Fluid Dynamics
A Variational Principle for the Vorticity Equation
Ahmed Farooq
We present a variational formulation of the incompressible vorticity equation based on Gauss's principle of least constraint using the Gauss constraint functional $\Zvec_\omega$. The central result is the Euler–Lagrange equation $\mathbf{Z}_\omega = -\nabla h$, where $h = \mathbf{u} \cdot\boldsymbol{omega}$ is the helicity density. This reveals that the helicity gradient $\nabla h$ acts as the constraint force maintaining the solenoidality of the vorticity field, exactly as the pressure gradient $\nabla p$ maintains incompressibility in Taha et al.'s pressure-gradient minimization principle. The helicity density naturally emerges as the Lagrange multiplier enforcing $\nabla \cdot \boldysmbol{omega}=0$, and at the solution the flow minimizes the norm of the helicity gradient $\|\nabla h\|^2$. This establishes the exact duality: helicity is to vorticity as pressure is to velocity. We apply the variational principle to the Burgers
vortex and verify the the Euler-Lagrange equation. The variational principle connects to Moffatt's helicity conservation
theorem, Arnold's
geometric formulation of ideal fluid flow, and Kambe's gauge-theoretic formulation. This work provides a unified variational framework for fluid dynamics that spans classical mechanics,
geometric mechanics, and topological field theory. We discuss how this work may provide a theoretical foundation for understanding the role of helicity gradients in boundary layer dynamics, with potential implications for the formation of coherent structures and the onset of transition. These applications are reserved for future work.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Meandering stripes in the frustrated $J_1$-$J_2$ Ising model on the honeycomb lattice
Denis Gessert, Martin Weigel and Wolfhard Janke
We study the frustrated $J_1$-$J_2$ Ising model on the honeycomb
lattice with
ferromagnetic nearest-neighbor couplings fixed at $J_1=1$ and strong
antiferromagnetic next-nearest-neighbor interactions, i.e., $J_2 \leq -1/4$. Little is known for this range of $J_2$, whereas for less negative values of $J_2$ the system orders ferromagnetically at low temperatures and appears to remain in the Ising universality class. In previous work it was shown that the model has a largely degenerate ground state, and it was conjectured that there is some kind of phase transition. We introduce a complex-valued nematic order parameter, which can differentiate between the high-temperature paramagnetic phase and the observed partially-disordered stripe phase at lower temperatures. Configurations in this phase consist of stripes of
spins parallel with respect to one
lattice direction, which collectively meander along the remaining two, producing partially disordered ground states. The sharp peaks in the specific heat observed in earlier work only appear when using periodic boundary conditions and are absent for free boundaries. Additionally, we reveal a striking dependence of the behavior on the aspect ratio of the considered samples. Ultimately, even a careful finite-size scaling analysis for $J_2 = -0.5$ and $J_2 = -1$ is unable to clearly discern between a crossover without any singularities and some form of continuous transition, including the possibility of an infinite-order transition of the Berezinskii-Kosterlitz-Thouless (BKT) type. For $J_2=-1/4$ we find that the system remains disordered at all temperatures and that it exhibits a finite ground-state
entropy per site, for which our simulations provide the accurate asymptotic estimate $S(T=0)/N = 0.230\,960\,93(14)$ in the thermodynamic limit $N\rightarrow\infty$.
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Fluid Dynamics
Droplet coalescence in fluids obeying Darcy's law
Jing Wang, Haicen Yue, Nandish Vora et al. (+4 more)
During drop coalescence, a connecting bridge of fluid forms and rapidly expands due to surface tension. For spherical drops, these dynamics are well understood in both the viscous and inertial regimes. However, under strong confinement, fluid motion is fundamentally altered by
geometric constraints, leading to dissipation on small lengthscales. We investigate the coalescence of drops confined in a Hele-Shaw
cell (two parallel plates separated by a narrow gap). In this geometry, the depth-averaged flow is governed by Darcy's law while surface tension drives the interface motion. We identify two distinct temporal regimes in the
evolution of the bridge radius that evolves as a power law ($R_b$). At early times, the bridge grows as $R_b \sim t^{1/2}$, which results from a confinement-dependent meniscus instability that determines the initiation of contact between droplets prior to bridge formation. At later times, the bridge growth slows substantially and follows $R_b \sim t^{1/5}$, consistent with recent theoretical predictions for Darcy-governed coalescence. We show that the transition between these regimes is controlled by several
geometric lengthscales. In particular, the onset of the Darcy regime occurs when the interface radius of
curvature becomes comparable to the plate spacing, such that the flow becomes fully confined. Using a boundary integral formulation, we find that both scaling laws for $R_b$ are determined by the bridge width. Together, these results identify a new universal regime of drop coalescence in a broad class of fluids obeying Darcy's law.
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All authors Jing Wang, Haicen Yue, Nandish Vora, Tabitha C. Watson, Zhengyan Lin, Itamar Kolvin, Justin C. Burton
Applied Physics
Shaping liquids into space structures - microgravity-assisted design and manufacturing of minimal surfaces
Erez Hochman, Aaron Sprecher, Amos A. Hari et al. (+1 more)
This work advances a fundamentally new approach to space-based construction by using
liquid self organization in microgravity as a generative design and fabrication principle. Building on the LiquiFab method, we demonstrate how minimal surface architectures - traditionally dependent on complex additive manufacturing - can instead emerge directly from the physics of fluid interfaces shaped by programmable boundary conditions. We present a simulation-to-fabrication workflow, that includes a boundary-driven minimal-surface solver integrated in Grasshopper/Rhino, and an experimental system that implements in a neutral buoyancy environment simulating microgravity. This enables the generation of customizable Schwarz-P-inspired with tunable geometry and thickness, illustrating a scalable pathway for material-efficient, on-orbit fabrication. To validate performance in true microgravity, a flight experiment on the International Space Station is scheduled for the first quarter of 2027 and we here detail the additional considerations required for this experiment.
