantaneous Taylor-scale Reynolds numbers. These findings demonstrate that weak temporal modulation can control particle organisation without substantially altering the mean transporting flow.
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All authors Mohammad Ahmad, Pierre Bragança, Christophe Cuvier, Martin Obligado

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

Can Universal Machine-Learning Interatomic Potentials Capture Small Polarons? A Geometry-Based Benchmark in Transition-Metal Oxides

Vladislav Korostelev, Pjotrs Žguns and Konstantin Klyukin

Small polarons, formed when excess electrons become localized through lattice distortions, play a critical role in charge transport, catalysis, and degradation processes in transition-metal (TM) oxides. Whether universal machine-learning interatomic potentials (uMLIPs), which lack explicit electronic degrees of freedom, can describe them remains unclear. Here, we investigate H-induced electron localization across a range of TM oxides using a geometric descriptor based on TM-O bond-length expansion (Z-score) to identify polaronic distortions. We find that uMLIPs exhibit varying degrees of electron localization, from delocalized states to metastable small polarons, with their behavior strongly dependent on the training data and treatment of electronic correlations. In particular, the absence of Hubbard U corrections for specific TM cations in the training data leads to electron delocalization, in contrast to DFT+U predictions. Although some uMLIPs reproduce polaron hopping barriers during coupled proton-polaron transfer, all of them fail to stabilize spatially separated polaron-donor configurations, instead favoring localization near the H dopant.
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Applied Physics

Progress in Cavitated Front Gridline Ag Paste for PERC Solar Cell Architecture

Donald Intal, Sandra Huneycutt, Abasifreke Ebong et al. (+2 more)

This paper reports on the progress made with the cavitated silver paste on passivated emitter rear contact cells (PERC) to reduce the series resistance. Thus, electrical and microscopy characterizations are used to examine these resistive losses. Fifteen 252 cm2 PERC cells were fabricated for this assessment. Under standard test conditions, these cells achieved a mean conversion efficiency of 21.87% and a maximum of 22.07%. The mean measured fill factor was 79.98%, compared with a pseudo-fill factor of 83.60%, giving a mean deficit of 3.62 percentage points. Fill factor decreased with increasing area-normalized series resistance, which ranged from 0.53 to 0.83 ohm-cm2, while open-circuit voltage and short-circuit current remained unvaried. These electrical trends support resistive current extraction as the principal contributor to fill-factor variation. Electroluminescence imaging revealed localized emission nonuniformity. Cross-sectional scanning transmission electron microscopy showed variation in the glass interfacial layer, while scanning electron microscopy identified micrometer and sub micrometer silver particles. Conductive atomic force microscopy after sequential removal of the bulk metal and glass revealed spatially heterogeneous current response. These observations are consistent with nonuniform electrical access at the contact, where restricted conducting pathways may concentrate on current and increase local voltage drops. The combined characterization identifies interfacial heterogeneity and the connectivity of conducting pathways as priorities for understanding and reducing extraction losses in cavitated silver metallization.
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All authors Donald Intal, Sandra Huneycutt, Abasifreke Ebong, Dana Hankey, Marshall Tibbetts

Atmospheric and Oceanic Physics · Cross-listed from stat.ML

Accounting for model error in ensemble variational data assimilation through learned generative dynamics

Marc Bocquet, Tobias S. Finn and Sibo Cheng

Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced sequential data assimilation schemes with a generative forecasting model which accounts for complex model errors. This generative model provides a stochastic approximation of the conditional distribution of the successor state given the current state. The proposed variational formulation is a novel one-step weak-constraint 4D-Var scheme in which a learned conditional stochastic forecast replaces the prescribed additive Gaussian model error representation. We derive and implement this formulation as an ensemble variational algorithm, the generative iterative ensemble Kalman filter (IEnKF-G), which generalises the iterative ensemble Kalman filter with additive Gaussian model error (IEnKF-Q). We extend the method to a smoother with an assimilation window spanning multiple time steps and propose a simplified variant. The IEnKF-G is evaluated on a stochastic variant of the Lorenz 1963 model to assess its robustness. It is then tested on a multi-scale variant of the one-dimensional Lorenz 1996 model, in which the resolved variables are affected by model error originating from the hidden small-scale variables. The algorithm is then compared to an ensemble Kalman filter that adaptively mitigates model and sampling errors, and to the IEnKF-Q. The IEnKF-G is generally more accurate in the tested configurations, with substantial gains under partial observations and significant model error. This demonstrates the potential of generative data-driven models when paired with classical data assimilation methods, particularly when model error is substantial.
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Optics · Cross-listed from quant-ph

