Fluid Dynamics
The effect of bubble induced turbulent structures on the mass transfer of non-spherical bubbles
Veerle Dijke, Ruben Meijer and Maike Baltussen
Although mass transfer from bubbles to
liquid is essential for the prediction of the efficiency of reactors, the mass transfer from bubbles is not fully understood. To determine the effect of the local velocity profile on the mass transfer for a wobbling bubble with an Eötvös number of 2 and a Morton number of 10-11, 15 simulations were performed with a Front Tracking method using a sub-grid scale model for the mass transfer in the vicinity of the interface. The vortical structures created by the bubble are influenced by the exact physical properties chosen for the
liquid and gas. These changes in the vortical structures also resulted in changes in mass transfer. In addition, the vortical structures created transport barriers between the wake and the bulk of
liquid, which were identified by the high-value Finite Time Lyapunov Exponents. These barriers prevent convective mass transfer from the bubble wake to the bulk of the
liquid. Therefore, mass transfer from the gas phase to the bulk
liquid should take into account both the mass transfer from the gas to the
liquid and the transfer from the wake to the bulk of the
liquid.
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Computational Physics · Cross-listed from cs.LG
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf et al. (+2 more)
Neural scaling laws describe predictable power-law relationships between model size,
dataset size, compute, and performance. While these laws guide the development of modern foundation models, the mechanisms underpinning them remain poorly understood, in part due to the absence of scalable analysis tools. To close this gap, we introduce "spectral position": a scalable measure of which
eigenvalues of the empirical neural tangent kernel (eNTK) currently drive loss reduction. Applying this measure to scaling experiments, we find that spectral position decreases throughout training: learning shifts from dominant eigenmodes into the spectral tail. Larger models reach further into the tail than smaller models, revealing a size-dependent capacity we call "spectral reach". This suggests why larger models achieve lower losses: they sustain learning on weak spectral signals inaccessible to smaller models. We further identify feature learning as a key enabler of spectral reach. It adaptively amplifies gradient magnitudes as learning advances, sustaining progress where frozen representations stall. This points to concrete interventions through architecture and optimizer design.
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All authors Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf, Samuel Tovey, Christian Holm
Instrumentation and Detectors
Novel Strip-like Readout Geometries in Resistive AC-coupled Silicon Detectors (RSD / AC-LGAD)
L. Leander Grimm, Lorena Hahn, Brendan Regnery et al. (+6 more)
Resistive Silicon Detectors (RSD/AC-LGAD) are novel silicon detectors capable of both precise spatial and temporal resolution. Such sensors will be essential for the next generation of particle
colliders (EIC, FCC-ee, CEPC, FCC-hh) and would enable the possibility of a 4D tracker. RSD sensors are typically fabricated with a pixel-like geometry that provides excellent spatial resolution in the x and y directions. However, in regions further from the interaction point, high spatial resolution in one direction (strip-like geometry) is often preferred to reduce the number of readout channels. For example, strip AC-LGADs are now the default option for the US electron ion
collider (EIC). The second production of RSD sensors by Fondazione Bruno Kessler includes sensors with unconventional readout pad shapes that act as a mixture between strip-like and pixel-like readout. This work presents the first characterization of these new pad designs using the Transient Current Technique (TCT). The measurements demonstrate exceptional one-dimensional spatial resolution, confirming the potential of novel strip-like RSDs for future tracking systems.
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All authors L. Leander Grimm, Lorena Hahn, Brendan Regnery, Luca Menzio, Roberta Arcidiacono, Nicolo Cartiglia, Alexander Dierlamm, Markus Klute, Michael Moll
Optics
Robust class-gated single-pixel diffractive optical neural network with random-aberration-aware training
Xianjin Liu, Qiwen Bao, Ting Ma et al. (+6 more)
Optical computing offers the theoretical potential for high-speed, energy-efficient inference, yet its practical deployment remains constrained by fundamental input-output bottlenecks, particularly the reliance on electronic sensors with limited frame rates and stringent
alignment requirements between optical components. Here, we demonstrate an image-class-gated single-pixel DONN that overcomes these limitations by converting spatial complexity into a temporal intensity signature. Using a minimal architecture comprising a reconfigurable digital micromirror device and a single-pixel photodetector, we implement a virtual optical gate. The system time-multiplexes class-specific masks, causing the detector response to peak only when the mask index matches the input class. This allows the predicted label to be read out via peak timing rather than spatial localization, eliminating 2D sensor constraints. To bridge the persistent sim-to-real gap, we introduce a physics-aware training strategy using random-phase augmentation. This method renders the model intrinsically tolerant to phase aberrations and mechanical misalignments without requiring precise hardware modeling. Our prototype achieves 90.0%(MNIST) and 80.0% (Fashion-MNIST) accuracy at a readout rate of 5 kHz. By combining gigahertz-compatible single-pixel detection with robust and
alignment-tolerant training, this work provides a scalable, hardware-efficient pathway toward real-time optical intelligent sensing.