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All authors Erez Hochman, Aaron Sprecher, Amos A. Hari, Moran Bercovici
Geophysics · Cross-listed from stat.AP
Matching Urban Flood Sensor Placement to Monitoring Objectives Using Bayesian Optimal Experimental Design
Chen Cheng, Vinh Ngoc Tran, Jiayuan Dong et al. (+5 more)
Flood-monitoring sensors are often placed according to coverage, access, or expected inundation. However, the value of a measurement depends on the prediction or decision it is intended to inform. Using tRIBS-Urban simulations and a neural-network surrogate of the August 2014 metropolitan Detroit flood, we examine how this learning target changes single-sensor placement. Across 2,576 candidate locations, we compare parameter-oriented optimal experimental design (PO-OED), which values expected information gain (EIG) about model parameters, with goal-oriented optimal experimental design (GO-OED), which values EIG about specified flood predictions. We also examine how parameter EIG evolves during the event, and illustrate that parameter learning translates unevenly into reductions in predictive uncertainty across locations and lead times. Under GO-OED, point-depth targets favor nearby locations, whereas regional-average and regional maximum-depth targets can favor nonlocal locations. Weighted multi-point objectives retain similar broad spatial patterns, although their computed max-EIG locations differ. Public geospatial data further provide illustrative feasibility and contextual classifications for deployment screening. These results show how monitoring objectives shape sensor placement in optimal experimental design, and motivates an objective-first workflow that defines the intended prediction and priorities, applies field-verified restrictions, and ranks locations by EIG.
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All authors Chen Cheng, Vinh Ngoc Tran, Jiayuan Dong, Sarah Whitaker, Shannon Bergt, John Ziker, Valeriy Y. Ivanov, Xun Huan
Plasma Physics
First step toward multi machine ELM energy scalings and extrapolations to SPARC and ITER
R. Perillo, A. Redl, T. Eich et al. (+14 more)
It is shown that the ELM energy loss normalized by the
plasma stored energy ({\Delta}EELM/Wplasma) for high-density small/QCE ELM regimes scales inversely with the separatrix
turbulence parameter a_t. In contrast, the neoclassical electron collisionality at the pedestal top, nu*e,neo, expected to regulate {\Delta}EELM/Wplasma according to the Loarte scaling (
Plasma Phys. Control. Fusion 2003 45 1549), does not adequately capture {\Delta}EELM/Wplasma data for peeling-ballooning-limited type-I ELMs and ballooning-limited small/QCE ELMs, limiting its applicability for extrapolation to one scenario window. A multi-machine database including seven tokamaks and with {\Delta}EELM/Wplasma ranging from 0.5% to 14%, has been analyzed. A regression analysis on only type-I ELMs yields (({\Delta}E_ELM)/W_
plasma )_(Type-I ) [%]=6.8*T_(e,ped)^0.03 n_(e,ped)^(-0.4) ąppa}^(-0.4) R_major^0.4, corresponding to {\Delta}EELM/Wplasma =4.5% for nominal SPARC pedestal parameters and 12% for the ITER D-T Q=10 scenario. For the small/QCE ELM class, however, as a_t increases, the pedestal moves toward a ballooning-limited boundary, the toroidal mode number increases, the ELM frequency rises following the scaling f_ELM=46e^((2.25*a_t)), and {\Delta}EELM/Wplasma decreases via the relation ({\Delta}E_ELM)/W_
plasma [%]=1.6e^(-({\alpha}_t/2)). For SPARC QCE-relevant a_t=0.86 and ITER high-fueling scenario a_t = 0.64, the scaling favorably predicts {\Delta}EELM/Wplasma of 1.0% and 1.2%, respectively, with values below 1% if the small/QCE ELM regime is pushed beyond a_t >1. The small/QCE ELM-fitted results represent an initial step toward future analysis on broader
datasets, which will be necessary to improve the accuracy of projections for future reactor-relevant scenarios.
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All authors R. Perillo, A. Redl, T. Eich, C. J. Lasnier, A. Nelson, R. Rizkallah, D. Silvagni, A. Stagni, J.A. Boedo, A. McLean, P. Traverso, N. Vianello, the DIII-D team, JET contributors, the ASDEX Upgrade team, the TCV team, the EUROfusion Tokamak Exploitation team
Optics
A Fourier Neural Operator for Accelerated Discretization-Invariant Solutions of the Radiative Transfer Equation
Daniel Carne
The radiative transfer equation (RTE) governs thermal radiation in participating media, which is critical to modeling combustion, atmospheric, high-temperature, and radiative thermal management applications. However, due to the inherently high-dimensional nature of radiative transfer, numerical solutions to the RTE induce significant computational cost. This work develops a
Fourier neural operator (FNO) as a fast, discretization-invariant, surrogate model for predicting the absorbed heat flux field in participating media. A
dataset is generated on a
benchmark 2-dimensional problem modeling thermal surface emission into a participating medium with spatially varying properties using Monte Carlo simulations. The FNO is trained to learn the solution operator, mapping the input property fields and geometry to the resulting heat flux field. The trained FNO provides up to 26-times computational acceleration compared to Monte Carlo simulation at equivalent error. Furthermore, as the FNO learns the solution operator and not an image-to-image mapping, the trained FNO accurately generalizes to various spatial discretizations, including those not seen in the training
dataset. A spectral analysis of the spatial frequencies present in the heat flux prediction demonstrates the FNO's ability to suppress the high-frequency noise present in the training
dataset, while preserving the physically meaningful low-mid frequency spectrum. These results demonstrate the
Fourier neural operator surrogate model can provide accurate, computationally efficient, and discretization-invariant predictions for radiative transfer in a participating medium. By learning the underlying solution operator, this approach moves toward the development of surrogate models for radiative transfer capable of generalizing across a broad range of geometries, discretizations, and boundary conditions.