Ghost unitaries for loss-resilient photonic resource-state generation

Kevin J. Randles, Manuel H. Muñoz-Arias and Kevin Thompson

Entangled few-photon states, such as Bell and Greenberger-Horne-Zeilinger (GHZ) states, are central resources for discrete-variable photonic quantum information processing. In many architectures, these states are generated probabilistically by first heralding single photons from biphoton sources and then interfering them in linear-optical circuits. In this work, we show that the entanglement structure of said biphoton sources can be directly exploited for noise-engineering to deliver higher quality resource states. Using the transpose identity of multi-mode squeezed vacuum states, we introduce "ghost-unitary" implementations in which the target resource-generation interferometer is applied to the heralding modes rather than to the delivered resource modes. When the dominant imperfection is interferometric loss, this relocation converts direct amplitude damping of the output into false-positive heralding events that require both higher-pair emission and photon loss. In the low-squeezing regime, these events are parametrically suppressed, yielding higher-fidelity conditional resource states. We analyze this mechanism for single-qubit states, two-photon NOON states, Bell states, and a family of $n$-qubit GHZ states, as well as for single-qubit teleportation and type-I-fusion-based resource growth. Across these examples, we delineate finite parameter regimes in which ghost-unitary implementations outperform direct implementations, with particular relevance to frequency-bin photonics where active linear-optical transformations can be relatively lossy. We also prove that, in the setting considered here, the direct implementation of the $n$-GHZ family delivers identically damped outputs: equivalent to the ideal state followed by uniform amplitude damping. This extends the known $n=2,3$ cases and provides a structural result of independent interest for photonic quantum information processing.
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Chemical Physics

Chromatographic Peak Shape beyond the High-Péclet Approximation: Analytic Time-Domain Expression and Derivatives

Hernán R. Sánchez

Analytic time-domain expressions for the elution-time density and its parameter derivatives were recently derived from a stochastic model of linear isocratic chromatography incorporating advective-diffusive transport, injection dispersion, and fast and slow retention mechanisms. A Gaussian approximation, justified asymptotically at high column-scale Péclet numbers, was adopted for the distribution of the combined mobile-phase and fast-retention time, while slow retention was incorporated through a series of Gaussian-Gamma convolutions. Here, the underlying normal-inverse-Gaussian density is approximated by a finite Gaussian mixture using an existing quadrature rule. The elution-time PDF is a weighted sum of series of the previously derived Gaussian-Gamma form. Its parameter derivatives are also obtained. Newly derived recurrences reduce the cost of evaluating the coefficients of that series and their derivatives from quadratic to linear in truncation order for a fixed number of mechanisms. Through a probabilistic analysis of the series coefficients, the required number of terms is found to be small enough for rapid evaluation under usual chromatographic conditions. A relative roundoff-error estimator is derived to quantify finite-precision loss in the inherited forward recurrence for Gaussian-Gamma convolutions. For the cancellation-prone regime, a backward reconstruction is developed, initialized from an exact ratio of consecutive Gaussian-Gamma terms expressed through parabolic-cylinder functions. A highly accurate approximation is derived for rapid evaluation of this ratio. Independent high-precision calculations confirm the high accuracy of the resulting double-precision evaluation in the tested numerical case.
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Accelerator Physics

HOM Mitigation

Nicoleta Baboi

Higher Order Modes (HOMs) in accelerating structures are resonant electromagnetic fields excited and left behind by charged particle bunches. They can severely degrade the beam quality in accelerators, such as the multi-bunch energy spread and transverse emittance, and therefore one must carefully study and mitigate them. In this lecture, various methods of HOM mitigation are reviewed. On one hand, one tries to avoid their existence, for example by careful design of the accelerating structures and damping systems. On the other hand, one can reduce the effect of the excited HOM fields, for example by means of fast feedbacks or beam alignment. Additionally, a few ways to make HOM signals useful are referred to, such as diagnostics of the beam or of accelerating structures.
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Chemical Physics · Cross-listed from cs.LG

MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching

Ge Sun and Juan J. de Pablo

Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling. Chemical building blocks reduce graph size, but make each node a choice among tens of thousands of related identities. We introduce MIRRA (Molecular Identity Representation, Refresh and Assembly), a discrete flow-matching generator that learns from chemical knowledge and evaluates identity probabilities only when needed, while keeping every identity in its vocabulary available. Chemical features share information across related blocks, and learned identity corrections distinguish them in molecular context. By deciding which positions will refresh before computing their probabilities, the sampler avoids unused predictions while preserving each sampling transition. Against the strongest baselines in each task, MIRRA reduces chemical and graph distribution errors on ZINC250k by 29-31% and 53-60%, respectively, with generation up to 8.6 times faster. Property targeting yields an estimated 7.8-20.8 times more distinct novel hits per second. On a held-out polymer test set, MIRRA achieves 100% periodic validity with known chain topology, versus 57.0% for a polymer variational autoencoder. These results demonstrate fast, high-fidelity generation with the full chemical vocabulary available throughout sampling.
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Accelerator Physics

Anomaly Detection in Bunch Oscillations for Early Warning of Sudden Beam Loss at SuperKEKB

H. Haigh, P. Horak, G. Inguglia et al. (+5 more)

Sudden beam loss (SBL) events at the SuperKEKB collider result in the loss of a large fraction of the stored beam within just a few revolutions, on a timescale comparable to or shorter than the 20-30 {\mu}s latency of the existing abort system. Such events have damaged collimators and threaten Belle II subdetectors. Bunch Oscillation Recorders (BORs) capture transverse bunch positions at each revolution, which often show sizable oscillations that precede significant beam current loss. We test whether these precursors can be detected earlier than a charge-based reference that mimics the existing abort sensors. We apply several oscillation-based anomaly detection algorithms to 208 SBL events recorded in the low-energy ring during the 2024 run period, ranging from simple rolling statistics to machine learning models trained to forecast bunch oscillation. On 173 events not used for training or calibration, the best-balanced model (DeepLSTM) triggers before the charge-based reference in 86-90% of events across five training seeds, with a mean lead time of 25-30 {\mu}s. The only trigger classified as a false alarm fires on an oscillation that begins about 60 turns before the abort and persists until the beam is lost, making it more likely an early precursor than a spurious trigger. These results establish bunch oscillations as a viable early abort trigger and motivate a shadow-mode deployment to measure the false-alarm rate in continuous operation.
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All authors H. Haigh, P. Horak, G. Inguglia, C. Krause, G. Mitsuka, H. Nakayama, R. Nomaru, M. Tobiyama

Optics

Observation of Photon Bunching in Broadband Multimode Thermal Light Enabled by Temporal Imaging

Filip Sośnicki, Michał Mikołajczyk, Sanjay Kapoor et al. (+3 more)

Photons emitted by incoherent sources exhibit temporal bunching, a fundamental manifestation of Bose-Einstein statistics first observed by Hanbury Brown and Twiss (HBT). While intensity correlation measurements originated in astronomy and are now central across quantum technologies, biophysics, and spectroscopy, observing temporal bunching in broadband thermal light remains limited by detector temporal resolution. Conventional approaches - such as streak cameras or optical upconversion - introduce severe trade-offs in efficiency, bandwidth, or setup versatility. Here, we propose and demonstrate a third route: pre-detection temporal magnification of the optical field prior to measuring its intensity autocorrelation function in a conventional HBT setup. Using an electro-optic time lens with a temporal magnification of 660, we resolve the temporal intensity autocorrelation of broadband thermal light generated via non-degenerate spontaneous parametric down-conversion with a bandwidth of 250 GHz. This enables direct observation of thermal photon bunching on sub-3.5 picosecond timescales without narrow spectral filtering, establishing pre-detection temporal imaging as a general platform for broadband statistical optics, with applications spanning ultrafast quantum optics, biophysical imaging, and potentially stellar intensity interferometry.
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All authors Filip Sośnicki, Michał Mikołajczyk, Sanjay Kapoor, Dmitri B. Horoshko, Mikhail I. Kolobov, Michał Karpiński