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All authors Xianjin Liu, Qiwen Bao, Ting Ma, Yihuan Liang, Yongqiu Lai, Bolun Zhang, Fansanqiu Li, Licheng Wang, Jun-Jun Xiao
Applied Physics
Hydrogel microwells with light-controlled reversible closure
Qifei Ma, David Urban, Stefano Gabetti et al. (+8 more)
We present a light-responsive hydrogel nanocomposite engineered into arrays of micrometer-scale wells that can be selectively and sequentially closed and re-opened via
laser illumination. Polarization-controlled light exposure induces anisotropic surface deformations, leading to the formation of protrusive flaps sealing the wells. Owing to the intrinsic elasticity and anti-adhesive properties of the hydrogel
matrix, the deformation process is partially reversible, allowing flap retraction and restoration of the original well geometry. This platform facilitates contactless, on-demand trapping and release of microscale objects using a standard optical microscopy configuration. As a proof of concept, we demonstrate the controlled manipulation of a single polystyrene microbead using optical tweezers, including bead positioning within a well, light-triggered closure, and subsequent reopening to release the particle into the surrounding aqueous environment.
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All authors Qifei Ma, David Urban, Stefano Gabetti, Beatrice Masante, Huaizhou Jin, Federica Galvagno, Diana Massai, Alberto Puliafito, Shangzhong Jin, Denis Garoli, Emiliano Descrovi
Optics
Universal free-space photonic circuits for polarized structured light
Dilip Paneru, Paola Savarese, Alessandro Bisio et al. (+2 more)
Traditional linear-optical schemes for implementing discrete
unitary transformations rely on multi-channel interferometers, typically composed of regular meshes of beam splitters and phase shifters. These architectures are well-suited to integrated photonics, where scalability and miniaturization are central requirements. In free space, universal transformations of optical modes have often been pursued through propagation in structured or inverse-designed optical systems, offering advantages in terms of addressable optical modes, reconfigurability, and flexible detection schemes. Here, we present a complementary approach: a collinear free-space photonic
circuit that implements the standard interferometric paradigm in a
Hilbert space of polarized structured-light modes. The
circuit exploits
spin-orbit coupling between circular polarization and optical modes carrying quantized transverse momentum, so that conventional beam splitters and phase shifters are replaced by structured optical materials acting directly on selected
spin-orbit modes. In our demonstration,
liquid-
crystal metasurfaces serve as mode beam splitters and phase shifters, and we experimentally realize representative gates in a four-mode space. We further show how the architecture generalizes to larger numbers of modes, providing a scalable route to arbitrary
unitary transformations in free space. Numerical optimization over random target
unitaries supports the universality of the proposed scheme. These results establish a framework for universal free-space photonic
circuits encoded in structured light and structured matter.
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All authors Dilip Paneru, Paola Savarese, Alessandro Bisio, Francesco Di Colandrea, Filippo Cardano
Physics and Society
q-Exponential Random Graphs: higher-order networks from simple constraints
David Dobáš, Diego Garlaschelli and Petr Jizba
Exponential Random Graphs (ERGs) are among the most widely used network models, derived as principled least-bias graph ensembles that maximize Shannon
entropy under constraints on the expected values of given structural properties. However, it has been recently (re)discovered that, in the absence of additional information privileging Shannon
entropy, the most agnostic inferential construction should maximize the broader class of Uffink
entropies. The resulting
entropy-maximizing distribution changes from the exponential (Boltzmann-Gibbs) to the so-called q-exponential one. Since maximizing Shannon
entropy may produce an unjustified independence between degrees of freedom, here we investigate how the most popular ERGs with independent edges (namely, the Erdos-Renyi and configuration models) generalize to higher-order q-Exponential Random Graphs with dependent edges in the non-Shannon case, while keeping their defining constraints (number of links and degree sequence, respectively) unchanged. We find features, such as a phase transition between sparse and dense regimes, that are absent in the original ERGs but typical of higher-order networks, plus novel phenomena such as richer assortativity and clustering profiles, which allow for the coexistence of link sparsity and triadic closure. These results show that higher-order networks do not necessarily require higher-order constraints, as they naturally arise from simpler ones in a framework that is even more agnostic than Shannon's.
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Chemical Physics
Rigorous extension of semilocal collinear functionals to noncollinear DFT using $SU(2)$ rotations
Konstantin Gaul
In the presence of
spin-orbit coupling and in geometrically frustrated materials, a noncollinear treatment the magnetization density is essential. However, in density functional theory most exchange–correlation functional approximations were originally developed for locally collinear magnetization. Many practical approaches to noncollinear DFT have emerged over the past decade. However, a first-principles connection between widely used semilocal collinear functionals and their noncollinear generalizations remains lacking. In this work, a locally exact relation between collinear and noncollinear exchange–correlation functionals is derived at the level of gradient expansions within a $u(2)$
matrix representation of the energy functional. Within this framework, collinear semilocal variables naturally acquire distinct dependencies on transverse and longitudinal magnetization gradient components. The widely used Scalmani–Frisch scheme emerges as a first-order approximation. The transformation of collinear functional derivatives to noncollinear space is implemented through numerically robust $SU(2)$ rotations. A consistent description of local
magnetic torques is demonstrated for the prototypical
spin-frustrated Cr$_3$ cluster. The approach further extends to fully nonlocal functionals and provides a direct route towards numerically stable relativistic response calculations. The influence on
magnetic properties in presence of
spin-orbit coupling is illustrated through calculations of hyperfine couplings in the high-
spin ground states of uranium and the uranium ion.