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Instrumentation and Detectors
Preventing quart-NaI adhesion in Bridgman growth using ammonium iodide
Lam Tan Truc, N. T. Luan, Gul Rooh et al. (+7 more)
Adhesion between NaI(Tl) single
crystals and the walls of quartz ampoules remains a major limitation for Bridgman growth under sealed conditions, particularly for applications requiring ultra-radiopure scintillators for dark matter searches, where sealed handling is essential. In this study, ammonium iodide (NH4I) was used as an additive to generate HI in situ, thereby suppressing the NaOH-SiO2 reaction that forms adhesive sodium silicate phases. Small-diameter
crystals (8 mm) were first grown to determine the NH4I concentration required to eliminate adhesion. The optimized condition was then applied to the growth of a large NaI(Tl)
crystal. A crack-free and bubble-free NaI(Tl)
crystal with dimensions of 3 inches in diameter by 3 inches in length was successfully grown. The
crystal exhibited a light output of 59,000
photons/MeV, which is higher than that of the commercial NaI(Tl)
crystals used as references in this study.
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All authors Lam Tan Truc, N. T. Luan, Gul Rooh, O. Gileva, K.A. Shin, H.S. Lee, A. Iltis, C.R. Byeon, C.H. Lee, H.J. Kim
Optics
Neuromorphic Infrared Fibre-Optic Event-Based Sensing with Fast and Efficient Photonic-Electronic Spiking Neurons
Dylan Black, Giovanni Donati, Joshua Robertson et al. (+5 more)
Current photonic technologies for remote sensing require the capture of large amounts of data, suffering as a result from high energy consumption, excessive data redundancy and storage, and costly data processing requirements, limiting their ability for direct and efficient edge-processing for rapid decision making and alarm triggering. In contrast, biological sensing systems, given their event-driven nature and in-sensor processing capabilities offer energy-efficient and practical alternatives. In this work, we draw direct inspiration from the neural spiking in biological sensory systems, to propose a novel neuromorphic event-based photonic technology permitting the remote sensing of environmental events-of-interest efficiently, at high speeds and across a wide dynamic frequency range. Our approach combines widely-deployed fibre-optic telecommunication links and photo-detecting resonant tunnelling diodes (pRTD) acting as light-triggered spiking
neurons. We demonstrate that this new neuromorphic photonic sensing approach allows the remote detection of different types of environmental events, including temperature variations, strain-induced motion, audio and air
turbulence, with high temporal resolution (encoding them with fast nanosecond-rate neural-like spikes). These results pave the way for novel light-enabled neuromorphic remote infrared fibre-optic sensing networks that are fast, efficient, event-driven, scalable, permit direct processing at-the-edge, and offer practical alternatives to current data-intensive approaches for photonic remote sensing.
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All authors Dylan Black, Giovanni Donati, Joshua Robertson, Qusay Raghib Ali Al-Taai, José Figueiredo, Edward Wasige, Bruno Romeira, Antonio Hurtado
Physics and Society
From Timbuktu to SKA: Who owns the Astronomy knowledge Africa produces?
Rhea Koch and Amare Abebe
Africa has deep and diverse traditions of astronomical knowledge, ranging from archaeological astronomy, pharaonic
stellar timekeeping, and manuscript astronomy in Timbuktu and the Sahel, to ecological seasonal astronomy in southern Africa and calendrical computation in the Ethiopian tradition. These multiple epistemic traditions form part of a long intellectual history that precedes and intersects with contemporary astronomy on the continent. In the context of this long-standing intellectual heritage, an important question arises regarding the contemporary circulation of African astronomical knowledge within the global scientific system. This study therefore examines where African astronomical knowledge is produced, validated, and circulated in modern scholarly communication. Using bibliometric data from the Web of Science Core Collection, our analysis investigates publication and citation patterns in the research area Astronomy and Astrophysics between 2000 and 2025, with particular
attention to the publication venues of African scholars. The findings highlight a critical paradox: while African researchers are active contributors to global astronomical discovery, the intellectual capital generated through this work is largely stored, validated, and circulated through publication systems located outside the continent. This pattern reflects broader asymmetries within global scholarly communication, where dominant publishing infrastructures shape visibility, citation impact, and authority. By applying a decolonial lens to metrics such as citation impact and ownership, the paper calls for a critical reassessment of the academic practices that sustain epistemic coloniality. It concludes that achieving scientific equity requires a strategic shift in publication choices to build and fortify a sovereign African knowledge archive.