Chemical Physics

Solvating machine-learning interatomic potentials for solution-phase chemistry

Anton Z. Ni and Joonho Lee

Most machine-learning interatomic potentials (MLIPs) are trained on gas-phase data, which limits their use for chemistry in solution. Here, we introduce AnySolv, a transferable implicit solvation potential that provides solvent-dependent energy and force corrections to existing gas-phase potentials without retraining them. It is trained on 1.4 million solvation energies and forces computed with density functional theory (DFT) and SMD in 36 solvents, spanning charged, open-shell, and transition-state structures. Because solvents are represented by continuous physical descriptors, AnySolv supports 179 solvents, including those absent from training. On external benchmarks, AnySolv reproduces DFT-SMD solvation energies to 2-4 meV mean absolute error at more than three orders of magnitude lower cost, and it matches the accuracy of DFT-SMD against experimental solvation free energies. This enables screening of the mechanisms of 120 nucleophilic aromatic substitution reactions in six phases as well as molecular dynamics simulations that reproduce the experimentally observed solvent-dependent conformational preferences of alanine dipeptide. By providing transferable solvation corrections to existing MLIPs, AnySolv enables scalable simulations of solvent-dependent chemical reactivity and molecular dynamics.
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Computational Physics

Principles and Techniques for Agentic Research in Experimental Physics

Niko R. Reed, Declan M. Daly, Andrew J. Beling et al. (+4 more)

The use of artificial intelligence (AI) in physics research is increasingly common, yet few guidelines exist for its effective use. Starting with a principles-based approach, we outline a set of ten requirements for collaborative AI agents in experimental physics, spanning scientific norms, effective agent use, and domain expertise. We take a human-centered approach in which agents act as collaborators that amplify rather than replace the expertise of a human researcher (user) who directs the work and is responsible for its results. We then present a framework designed to enforce these requirements, detailing methods for improving off-the-shelf behavior of large language models (LLMs) with custom scaffolding and describing general-purpose scientific workflows and skills. In particular, we highlight the power of closed-loop campaigns where agents work iteratively toward a well-defined goal. We demonstrate the potential of this mode of research through six case studies in experimental physics, covering device design, hardware control, neural network optimization, physical model identification, and data analysis. All six case studies result in useful work products that add utility to our research, including a neural network architecture and solid-state quantum defect photodynamic model that substantially outperform our published work; a microwave waveguide design that extends a published design concept to a fabrication-ready device meeting our experimental constraints; a quantum control pulse sequence candidate that improves simulated performance by more than an order of magnitude; commissioning of an NMR spectrometer to $\sim$1~Hz frequency precision on live hardware; and finally a data-fitting pipeline that achieves, to our knowledge, the first reconstruction of the Ising microstate of an artificial spin-ice array from diffraction-limited magnetic imaging.
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All authors Niko R. Reed, Declan M. Daly, Andrew J. Beling, Jordan D. Giandrea, Ronald L. Walsworth, John W. Blanchard, Daniel Ang

Geophysics · Cross-listed from cs.LG

CartoVec: General-Purpose Location Embeddings from Open Map Data

Furkan Gursoy

Geographic coordinates are common inputs in applied machine learning, but latitude and longitude describe position rather than the surrounding environment. Image-grounded location encoders learn this context from satellite or ground-level imagery; structured open map data offers another source of supervision whose utility for continuous coordinate encoders remains less explored. We introduce CartoVec, a coordinate encoder trained to predict Overture-derived land cover, land use, transportation, place and connectivity targets at multiple spatial scales, without image inputs or downstream-task labels. We evaluate frozen embeddings on Census outcomes, Airbnb prices and Philadelphia crime in the US Northeast using spatial block cross-validation. CartoVec has the highest mean performance on most Census outcomes and the highest average across the full suite, with several comparisons close across folds. The findings support open map data as a practical source of reusable location representations with a coordinate-to-vector interface that requires no map lookup at inference. The open-weight model is available at https://huggingface.co/gursoyfurkan/CartoVec.
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Data Analysis and Statistics · Cross-listed from astro-ph.CO