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Instrumentation and Detectors
Exploring Low Energy Excess in MINER with sapphire detectors using Convolutional Variational Autoencoder (CVAE)
D. Mondal, W. Baker, M. Chaudhuri et al. (+19 more)
As cryogenic detectors push toward ever-lower energy thresholds, their sensitivity is increasingly constrained by a persistent low-energy background known as the low-energy excess (LEE). We report observation of LEE in the MINER experiment using a sapphire ($\mathrm{Al_2O_3}$) detector at energies around 200 eV, with the excess reproducibly reappearing after each non-operational warm-up period. To address this limiting background, we implement an unsupervised convolutional variational autoencoder (CVAE) framework that identifies anomalous events through a reconstruction-based anomaly score. For the first time in a pulse-shape driven analysis, we uncover a significant deviation in the rise-time of LEE events relative to Monte Carlo simulated ideal signals. Using this feature, we develop a discrimination pipeline based on rise-time selection. This method achieves up to 53% rejection of LEE events, corresponding to an expected sensitivity improvement of nearly 10% for MINER at HFIR. These findings are consistent with a scenario in which a substantial fraction of the LEE originates from bulk-related defects or microfractures within the detector
crystal, while leaving room for additional detector-related contributions. Our result provides a powerful, data-driven pathway for mitigating LEE and enhancing the discovery potential of next-generation cryogenic experiments.
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All authors D. Mondal, W. Baker, M. Chaudhuri, J. B. Dent, B. Dutta, V. Iyer, A. Jastram, V. K. S. Kashyap, A. Kubik, K. Lang, R. Mahapatra, S. Maludze, N. Mirabolfathi, M. Mirzakhani, B. Mohanty, H. Neog, J. L. Newstead, M. Platt, S. Sahoo, J. Sander, L. E. Strigari, J. Walker
Chemical Physics
Thermal chemical reactivity in Frenkel exciton-polariton cavities
Bingyu Cui and Abraham Nitzan
Hybrid light-matter states formed under strong coupling between molecular excitations and confined electromagnetic modes provide a potential route to modify chemical properties. Here we compute and compare a thermally averaged measure of molecular chemical activity for an
equilibrium ensemble of molecules inside and outside a planar microcavity, explicitly accounting for the spatial distribution (and hence the in-plane wavevector dispersion) of the coupled light-matter states. Within a generalized Tavis-Cummings description, we find that the cavity-induced change in thermal chemical activity is most pronounced for small molecular ensembles (low areal density within a given cavity mode volume) and increases with the collective coupling strength (Rabi splitting), particularly at low temperatures. These results highlight the importance of the polariton dispersion and molecular-mode counting in assessing cavity modifications of thermally driven molecular reactivity.
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Optics
High-energy topological edge states and strain-induced multiple flat bands in a honeycomb lattice
Xiaoqin Bai, Haozhen Tian, Boris A. Malomed et al. (+2 more)
We propose a novel anti-twig edge in the honeycomb
lattice (HCL) that supports two symmetric high-energy edge states. It is different from the twig edge supporting the zero-energy flat band. Moreover, multiple flat bands are produced by applying a strain to the HCL with a twig edge or an anti-twig edge, and the suppression or enhancement of the high-energy edge state is observed. Under the edge-parallel stretch strain, the high-energy edge state band merges into the bulk band, suppressing the high-energy edge state, while the zero-energy edge state becomes delocalized. On the other hand, under the action of the edge-parallel compression strain, both the high-energy and zero-energy edge states exhibit strong localization. Pseudo-topological protection of the high-energy edge state is explored too. Finally, by reconstructing the anti-twig edge in the HCL, degenerate flat bands and strain-induced multiple flat bands are produced, and topologically protected vacated anti-twig edge states are demonstrated.
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All authors Xiaoqin Bai, Haozhen Tian, Boris A. Malomed, Rongcao Yang, Xiaojun Jia
Optics
Orbital Angular Momentum Locking via Bound States in the Continuum
Enrico Baù, Connor Heimig, Jonas Biechteler et al. (+6 more)
Optical
vortices are electromagnetic fields twisting around a phase singularity, resulting in quantized orbital angular momentum (OAM). When such
vortices are formed by evanescent hybrid light-matter quasiparticles known as polaritons, they are referred to as polaritonic
vortices (PVs). The nanometer-scale topologically robust features of such PVs promise to enable applications for lasing and thermal emission at deeply subwavelength scales. However, many conventional techniques are prone to producing multimode PVs due to poor mode selectivity, resulting in OAM mixing that degrades
vortex purity and limits their performance for high-fidelity optical information encoding and multi-dimensional imaging. To overcome this limitation, we introduce a platform that generates deeply subwavelength PVs through quasi-bound states in the continuum (qBICs) in dielectric metasurfaces. In contrast to existing approaches, the qBIC intrinsically locks the PV to a single OAM and makes it robust against the polarization state of the excitation, including linear, elliptical and circular polarization. We experimentally realize qBIC-driven PVs through the interference of
hyperbolic phonon polaritons (HPhPs) in hexagonal boron nitride by exploiting the highly uniform out-of-plane electric fields generated by the photonic qBIC, characterized via scattering scanning near-field optical microscopy. This results in HPhPs with a wavelength of around 30-40 smaller than the incident light, thereby enabling ultra-dense packing of multiple robust PVs with distinct OAM. Our platform brings PVs to the photonic chip scale, enabling applications in structured optical information transfer and communications.