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Atmospheric and Oceanic Physics
Remote sensing of the temporal and spatial variability of atmospheric refractivity using ADS-B interferometry (ADSBi)
Ollie Lewis, Chris Brunt, Malcolm Kitchen et al. (+2 more)
The extreme variability of humidity in the lower atmosphere over short spatial and temporal scales presents an enormous challenge for existing observing systems. Variations in humidity can be detected due to changes in the refractive properties of the atmosphere, which influence the propagation of electromagnetic radiation. We demonstrate the ability to retrieve high-resolution refractivity gradient measurements using an interferometer to measure the refraction of the Automatic Dependent Surveillance-Broadcast (ADS-B)
radio transmission routinely broadcast by commercial aircraft. The ADS-B interferometry (ADSBi) technique is sensitive to small-scale spatial and temporal variability in the refractivity gradient due to changes in humidity. We demonstrate how the
retrieval of refractivity profiles is possible exploiting the differences in the observed refraction of
radio transmissions broadcast by aircraft at different distances and altitudes.
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All authors Ollie Lewis, Chris Brunt, Malcolm Kitchen, Neill E. Bowler, Edmund K. Stone
Computational Physics · Cross-listed from quant-ph
Symmetry Constrained Quantum Error Mitigation for the Schwinger Model
Alexander Tomlinson, Graham Van Goffrier, Bipasha Chakraborty et al. (+1 more)
Quantum error mitigation (QEM) is at the very heart of near-term
quantum simulations and
lattice gauge theories are no exceptions, rather their physical
symmetries provide natural consistency checks on noise
quantum states. In this work, we exploit the parity and
fermion-number
symmetries of a gauge theory, the (1+1)-dimensional Schwinger model, under depolarising noise and investigate
symmetry verification under digital
quantum simulation. We investigate two set-ups -
symmetry- sector post-selection in adiabatic state preparation followed by real-time measurements of the
chiral condensate and
symmetry verification within a variational
quantum eigensolver (VQE). In the first case, post-selection reduces the bias in the
chiral condensate consistently removing up to 60% of the
quantum noise induced error in our system. Motivated by the observed regularity of the residual bias (in the low-noise regime), we further introduce a global-noise calibration obtained from classically accessible smaller
lattices and implemented on larger
lattices recovering noiseless
chiral condensate values within statistical uncertainty. However, in VQE,
symmetry verification does not seem to generally improve the optimised parameters or the fidelity of the prepared states, although it reduces the bias in the estimated ground-state energy. This demonstates that improving a noisy cost-function
estimator in variational
algorithms does not necessarily improve the outcome of the
algorithm. Our results show the strength of
symmetry verification in different approaches while establishing that its usefulness critically depends on where it is applied in the computational workflow, and provide practical guidance for
symmetry-assisted
quantum error mitigation in
quantum simulations of
lattice gauge theories.
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All authors Alexander Tomlinson, Graham Van Goffrier, Bipasha Chakraborty, Zhenyu Cai
Accelerator Physics
Flow-based surrogate models for particle tracking
Matthias Remta, Yann Dutheil and Francesco Velotti
Particle tracking is a fundamental tool for particle-accelerator design and optimisation. Conventional tracking routines provide high accuracy but are computationally demanding, especially when simulating large particle ensembles or long time spans. As a result, optimising moderate- to high-dimensional parameter spaces is challenging, and real-time surrogate models remain out of reach for many applications. This contribution introduces a surrogate-modelling approach based on conditional flow matching (CFM). A CFM model is trained on tracking simulations of CERN's
Proton Synchrotron (PS) over a 10-dimensional parameter space. The trained model reproduces final phase-space distributions with a median squared maximum mean discrepancy MMD$^2$ of $3\times 10^{-4}$ and mean inference time of 0.04 s, a speed-up of three orders of magnitude over conventional tracking. To capture distribution-dependent dynamics that vanilla CFM cannot represent, we extend the model with cross-
attention over the initial particle ensemble (Cross-
Attention-CFM) and demonstrate that this extension recovers performance on a space-charge
benchmark in the PS, where vanilla CFM degrades. Finally, we introduce Hybrid-CFM, in which a small number of conventionally-tracked particles are used to inform the model. On the same 10-dimensional PS task, Hybrid-CFM with 100 auxiliary particles trained on 200 distributions matches the vanilla CFM trained on 1500, and improves the worst-case (90th-percentile) MMD$^2$ by roughly a factor of four, substantially reducing the upfront cost of building a surrogate.
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Fluid Dynamics · Cross-listed from cond-mat.soft
Active Reinforcement of Jammed Emulsions by Living Microswimmers
Marie Corpart, Hugo Le Roux, Daniel Bonn et al. (+1 more)
We show that living microswimmers mechanically reinforce dense emulsions. Castor-oil-in-water emulsions laden with the microalga Chlamydomonas reinhardtii are compared in three states: without algae, with immobilized algae, and with motile algae, over a broad range of oil fractions spanning the jamming transition. Oscillatory rheology reveals that motile algae systematically increase the yield stress, by up to a factor of two, whereas immobilized
cells at the same concentration leave it essentially unchanged. The reinforcement thus originates from activity rather than from the mere presence of inclusions. Single-
cell tracking shows that the droplet network confines the swimmers in pores that shrink as the oil fraction increases, and confinement is known to amplify the propulsion force of C. reinhardtii. A simple estimate based on this confinement-enhanced force accounts for the measured excess yield stress and indicates an effective, activity-induced depletion-like attraction between the passive droplets. These results identify a feedback loop: the microstructure confines the swimmers, confinement amplifies the forces they exert, and these forces stiffen the microstructure. Active emulsions thus emerge as a model platform for programming the mechanics of disordered soft solids through activity.