Beyond the Kullback–Leibler divergence: the likelihood-ratio distribution as a detectability measure

Amirhossein Samandar, Mikel Martin Barandiaran, Linn Htat Lu et al. (+13 more)

The Kullback–Leibler (KL) divergence is the expectation value of the log-likelihood ratio between two specified models, evaluated with respect to one of the two models. It provides an average measure of the distinguishability of the models. We examine its connection to discovery potential and the accuracy of parameter estimates when one model represents a null hypothesis, and the alternative is a family of models characterized by one or more continuous parameters. For data drawn from a multivariate Gaussian distribution, we derive the log-likelihood-ratio distribution and its cumulants under two such hypotheses. We then search over the alternative model's parameter space and use peak prominence, the height of a likelihood-profile peak above its surrounding baseline, as an operational detection statistic. Repeating this search on simulated data drawn from the null model allows us to calibrate the prominence threshold for a specified false-positive rate. We study a toy Gaussian model as well as three cosmological examples involving cosmic topology, the tensor-to-scalar ratio, and dark energy. In all these cases, detectability of a particular model depends not only on KL divergence, but also on the size of the model parameter space, the likelihood-ratio distributions, and the distance of the model from any parameter space boundaries. The relationship between KL divergence, discovery potential, and parameter estimation must therefore be calibrated for each family of models and detection statistic under consideration.
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All authors Amirhossein Samandar, Mikel Martin Barandiaran, Linn Htat Lu, Andrew H. Jaffe, Deyan P. Mihaylov, Glenn D. Starkman, Cynthia Trendafilova, Yashar Akrami, Stefano Anselmi, Javier Carrón Duque, Craig J. Copi, Fernando Cornet-Gomez, Arthur Kosowsky, Anna Negro, Ricardo G. Rodrigues, Andrius Tamosiunas

Optics · Cross-listed from cond-mat.mes-hall

Spatial Engineering of Topological Quantum Matter with Structured Light

Matias Mustonen, Ali G. Moghaddam, Teemu Ojanen et al. (+1 more)

Structured light provides a powerful means of controlling matter while itself exhibiting nontrivial spatial topology. Here, we show that these spatial structures can be harnessed to locally engineer the topological properties of matter. In a paradigmatic example of a topological insulator with Dirac surface states driven by an optical skyrmion beam, we show that structured light spatially tailors the effective Hamiltonian, yielding position-dependent topological gaps and Dirac velocities. The resulting gap modulation can create topological and trivial regions separated by optically defined interfaces, without introducing material boundaries or chemical modifications. We further show that the geometry of these interfaces can be controlled through the spatial structure of the light, in particular its orbital angular momentum. Our results establish structured light as a route to dynamically pattern topological phases and open a broader perspective on the interplay between topology in light and matter, including the possibility of transferring topological characteristics between the two.
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All authors Matias Mustonen, Ali G. Moghaddam, Teemu Ojanen, Marco Ornigotti

Optics

Programmable Hard-X-Ray Spectral Comb Generation via Laser-Compton Scattering

Hao Zhang, 1Ji Qiang, Shukui Zhang et al. (+1 more)

Independent control of line spacing and offset underpins programmable frequency-comb spectroscopy, yet it has not been established for hard-X-ray sources based on inverse Compton scattering after electron transport and angular collection. Self-consistent multi-particle tracking and Li'enard-Wiechert radiation calculations model a transported 10 pC beam and its angle-integrated spectrum near 11 keV. Laser pulse separation programs the comb spacing, while pulse-to-pulse phase translates the entire line grid at fixed spacing. The spacing model agrees within 0.52%, while transport and optics change the phase-transfer slope by less than 0.2%. At fixed electron spot size, the largest resolvable separation scales as the inverse square of normalized emittance; subnanometer bunches also produce a mode-locked train with 13.9 as spacing. This two-coordinate programmability enables phase-stepped multicolor measurements and offers routes to hard-X-ray Ramsey interferometry, coherent control of core and nuclear excitations, and attosecond nonlinear spectroscopy.
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All authors Hao Zhang, 1Ji Qiang, Shukui Zhang, Sergio Carbajo