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All authors Enrico Baù, Connor Heimig, Jonas Biechteler, Lina Rohrer, Michael Hirler, Haoran Ren, Stefan A. Maier, Alexander A. Antonov, Andreas Tittl
Applied Physics
Non-Hermitian fluctuations enable model-free particle manipulation
Siarhei Zavatski, Tristan Nerson, Romain Fleury et al. (+1 more)
Contactless manipulation of microscopic matter is central to applications ranging from the isolation of circulating tumor
cells in
liquid biopsies to the removal of microplastics from environmental water. Electromagnetic approaches are particularly attractive because fields can be structured within compact microfluidic systems using either light or simple electrode architectures. However, precise manipulation requires calibrated models of the field distribution and accurate knowledge of the properties of both the object and the surrounding medium, which limits applicability to well-characterized, static systems. Here we show that energy dissipation itself provides sufficient information for deterministic particle control. Instead of relying on explicit field calibration, our approach exploits an original relationship between particle position, energy dissipation, and electromagnetic body forces, which can be accessed experimentally through variations of conductance
matrices. By extracting force-shaping voltage patterns from these measurements, we demonstrate fully automated closed-loop manipulation of silica microbeads in one and two dimensions, including in the presence of other freely moving particles in a disordered background. These results establish a pathway toward deterministic force control by deliberately measuring and exploiting the non-Hermitian response of the system to engineer electromagnetic momentum transfer. This framework expands micromanipulation into realistic, dynamically evolving environments, where wave-matter interactions cannot be fully pre-characterized or eliminated through design.
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All authors Siarhei Zavatski, Tristan Nerson, Romain Fleury, Olivier J.F. Martin
Chemical Physics · Cross-listed from quant-ph
Shallow Electronic State Preparation for Quantum Chemistry with Quantum Monte Carlo Pre-Selection
Eline Welling, Lila Cadi-Tazi, Alex J.W. Thom et al. (+1 more)
Quantum computers hold great promise for molecular simulation, but noise remains a fundamental obstacle. We introduce a
Quantum Monte Carlo (QMC) pre-screening procedure that constructs compact, physically motivated Givens rotation ansätze tailored to realistic
quantum hardware. By identifying the most important wavefunction contributions early in a QMC simulation, we build
circuits that are shallower that conventional alternatives while preserving number
symmetry. Benchmarked on Quantinuum System Model H1, QMC-prescreened
circuits outperform more complex ansätze under realistic noise conditions. The method offers a practical path toward chemical accuracy on
quantum devices, by providing an adjustable trade-off between expressivity and
circuit depth to generate shallow
circuits suited to current high-noise devices, as well as deeper, more expressive
circuits that can be deployed on future lower-noise devices.
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All authors Eline Welling, Lila Cadi-Tazi, Alex J.W. Thom, Maria-Andreea Filip
Space Physics
An extended scattering kernel formalism for multi-scale gas-surface dynamics
Sabin-Viorel Anton, Bernardo Sousa Alves, Christian Siemes et al. (+2 more)
Gas-particle interactions with non-absorbing surfaces are commonly described using the scattering-kernel formalism. In this framework, an operator $\mathbf{K}$ maps incident velocity distributions to reflected velocity distributions. The operator is self-adjoint and has norm $\lVert \mathbf{K} \rVert = 1$ in an $L^2$ space weighted by the three-dimensional Maxwell-Boltzmann distribution, and must satisfy non-negativity, normalisation, and reciprocity. In standard formulations, $\mathbf{K}$ represents the aggregate effect of all gas-surface interaction mechanisms through a single operator, without distinguishing the physical scales at which these mechanisms occur. For gas scattering from a rough surface, however, it is advantageous to separate
geometric effects associated with distinct roughness scales from the underlying thermochemical processes occurring at the atomic scale. We therefore introduce a roughness-based extension of the scattering-kernel formalism, in which a local kernel is successively lifted to larger scales via single- and multi-reflection operators associated with statistically defined surface morphologies. We derive sufficient conditions under which the resulting global kernels preserve reciprocity, normalisation, and non-negativity whenever these properties hold for the smallest-scale kernel. We further show that these constructions define operators on the space of scattering kernels, and establish the associated multi-scale composition laws that allow independent roughness contributions to be combined recursively. The resulting framework provides a general basis for modelling gas-surface scattering on rough surfaces with arbitrary scale decompositions.