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All authors Marie Corpart, Hugo Le Roux, Daniel Bonn, Antoine Deblais
Instrumentation and Detectors
Reconstruction Bias in Timepix4 Subpixel Centroiding for Electron Imaging
Nina Dimova, Richard Plackett and Daniela Bortoletto
Subpixel centroiding is widely used to improve the spatial resolution of hybrid pixel detectors for electron imaging by estimating the true interaction position within an entry pixel. Existing centroiding strategies are typically optimised using localisation accuracy. However, observations show that improved localisation does not necessarily translate into improved modulation transfer function (MTF) or reliable virtual-pixel rebinning. This work proposes a unified interpretation of these observations based on the ambiguity of the centroid reconstruction problem. We show that observable-based centroid
estimators solve an intrinsically non-unique inverse problem using deterministic reconstruction rules, which can introduce entry-phase-dependent reconstruction bias that governs the spatial distribution of reconstructed subpixel coordinates. This framework explains why localisation accuracy alone is insufficient to predict imaging performance and identifies approximate intra-pixel translational
symmetry as a prerequisite for faithful virtual-pixel imaging. The proposed interpretation is evaluated using simulated 200 keV and 300 keV electron data together with measured 200 keV data acquired with a Timepix4 detector. Comparisons of charge-weighted, timing-based, and morphology-dependent centroiding strategies demonstrate that
estimators with similar localisation performance can exhibit markedly different MTFs and rebinned flat-field behaviour. The results suggest that future centroid optimisation should consider subpixel phase bias and rebinnability alongside localisation accuracy.
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Chemical Physics
Intracavity Photon Statistics from Correlated Molecular Electronic Structure
Kurt R. Brorsen
Quantum optics characterizes light through
photon correlations, but ab initio cavity
quantum electrodynamics (QED) has only calculated raw field moments at present. In this study, intracavity
photon statistics of cavity-coupled molecules are computed from QED coupled-cluster (QED-CCSD-22) densities and validated against exact diagonalization of the Pauli-Fierz
Hamiltonian. A change of coordinate origin or dipole convention displaces the ground state coherently, so second-order and higher cumulants of the cavity field are well defined even for molecular ions. The
photon subsystem of QED Hartree-Fock is exactly coherent, so the invariant statistics measure electron-
photon correlation. The field fluctuations are super-Poissonian, and their degree of second-order coherence is set by the cavity frequency rather than by the coupling strength, falling from 18.5 to 4.9 for H2 as the cavity is detuned from 5 to 20 eV. A parity selection rule makes the sign of the third cumulant a probe of the orientation of the molecular charge asymmetry along the cavity polarization.
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Optics · Cross-listed from cond-mat.mes-hall
Electric-Field-Induced Second Harmonic Generation at a Reconfigurable LaAlO$_3$/SrTiO$_3$ Nanojunction
Pubudu Wijesinghe, Melanie Dieterlen, Kyoungjun Lee et al. (+5 more)
Electrically tunable nonlinear optical responses at the nanoscale remain challenging to achieve because conventional nonlinear materials lack the combination of large susceptibility, nanoscale confinement, and in situ reconfigurability. Here we report electric-field-induced second harmonic (EFISH) generation from a nanoscale tunnel junction defined by conductive atomic force microscope lithography at the LaAlO$_3$/SrTiO$_3$ interface. A conducting channel written at the interface is interrupted by a nanoscale insulating gap, across which applied DC bias produces local electric fields exceeding $10^7$ V/m. The SHG signal is spatially localized at the junction, exhibits a quadratic bias dependence described by $I(2\omega) \propto |\chi^{(2)}_\mathrm{0} + \chi^{(3)} E_\mathrm{DC}|^2$ with no hysteresis, a modulation depth exceeding 380% at $|V_\mathrm{DC}| = 1$ V, and shows a two-lobed input-polarization pattern aligned with the junction axis, consistent with EFISH from a centrosymmetric host. Calibration against a BBO reference
crystal gives $|\chi^{(3)}| \approx 1\times10^{-19}$ m$^2$/V$^2$ at 6 K. These results establish cAFM-written
oxide nanojunctions as a reconfigurable platform for nanoscale nonlinear optics in which the junction geometry sets the
symmetry of the response and the large field-induced $\chi^{(2)}$ of SrTiO$_3$ provides the optical nonlinearity. Because the nonlinearity is both generated and read out within the same nanoscale gap, the junction operates simultaneously as a subwavelength source and a near-field detector of optical nonlinearity.