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All authors Sabin-Viorel Anton, Bernardo Sousa Alves, Christian Siemes, Jose van den IJssel, Pieter N.A.M. Visser
Data Analysis and Statistics · Cross-listed from cond-mat.mtrl-sci
Clustering in atom probe tomography data: coordination number metric, percolation-based parameter scaling, and size effects
Mykola Lazarev and John Banhart
The ability to identify nanometer-scale
nuclei of new phases in atom probe
tomography (APT) is often limited by the sensitivity of clustering
algorithms to user-defined control parameters. Conventional approaches typically rely on the Euclidean distance metric and consider only solute atoms, thereby discarding the solvent atoms that contain most of the spatial information. Here, we introduce a coordination-number metric based on the composition and apply it to higher-order clustering. Using various metrics, we investigate percolation in typical APT structures. By scaling clustering properties to the corresponding percolation thresholds, we define a self-similar variable that is almost invariant with respect to metrics, clustering parameters, and structural disorder. This variable provides a relevant description of clustering and enables the formal transfer of optimal parameters between clustering methods. We also study the characteristic clustering behavior in small precipitates and quantify how the precipitate-
matrix interface alters the composition spectrum and broadens the clustering curve. Finally, using simulations that incorporate finite spatial resolution, detection efficiency, and other APT reconstruction artifacts, we show that the approach based on coordination numbers effectively compensates for heterogeneous dilations and outperforms solute-density-based methods in all tested scenarios.
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Data Analysis and Statistics
Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
Oz Amram, Marco Letizia and Mikael Kuusela
Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses. These techniques offer an opportunity to enhance the overall discovery potential of modern experiments, especially in regimes where theoretical guidance is scarce. In this document, we review the conceptual framework behind the main classes of AI-based model-agnostic strategies. We discuss the potential pitfalls of these methods, and strategies for their validation and interpretation. We aim for this document to serve as a useful reference both for practitioners and for researchers interested in learning more about these model-agnostic search strategies.
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Optics
Rewritable Chirality of Metasurfaces with Permittivity-Asymmetric Flatband Quasi-Bound States in the Continuum
Tao Jiang, Haiyang Hu, Dmytro Gryb et al. (+4 more)
Flatband eigenstates are widely applied to enhance angle-robust light-matter interactions in metaphotonics. However, controlling the polarization of flatbands remains challenging, as it is usually fixed once the metasurface is fabricated, with no options of post-fabrication modification. Here, we present a rewritable permittivity-asymmetric quasi-bound state in the continuum ({\epsilon}-qBIC) metasurface platform, where selective polymethyl methacrylate (PMMA) coating of a silicon double-nanorod unit
cell establishes a circularly polarized flatband state. By varying the PMMA thickness, the polarization of this state can be further controlled in the range from the right-circular to linear and to left-elliptical. The flatbands maintain stable resonance positions and robust far-field polarizations for the incidence angles up to 10 degrees. Importantly, the PMMA layer can be removed, recoated, and re-patterned on the same nanostructure, providing a pathway to rewrite the optical response. Building on this capability, we experimentally demonstrate the chirality encoding by spatially selective PMMA coating. The results establish a practical strategy for realizing high quality factor flatband metasurfaces with rewritable chirality, thus opening opportunities for applications in
chiral encoding and chiroptical photonic devices in general.
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All authors Tao Jiang, Haiyang Hu, Dmytro Gryb, Leonardo de S. Menezes, Maxim V. Gorkunov, Alexander A. Antonov, Andreas Tittl
Physics and Society
Vehicle Overacceleration – A Fundamental Microscopic Mechanism for Traffic Breakdown Control Using Automated Vehicles and AI
Boris S. Kerner
This review article addresses a fundamental controversial question in traffic theory: Is the nucleation character of traffic breakdown at a bottleneck governed by vehicle overdeceleration (overbraking) or by discontinuous vehicle acceleration, referred to as vehicle overacceleration. This question is of particular importance in the context of automated vehicles and AI, whose individual dynamic behavior can enable reliable strategies for traffic breakdown control in the future. We show that, in both human-driven and automated traffic flow, traffic breakdown is governed by vehicle overacceleration rather than vehicle overdeceleration. With this objective, in microscopic modeling we separate traffic breakdown caused by overacceleration from traffic instabilities caused by overdeceleration due to braking behavior, while following recent papers [Phys. Rev. E 108, 014302 (2023); 108, 064305 (2023); 112, 034309 (2025)].
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Optics
Mach-Zehnder based Rotational Shearing Interferometer for Non-destructive Testing using Spatial Phase-Shifting Shearography
Valentin-Johannes Bastgen, Michael Schuth and Georg von Freymann
This work introduces a novel optical setup based on a Mach-Zehnder interferometer, enabling spatial phase shifting shearography with rotational shear. The optical concept employs a virtual double slit configuration, which decouples the adjustment of the shear amount from the generation of the spatial carrier frequency, thereby enabling flexible control of the measurement sensitivity. Rotational shear is generated within the optical setup by means of image rotation using Dove prisms. Since the proposed system is based on spatial phase shifting shearography, full field measurements can be performed at the camera frame rate, making the method suitable for fast non destructive testing under industrial conditions. In contrast to conventional linear shear configurations, the rotational shear approach is sensitive to tangential displacement gradients around the centre of rotation. In this paper, the proposed rotational shear approach is compared with linear shear configurations, with the results demonstrating reliable detection of relevant defects. Spatial phase shifting shearography combined with rotational shear offers significant potential for industrial non destructive testing applications, particularly in sealing technology and for the inspection of rotationally symmetric components.
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Accelerator Physics
Attosecond Compression of Relativistic Electron Pulses via Continuous Harmonic Undulator Resonance
Hao Sun, Xiaofan Wang, Xiaozhe Shen et al. (+4 more)
Extending megaelectronvolt ultrafast electron diffraction (MeV UED) into the attosecond regime is essential for resolving intrinsic structural dynamics, yet requires simultaneously controlling electron-pulse duration and arrival-time stability. Here, we propose a generalized harmonic
laser-electron interaction that extends beam modulation into a continuous harmonic regime. We demonstrate that highly detuned, non-integer harmonic modulation via a single-period undulator achieves stronger coupling efficiency than conventional integer-harmonic resonance. Driven by a mid-infrared seed
laser whose wavelength is a small fraction of the nominal resonant wavelength, this mechanism enables effective longitudinal phase space manipulation. It facilitates attosecond compression with minimal
laser-induced energy spread, preserving the beam quality required for high-fidelity diffraction. Furthermore, deriving both the modulation and experimental pump
lasers from a common source intrinsically locks their relative timing. Simulations demonstrate 680-as pulse durations and 470-as arrival-time jitter, establishing a viable route to attosecond MeV UED for resolving coupled electron-
nuclear dynamics.
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All authors Hao Sun, Xiaofan Wang, Xiaozhe Shen, Huaiqian Yi, Cheng-Ying Tsai, Weiqing Zhang, Xueming Yang
Atmospheric and Oceanic Physics
Forecasting threshold exceedance of atmospheric variables at a specific location
Roberta Baggio and Jean-François Muzy
Accurate short-term
forecasting of extreme weather events is important for early warning and risk mitigation. We compare two approaches for predicting site-specific threshold exceedances of weather variables: direct binary probabilistic models trained on thresholded outcomes and full-distribution parametric models trained on the continuous target. Using an analytically tractable Gaussian random-location model, in which the distribution is predictably shifted by the
covariates, we quantify the consequences of the information loss induced by thresholding and derive the rare-event behavior of prediction errors and forecast skill. The analysis predicts an increasing relative advantage of the full-distribution approach as event probability decreases, because binarization progressively discards information contained in the continuous response. We then compare the two approaches to forecast wind speed and accumulated rainfall at various weather station sites over southeastern France using the same hybrid neural-network architecture. Although wind speed and rainfall depart from the toy-model assumptions, its main qualitative predictions are recovered for both variables: the relative advantage of distributional modeling increases toward rarer thresholds. This agreement further suggests that a substantial fraction of the forecastable signal associated with extreme events arises from predictable shifts in the conditional distribution. For the reasonably suitable parametric families examined, the results show limited sensitivity to the selected class of distribution. Overall, the results highlight the statistical advantage of training on continuous observations rather than on thresholded binary outcomes when
forecasting rare threshold exceedances.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Finite-inertia effects in Langevin dynamics of a lopsided elastic dumbbell using exponential-time differencing schemes
Lei Song, Dingyi Pan and Nhan Phan-Thien
Inertia effects in the Langevin dynamics of a lopsided elastic dumbbell are investigated using exponential-time-differencing (ETD) integrators for the corresponding stiff
stochastic equations at small mass limit. Starting from the bead-level underdamped Langevin model, we formulate the dynamics in modal coordinates, highlighting two distinct friction scales: an additive friction $\zeta_{\rm trans}=\zeta_1+\zeta_2$ controlling translation ($\zeta_i, i=1,2$ are the friction factor on bead $i$), and an effective internal friction $1/\zeta_{\rm eff}=1/\zeta_1+1/\zeta_2$ controlling configurational relaxation, with relaxation time $\tau_R=\zeta_{\rm eff}/H$ for a Hookean spring of stiffness $H$. We
benchmark ETD against Euler–Maruyama and overdamped Brownian dynamics using
equilibrium statistics, time-domain autocorrelations, and frequency-domain power spectra of the end-to-end vector. When time is rescaled by $\tau_R$, configurational and orientational relaxation curves collapse across asymmetry ratios, showing that the dominant long-time structural dynamics remains close to the overdamped description. Inertial signatures are instead confined to short-time transients, high-frequency modifications of the configurational spectrum, and a transient coupling between translational and internal modes. This study provides a practical and accurate route for lopsided dumbbells across overdamped and weakly underdamped regimes, and clarify how mass and friction asymmetry affect the translational and internal dynamics.
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Optics
Preventing the Breakdown of Tight-Binding Waveguide Optics by Löwdin Orthogonalization
Konrad Tschernig, Florian H. Huber, Janik Wolters et al. (+1 more)
Many advancements in optics have relied on the tight-binding approximation, which simplifies the description and prediction of complex system behaviors. This approximation describes the dynamics of the total light field by examining the coupling between the guided modes of individual single-mode substructures – also known as coupled mode theory. However, the underlying assumption, that the guided modes of individual waveguides form an orthogonal basis, breaks down when waveguides are brought into close proximity or when larger arrays are considered. In this work, we systematically analyze the consequences of this non-orthogonality and show that it leads to a generalized
eigenvalue problem involving an overlap
matrix, causing a fundamental mismatch between the standard TB model and solutions of the paraxial wave equation. To resolve this issue, we introduce a modified TB framework based on the Löwdin orthogonalization, which constructs an orthonormal basis from the non-orthogonal guided modes while minimally altering their physical shape and preserving their
symmetry properties. The resulting Löwdin-TB method restores the standard
eigenvalue problem and yields excellent agreement with exact beam propagation simulations across a wide range of system sizes and waveguide separations. Furthermore, it captures important physical effects, such as enhanced long-range coupling and nontrivial hopping phases, that are absent in the standard approach.