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All authors Pubudu Wijesinghe, Melanie Dieterlen, Kyoungjun Lee, Ahmed Omran, Aswini Ramankutty, Chang-Beom Eom, Patrick Irvin, Jeremy Levy
Chemical Physics
A differentiable photon-by-photon likelihood for continuous free-energy landscapes and diffusion coefficients from single-molecule FRET
Lars Dingeldein and Roberto Covino
Single-molecule FRET probes the conformational dynamics of biomolecules by measuring the distance between two dyes. The experiment produces a stream of coloured
photons, which is only an indirect readout of these dynamics. Recovering the free-energy landscape and
diffusion coefficient from such a
photon stream is a difficult inverse problem. Existing approaches assume a small number of discrete states, bin the
photons, or are computationally expensive. Here we derive an exact likelihood for the recorded
photon stream under a model in which the dye distance diffuses on a continuous free-energy landscape. It uses the raw inter-
photon times and colours at full time resolution, and analytically integrates out all hidden trajectories. The likelihood is differentiable, so automatic differentiation returns exact gradients with respect to all model parameters. This lets us jointly infer the free-energy landscape, the
diffusion coefficient, and the photophysical parameters by gradient-based optimization. On simulated data, we recover free-energy landscapes, including ones with a short-lived intermediate, together with the
diffusion coefficients. Uncertainties follow from the
curvature of the likelihood, computed directly from the same gradients. The framework also guides experimental design. Before any data are recorded, we can evaluate how much a given acquisition setting reduces the resulting uncertainty. Evaluation on the GPU is fast, and independent traces are processed in parallel, so a single fit converges in minutes and scales to large
datasets. The likelihood extends
photon-by-
photon analysis to continuous free-energy landscapes and
diffusion coefficients, putting fast quantitative inference with uncertainties within reach of the smFRET community.
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Fluid Dynamics
Zero-gravity convection in a closed duct as the realization of a thermal machine
Piero Olla
The possibility of convection in a wall-heated unstirred simple fluid in zero-
gravity conditions is discussed. It is shown that a low-Prandtl-number fluid possesses a stable circulating state in zero
gravity, without destabilization of the diffusive state, provided
geometric inhomogeneity allows the thermal cycle to perform net mechanical work. The analysis provides a quantitative explanation of why purely volumetric effects cannot induce by themselves convection in conventional fluids under zero-
gravity conditions.
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Data Analysis and Statistics · Cross-listed from astro-ph.IM
NestyNet. III. Symbolic Regression from Analytic Neural Surrogates
Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis et al. (+2 more)
Many physical laws are simple only after the right representation, decomposition or internal coordinate has been found, but discovering that structure from data is combinatorially hard. This task is symbolic regression (SR), the search for closed-form expressions that fit data without assuming a fixed model class. Here we present NestyNet-SR. A neural surrogate with analytic derivatives is used to detect separability, recursively reducing multivariate problems to simpler neural atoms. These atoms are distilled into closed form by a tiered symbolic-search stack, whose final tier is a novel factorized symbolic search that separates structure from calibration. Composing candidate internal coordinates freely, it scores each coordinate by how well calibrated functions of it (e.g.,
polynomials, power laws, sinusoids) fit the data, so the constants of those calibrated maps, however deeply nested in the final expression, are fitted rather than searched. The method supports multi-
dataset regression, automated feature discovery, and dimensional-analysis
pruning. On the SRBench AI~Feynman
benchmark, NestyNet-SR achieves exact symbolic recovery of all 120 noiseless equations, the first such result, and under noise a statistical audit certifies which structures survive. As a real-data vignette, given only the separate mass-model components of SPARC-survey
galaxies, the
algorithm discovers the baryonic acceleration coordinate, reproduces the established mass-to-light and acceleration scales and the non-unique form of the radial acceleration relation, and adds held-out-
galaxy generalization, a calibrated
symmetry abstention, and a posterior for the local slope of the law. Analytic derivatives thus provide a practical route from neural surrogates to interpretable closed-form empirical laws.
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All authors Rodrigo Ibata, Wassim Tenachi, Foivos Diakogiannis, Neil Ibata, Anirudh Shankar
Fluid Dynamics
Short spatial mooring-tilt variations from deep Mediterranean observations
Hans van Haren
Interaction between energy-abundant mesoscale eddies and internal waves can lead to convection-
turbulence generation and may prove important for deep-sea life and circulation. However, the size of scales of interacting flows is not well known. In this paper, a diagnostic tool of tilt is tested near the single top-buoyancy of 40 mooring lines 9.5 m apart horizontally and compared with 50-m scale relative vorticity and waterflow above a 2500-m deep flat Northwestern-Mediterranean seafloor. Whilst tilt relates to first order with flow-speed squared induced by mooring-line drag, considerable deviations from this relationship and larger tilt occur when the amplitude of relative vorticity attains values O(f), f the inertial frequency of planetary vorticity. The sign of relative vorticity is of no importance. Such larger-tilt events occur during most intense convection
turbulence via warm-water slanting from above. During these events, variations in tilt-angle magnitude are as large as the average tilt O(0.1)degree, thereby reducing variational scales from 50 to 9.5 m. Thus, in a deep-sea environment where flow speeds are <0.07 m s^-1, O(0.01) m s^-1 flow-speed variations provide important turbulent mixing, without deep dense-water formation.
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Geophysics
Time-Resolved Surface-Fault Displacement During the 2026 Kumamoto Earthquake From Near-Fault Video
Bogdan Enescu, Shinji Toda, Yuji Yagi et al. (+3 more)
Video recordings can reveal how rapidly fault displacement develops at the Earth's surface, but camera motion and recording artifacts can obscure the ground signal. We analyze a secondary copy of security-camera footage that captured surface displacement during the 28 July 2026 Kumamoto earthquake; the native recording was unavailable and could not be recovered. Two independent image-tracking methods were used. Optical flow follows identifiable image features, whereas normalized cross-correlation (NCC) template matching follows fixed image patches by their similarity. Both measured target-region motion relative to spatially separated reference regions while correcting motion shared by the recording. Image displacement was calibrated to the magnitude of the field-measured offset vector: 1.05 m right-lateral and 0.90 m east-side-up, or 1.383 m in total. We characterize the principal rise by the time required for displacement to progress from 20% to 80% of the selected final level. Across prespecified endpoint choices, optical flow gives 0.866-0.901 s and NCC gives 0.910-0.928 s. These durations correspond to average rates of 0.920-0.958 and 0.895-0.912 m/s, respectively. Checks using independently published tracking windows reproduce the displacement scale, although exact timing is more sensitive in spatially restricted tests. The record also shows an early apparent peak and decline followed by renewed apparent horizontal displacement. Because that later motion may represent either continued ground displacement or the geometry of the secondary recording, neither the permanent endpoint nor physical overshoot can be determined. The most robust conclusion is that the central part of the surface displacement developed in approximately 0.9 s at an average rate near 0.9 m/s.