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All authors Konrad Tschernig, Florian H. Huber, Janik Wolters, Jasmin Meinecke
Chemical Physics · Cross-listed from stat.ML
Free energy Estimation on Any State Space
Jiajun He, Zijing Ou, Francisco Vargas et al. (+4 more)
Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformations, ranging from direct estimation, quasistatic integration, to finite-time averaging. Recent work [He and Du et al., 2025] learns neural transports to significantly accelerate the efficiency in the finite-time regime. In this paper, we generalize this framework to arbitrary state spaces. Building on this view, we develop a generalized neural transport learning approach for efficient estimation. Experiments validate the effectiveness and efficiency of the proposed method beyond continuous settings, extending to discrete and
multimodal spaces as well as autoregressive settings. Beyond free energy estimation, we establish algebraic identities and reveal a group-theoretic structure linking infinitesimal time reversal and generalized Doob's $h$-transforms, showing that their compositions form a generalized dihedral group.
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All authors Jiajun He, Zijing Ou, Francisco Vargas, Yingzhen Li, José Miguel Hernández-Lobato, Carles Domingo-Enrich, Yuanqi Du
Space Physics
Extreme, transient bursts of energy in the auroral ionosphere. I. Predictive radar tracking
Magnus F Ivarsen, Jean-Pierre St-Maurice, Devin R Huyghebaert et al. (+7 more)
Three-metre Farley-Buneman irregularities observed by the icebear VHF radar organize into clusters whose apparent motion follows the electric field mapped from the magnetosphere. We track these clusters automatically: each is bounded by an $\alpha$-shape at every time step, consecutive frames are associated by an optimal assignment combining shape overlap with a predicted displacement. Births, deaths, splits, and mergers are monitored, and each trajectory is reduced to per-segment velocities by piecewise linear regression. Tracked speeds are validated against in-situ ion drifts measured by DMSP F16 during two conjunctions in May 2021. Across four years of disturbed conditions, the speed distribution of 74,517 tracked clusters agrees with Swarm A cross-track ion drifts to within a factor of two in probability density for all speeds between 300 and 4000 m/s, and the radar-tracked speed distribution continues as a power law well beyond the noise limit imposed on Swarm by spacecraft attitude jitter. Binning by geomagnetic activity yields a parameterization of the field dispersion conditional on threshold exceedance, $\sigma^2 = 3.68 \times 10^5$ (SME/100 nT)$^{0.353}$~m$^2$ s$^{-2}$, equivalent to 30 to 60 mV/m across the observed activity range, which supplies the amplitude statistics entering the variance term of height-integrated Joule dissipation. During the 10 May 2024 super-storm, on closed field lines equatorward of the dayside cusp, we retrieved an upper-tail sample of this distribution, finding a cluster moving at $11,240\pm660$ m/s and implying a field of approximately 560 mV/m. Together with the unstable fraction of a space weather model's grid volume, our parameterization can in future close the sub-grid contribution to the storm-time heating budget in the auroral ionosphere.
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All authors Magnus F Ivarsen, Jean-Pierre St-Maurice, Devin R Huyghebaert, Yukinaga Miyashita, Saif Marei, Jordan Cho, Mahith Madhanakumar, Megan Gillies, Dan Billett, Glenn C Hussey
Physics Education
Data evaluation processes of different-sized datasets: an eye-tracking with concurrent think-aloud study
Gregor Benz, Sarah Taha and Andreas Vorholzer
Considering the increasing availability of digital tools and measurement data in physics classrooms, students are more frequently confronted with larger
datasets. While existing literature often assumes that working with large amounts of data is more complex, recent work by Benz, Ludwig, and Vorholzer [Sci. Ed. 119, 1669-1700 (2025)] suggests that this is not necessarily the case when data are presented in diagrams. Building on this, the present study investigates how students evaluate small and large
datasets in diagrammatic representations. Using a process-oriented approach, we analyzed the data evaluation processes of $N=20$ university physics students via eye-tracking and concurrent think-aloud. The results indicate that
dataset size, in interaction with the visibility of patterns in the data, systematically shapes how students reason with measurement data. Larger
datasets support more pattern- and trend-based evaluation and can lead to more unambiguous conclusions. In contrast, smaller
datasets are associated with a stronger focus on single measurement points and increased expressions of uncertainty, including an articulated "need for data," alongside an overinterpretation/overweighting of single measurements. The observed shift from uncertainty-related and locally focused evaluation toward more integrative and trend/pattern-based
reasoning suggests that larger
datasets may support students in integrating multiple data points into more coherent interpretations of measurement data. Overall, the study contributes to a process-oriented understanding of how students engage with measurement data in physics education.