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All authors Bogdan Enescu, Shinji Toda, Yuji Yagi, Kazuo Oike, Yu Xuan Teh, Shohei Nobuhara
Instrumentation and Detectors · Cross-listed from quant-ph
Minimization of micromotion for nanoparticles in a Paul trap
Jamie Morley, Jean Paul Louys Sansó, Dmitry Bykov et al. (+2 more)
When a charged particle in a Paul trap is displaced from the node of the AC trapping field, excess micromotion arises as an undesired effect. Excess micromotion heats the particle, limits the precision with which the particle can be localised, and acts as a decoherence channel in
quantum mechanical experiments. However, thus far there is no standard procedure for micromotion compensation with mesoscopic particles. Here, we experimentally demonstrate three different methods for minimizing the micromotion of a nanoparticle in a linear Paul trap along three axes. The most precise method allows us to nullify the stray field to within 2.9 V/m, which is comparable to reported values in trapped-ion experiments.
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All authors Jamie Morley, Jean Paul Louys Sansó, Dmitry Bykov, Simon Baier, Tracy Northup
Computational Physics · Cross-listed from cond-mat.mtrl-sci
On Transmission Function Amplitude and Phase Recovery in Multislice Electron Ptychography
Bridget R. Denzer, Colin Gilgenbach and James M. LeBeau
{Multislice electron ptychography commonly accounts for inelastic scattering by including an absorptive object potential in the reconstruction forward model that attenuates the elastic signal. However, it remains unclear how the reconstruction is quantitatively impacted by thermal scattering. Here, we use the
quantum excitation of
phonons (QEP) formalism, which explicitly separates elastic and inelastic (thermal diffuse) scattering within a single multislice propagation, to simulate four-dimensional scanning transmission electron microscopy (4D STEM)
datasets of PbTiO$_3$ and SrTiO$_3$ and reconstruct their phase and amplitude. We find that reconstructions of the QEP total and elastic-only
datasets are nearly indistinguishable across sample thicknesses from 11 to 40 nm and across atomic species, demonstrating that the reconstruction is largely insensitive to incoherent thermal diffuse scattering within the collected angular range. Reconstructions from multislice phase-only simulations further confirm that appreciable amplitude does not arise from multiple elastic scattering, but instead reflects attenuation of the coherent elastic signal due to thermal scattering. Further comparison of the QEP elastic-only reconstruction with absorptive multislice simulations reveals substantial amplitude contrast deviations for heavy Pb columns (up to 17%), arising from the approximations used to evaluate the absorptive potential for the 4D STEM simulation. These results thus indicate that reconstruction phase and amplitude accuracy are not significantly limited by the absorptive potential forward model, even in the presence of thermal diffuse scattering.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Scalable photoexcitation-induced molecular dynamics with machine-learned Hamiltonians
Leyu Cai, Yunzhe Jia, Daqiang Chen et al. (+1 more)
Ultrafast photoexcitation offers a controllable route to steer structural dynamics in solids, yet predicting how nonequilibrium electronic excitation drives
lattice motion across extended spatial and temporal scales remains a major computational challenge. Here we introduce time-dependent ab-initio propagation with electronic machine learning (TDAP-eML), a framework that explicitly incorporates electronic
evolution into scalable simulations of photoexcitation-induced
lattice dynamics. By integrating machine-learned electronic structure with atomistic propagation, TDAP-eML describes how photoexcitation reshapes the evolving energy landscapes and forces governing structural motion. Across representative examples including silicon and FeSe, the framework reproduces key photoexcited
lattice responses obtained from first-principles time-dependent density functional theory calculations and captures coherent
phonon dynamics together with their dependence on excitation conditions. Its computational advantage increases with system size, reaching nearly three orders of magnitude reduction in computational cost for the large systems examined. TDAP-eML thus establishes a scalable framework for coupled electronic and
lattice evolution, linking nonequilibrium excitation to photoinduced forces, predictive structural dynamics, and experimentally accessible observables.
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All authors Leyu Cai, Yunzhe Jia, Daqiang Chen, Sheng Meng
Optics
Artificial Anisotropy Induced Bound States in the Continuum for Integrated Photonic Waveguide
Jinzhao Wang, Kunrun Lu, Yuanlin Li et al. (+9 more)
Bound states in the continuum (BICs) enable counterintuitive light confinement without radiation loss, providing a powerful foundation for integrated photonic waveguides. However, existing BIC waveguides are predominantly realized through geometry-dependent designs, where the BIC condition is restricted to narrowly defined structural parameters, limiting design flexibility and practical applicability. Artificial optical anisotropy is introduced as a new design paradigm for BIC waveguides. Implemented using subwavelength-grating (SWG) metamaterials, continuously tailorable anisotropy provides an independent degree of freedom for deterministically reshaping the radiative continuum, enabling flexible formation and systematic control of BIC waveguides over a broad design space. Anisotropy-engineered
symmetry breaking further enables controllable asymmetric radiation and precisely tailored field leakage. This paradigm transforms BIC waveguides from geometry-constrained structures into an anisotropy-engineered platform, establishing a general framework for programmable radiation engineering and next-generation integrated photonic devices.