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Optics
Towards Neuromorphic Event-Based Sensing for High-Speed Multi-Spectral Classification and Tracking of Microparticles
Joana M. Teixeira, Tomás Lopes, Tiago D. Ferreira et al. (+3 more)
Conventional image-based microfluidic systems face an inherent trade-off between throughput, imaging speed, and data bandwidth, limiting their ability to monitor high-velocity flows without significant motion blur or prohibitive data generation. Event-based sensing has emerged as a high-speed, low-power alternative, but has so far been largely restricted to tracking monodisperse, spherical particles. In this work, we introduce a microfluidic sensing platform that enables the simultaneous extraction of kinematic and spectral information from polydisperse microparticles using a neuromorphic imaging approach. By integrating a spatially multiplexed RGB filter mask with an asynchronous event-based sensor, spectral signature and motion are encoded directly at the sensing stage, eliminating the need for image reconstruction or learning-based inference. The system achieves sub-millisecond temporal resolution and maintains robust classification performance across a broad range of particle sizes and flow velocities, including under non-laminar conditions, reaching up to 82% accuracy for classification of colored particles within the 0.08-0.18 mm range. The event-driven architecture reduces data bandwidth by >240x compared to conventional high-speed imaging, while sustaining an area throughput of 460 mm^2/s. By providing a computationally efficient and low-latency particle characterization, this framework paves the way for a scalable solution towards high-speed, label-free screening of heterogeneous analytes in clinical diagnostics and environmental monitoring.
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All authors Joana M. Teixeira, Tomás Lopes, Tiago D. Ferreira, Catarina S. Monteiro, Pedro A.S. Jorge, Nuno A. Silva
Fluid Dynamics
amerta: A Python Library for Idealized 1D Saint–Venant Dam-Break Simulation
Dasapta E. Irawan, Sandy H. S. Herho, Iwan P. Anwar et al. (+6 more)
The Saint-Venant shallow water equations (SWE) govern depth-integrated free-surface flows arising in dam-break inundation, flood routing, tsunami runup, and estuarine
tidal dynamics. Closed-form analytical solutions exist only for highly idealized Riemann configurations, making rigorously verified numerical solvers essential. This work presents amerta, an open-source Python library that solves the one-dimensional frictionless Saint-Venant system on a uniform Cartesian grid using Monotone Upstream-centered Schemes for Conservation Laws (MUSCL) reconstruction with a minmod slope limiter, the Harten-Lax-van Leer-Contact (HLLC) approximate Riemann solver, and two-stage strong-stability-preserving Runge-Kutta (SSP-RK) time integration. Numba just-in-time (JIT) compilation accelerates the performance-critical kernels. The solver is verified end-to-end against the four canonical Riemann configurations: wet-bed dam break, dry-bed dam break, double rarefaction, and double shock. A six-component post-processing pipeline quantifies space-time
topology, final-time error norms with empirical
quantile decomposition, self-similarity collapse onto the analytical Riemann fan, integral-norm
evolution, boundary-flux-corrected mass and energy diagnostics, and phase-plane analysis against analytical wave curves. The implementation conserves discrete mass to floating-point precision, satisfies discrete
entropy admissibility identically, and reproduces all four analytical wave-curve geometries to within sub-centimetre accuracy in the depth-velocity phase plane. The complete source code, analytical-solution evaluators, post-processing scripts, and Network Common Data Format (NetCDF) archives are released under the MIT license.
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All authors Dasapta E. Irawan, Sandy H. S. Herho, Iwan P. Anwar, Faruq Khadami, Astyka Pamumpuni, Rendy D. Kartiko, Edi Riawan, Rusmawan Suwarman, Deny J. Puradimaja
Accelerator Physics
Explicit Turn Resolution with Anisotropic Homogenisation for Efficient 3D Magneto-Thermal Finite-Element Simulation of Large-Scale No-Insulation HTS Magnets
Louis Denis, Erik Schnaubelt, Julien Dular et al. (+3 more)
No-insulation (NI) and metal-insulation (MI) high-temperature superconducting (HTS) magnets require three-dimensional (3D) models to describe the current distribution around critical current defects. In this work, we design and validate the EXTRA homogenisation method, standing for explicit turn resolution with anisotropic homogenisation method. It allows 3D magneto-thermal finite-element (FE) simulations of large-scale magnets to be performed with high accuracy at a reasonable computational cost. The method combines the anisotropic homogenisation of turn-to-turn contact layers (T2TCLs) and their neighbouring winding turns with the explicit resolution of specific T2TCLs. In particular, the inner- and outermost winding turns and adjacent contact layers are explicitly resolved to properly describe the current distribution near current leads. In addition, the method is able to simulate local $J_{\textrm{c}}$ defects for a broad range of turn-to-turn contact resistances, provided the winding turns and T2TCLs next to the defect are explicitly resolved. For efficiency, the resolved T2TCLs are modelled using the surface contact approximation. The consistency of the proposed method is first verified on a 50-turn single pancake
benchmark. It is shown to reproduce AC losses and temperature distributions obtained with a turn-resolved FE reference model, for both nominal operation and during thermal runaway. The computational efficiency of the EXTRA method is demonstrated with the simulation of a stack of three 150-turn pancake coils, for which computation time is reduced by a factor of up to 13 with respect to a turn-resolved FE reference model. Finally, the results of a large-scale 3D FE simulation, currently out of reach of turn-resolved models, are provided for an insert HTS magnet with 10,000 turns. The EXTRA method is open-source and input files to reproduce all results are made available.
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All authors Louis Denis, Erik Schnaubelt, Julien Dular, Mariusz Wozniak, Benoît Vanderheyden, Christophe Geuzaine