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All authors Jinzhao Wang, Kunrun Lu, Yuanlin Li, Weiming Yao, Yang Feng, Yidi Cao, Wei Liu, Feng He, Jianan Duan, Yi Zou, Yongkang Dong, Xiaochuan Xu
Instrumentation and Detectors
Ion Irradiation of GridPix Detector at Bonn Isochronous Cyclotron
Vladislavs Plesanovs, Hemanth Manikantan, Dennis Sauerland et al. (+6 more)
The potential successor of the IXPE — the EXPO or Enhanced X-ray Polarimetry Observatory will have a new X-ray polarimeter based on GridPix technology. To certify this detector for operation within the expected radiation environment conditions, the irradiation tests were performed at the Bonn Isochronous Cyclotron at HISKPabbreviation\footnote{ stands for Helmholtz-Institut für Strahlen- und Kernphysik or Helmholz Institute for Radiation and
Nuclear Physics}, University of Bonn. Two beam modes were used —
proton beam at 14\,MeV and 100\,MeV $^{14}\mathrm{N}^{5+}$ ions. The aforementioned GridPix detector contains the readout ASIC, Timepix, with $256\times256$ pixel grid with a pitch of \SI{55}{\micro\meter} and covered with a protection layer. The Al grid stands on the SU8 pillars above pixels forming the amplification stage. The
alignment of grid holes and silicon pixels enable primary electron detection. This paper focuses on the description and results of these testbeams. The accumulated ion rates exceed by far the expected limits without a significant deterioration in chip performance.
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All authors Vladislavs Plesanovs, Hemanth Manikantan, Dennis Sauerland, Paolo Soffitta, Carlo Lefevre, Ettore Del Monte, Jochen Kaminski, Reinhard Beck, Klaus Desch
Computational Physics
Resolving positive semi-definiteness in physics-informed kernels for scientific machine learning
J. Moser, C. Albert and S. Ranftl
Many modern machine learning models can be understood as kernel-based function-space models, including Gaussian processes and neural tangent kernels. In scientific machine learning, differential operators are increasingly used to encode physical structure directly into such models. However, it has remained unclear under which conditions these constructions are valid machine learning models, i.e. preserve positive semi-definiteness, and whether observed instabilities arise from ill-posed modeling or numerical effects. Here, we establish a simple and sufficient condition for positive semi-definiteness: for linear differential operators of order m, the base kernel must be m-times continuously differentiable. Crucially, this guarantee holds for operators with non-constant and even discontinuous coefficients. Examples are ubiquitous in physical systems, including
diffusion, material elasticity, wave propagation in inhomogeneous media, and
quantum systems. We conclude that remaining instabilities are attributable to numerical issues, providing a unifying validity foundation for operator-informed kernel methods.
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Computational Physics · Cross-listed from eess.AS
Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement
Alessia Milo, Georg Götz, Steinar Gu{\dh}jónsson et al. (+3 more)
We investigate how the realism of synthetic room impulse response (RIR)
datasets affects the training of DeepFilterNet3 for single-channel
speech enhancement. We compare a DNS4 image-source-method (ISM) RIR
dataset with a higher-acoustic-fidelity
dataset generated using hybrid wave-based and geometrical acoustics simulation. Rather than isolating individual simulation factors, we compare complete RIR generation pipelines while keeping the enhancement model unchanged. Models are evaluated on unseen measured RIRs using objective
speech enhancement metrics and downstream automatic
speech recognition (ASR). Training with the higher-fidelity
dataset consistently yields modest improvements in objective metrics and substantially lower ASR word error rates than the ISM
dataset. Although the experiments do not attribute these gains to individual modelling components, they show that increasing the overall realism of synthetic acoustic training data improves the generalization of DeepFilterNet3 to unseen measured environments.
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All authors Alessia Milo, Georg Götz, Steinar Gu{\dh}jónsson, Daniel Gert Nielsen, Jesper Pedersen, Finnur Pind
Optics
Direction-Selective Wave Freezing and Amplification at a Hyperbolic Time Interface
Zhao Wang, Ai Gang, Xinghong Zhu et al. (+3 more)
Hyperbolic media are well known for converting high k-components that are evanescent in conventional dielectrics into propagating bulk waves through their open equifrequency contours. Here, we reveal a complementary temporal effect: after a sudden transition into an effectively nondispersive
hyperbolic state, conservation of the full wavevector causes the indefinite dispersion to partition momentum space into real-, zero-, and purely imaginary-frequency regimes. Consequently, p-polarized waves undergo conventional temporal scattering, critical
magnetic-field freezing, or exponential growth and decay, depending solely on their conserved wavevector direction, whereas s-polarized waves remain in the real-frequency regime. A second temporal boundary releases the frozen or amplified fields into propagating waves at original frequency. Analytical temporal boundary theory, k-space pulse reconstruction, and finite-difference time-domain simulations corroborate these dynamics. These results establish
hyperbolic temporal boundaries as a compact route to direction-selective imaginary frequency dynamics and wave amplification without Floquet periodicity.
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All authors Zhao Wang, Ai Gang, Xinghong Zhu, Hongru Ma, Wen Xiao, Huanyang Chen