Optics
Bridging Relativistic Twisted Fermion Beams and Photonic OAM Flux in Gauged Hopf Lattices: Emergent Topological Analogs
Aaron Michael Kinder
Recent work has demonstrated non-diffractive topological
spin textures (Skyrmion- and meron-like) that persist in the core of diffracting relativistic twisted
fermion beams [1]. Here we present numerical simulations of an analogous photonic system: Laguerre-Gaussian (LG) twisted
photon packets coupled to a gauged
Hopf lattice with discrete flux flywheels. Our results reveal emergent behavior of the same qualitative class, including persistent core features under propagation, mean survival approx 0.150 at critical lambdat = 2 clustering tightly with the mystery scale e^{-2} approx 0.1353 and residual R approx 0.1375, golden-angle correlations in the OAM mode ladder, and topological residuals with long-lived structure. Multi-l simulations further exhibit z-resolved flux transfer and twist dynamics that play a role analogous to probability-current continuity in the fermionic setting. These findings suggest photonic platforms as accessible analogs for exploring topological textures in structured waves, and they formalize a concrete computational bridge between relativistic twisted matter waves and topological photonics.
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Biological Physics · Cross-listed from q-bio.NC
Emergent topological structure in spontaneous brain-organoid activity
Eve Bodnia, Margaux Basart, Sofie Hai et al. (+5 more)
Neural activity is widely held to organize on low-dimensional structure embedded in a high-dimensional state space. Persistent homology reads such structure directly from the pattern of pairwise correlations, without assuming in advance which variables are relevant. We apply persistent homology to microelectrode-array (MEA) recordings of spontaneous activity from human (Lancaster) and mouse (Paşca) cortical organoids, spanning $26$–$234$ simultaneously sorted units, and ask whether topological data analysis resolves structure at the node counts that neural recordings actually deliver. Building weighted networks in correlation space and characterizing them by Vietoris–Rips filtration, we find that the first homology ($H_1$, loops) rises significantly above a rate- and population-preserving null in $14$ of $18$
datasets. This loop structure occupies a non-redundant core: it is robust to random removal of units yet disrupted by targeted removal of the units that carry it. Topological richness grows with network size, and second homology ($H_2$) emerges significantly above the null only in the larger networks. These results show that persistent homology resolves structured
topology in neural recordings at the scale experiments actually deliver.
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All authors Eve Bodnia, Margaux Basart, Sofie Hai, Lenzie Ford, Nina Miolane, Kenneth S. Kosik, Dirk Bouwmeester, Lincoln D. Carr
Plasma Physics
Assessment of 0-D L-H Power Threshold Scaling and Regression Stability in DIII-D with Applied 3D Magnetic Fields
Michael O Hanson, George R Tynan and Dmitri M Orlov
A database of 192 L-H transitions in DIII-D is used to assess the effects of applied three-dimensional (3D)
magnetic fields on the H-mode power threshold and the stability of zero-dimensional (0-D) empirical regressions. The
dataset includes nominally axisymmetric discharges and discharges with resonant or non-resonant
magnetic perturbations. Filtering criteria reduce uncertainties associated with absorbed power, neutral-beam modulation, and fast-ion losses, while applied-field components are quantified using
equilibrium reconstruction and spectral analysis. Measured threshold powers show substantial scatter and systematic deviations from the 2008 ITPA multi-machine scaling, including for discharges without applied perturbations. TRANSP modeling indicates that empirical estimates can substantially underpredict fast-ion losses, particularly at low
plasma current and with non-axisymmetric fields. Adding global 3D-field metrics does not robustly isolate the effects of applied perturbations. A fully unconstrained regression retains a 70% residual root-mean-square error and produces nonphysical parameter dependencies, including a
plasma surface-area exponent of 2.79. Extrapolations to ITER-relevant conditions consequently have broad confidence intervals. These results show that hidden-variable dependencies can strongly affect empirical threshold parameterizations even in a restricted single-machine
dataset. Machine-specific conditions, local edge physics, and power-accounting uncertainties limit the predictive capability of purely 0-D scalings. Improved predictions for ITER and future devices will require better fast-ion-loss treatment and physics-based, edge-localized quantities.
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Medical Physics
Theoretical derivation of blood velocity from TOF-MRA based artery centerline
Abrar Faiyaz
Time-of-flight
magnetic resonance angiography (TOF-MRA) is widely used for nonin-vasive visualization of arterial anatomy, but extracting hemodynamics like blood velocity typically requires supplementary phase-contrast scans, tagging or multi-TE images. This study proposes a novel, physics-informed computational framework to extract variable fluid velocity directly from standard TOF-MRA signal profiles. We analytically expand the approach-to-steady-state Bloch equations to include convective flow, establishing a mathematical relationship between the spatial decay of longitudinal magnetization and fluid velocity. The velocity derivation was further extended to pointwise estimation over a 1-D centerline, overcoming the limitations of constant-velocity assumptions. To validate and solve this problem, a MATLAB (R2025b) simulation framework was developed to model fluid flow in two variable-geometry flowing tube cases, i.e., continuous narrowing and focal stenosis, under synthetic scanner noise. A global inverse optimization approach utilizing Dual-Tikhonov regularization was applied to stably invert the ill-posed transit time integral, actively penalizing high-frequency numerical ringing while preserving structural curves. The computational simulations successfully recovered ground-truth point-wise velocities, tracking gradual hemodynamic accelerations and sharp stenotic jets. This theoretical framework and the example centerline TOF-MRA signal intensity pro-vide a robust mathematical proof-of-concept that quantitative, localized functional he-modynamic metrics can be extracted from standard structural MRA imaging, establishing a foundation for advanced flow quantification without requiring additional scan time.
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Applied Physics
Study of GeSn Selective Area Growth with Demonstration of SWIR Light Detection
Hryhorii Stanchu, Quang Minh Thai, Rajesh Kumar et al. (+16 more)
As germanium-tin (GeSn) epitaxial growth quality continuously improves, the search for an efficient integration strategy of GeSn optoelectronics devices into complementary metal-
oxide-
semiconductor (CMOS) manufacturing line also accelerates. Selective area growth (SAG) on patterned substrate emerges as a promising approach for this quest, with locally controlled growth of GeSn
laser/detector suitable for either co-integration with silicon-based waveguide structure or stand-alone module like focal plane array. In this work, we report successful GeSn SAG with Sn content ranging from 3.2% to 8.7% of good optical quality, with demonstration of tunable GeSn SAG photoluminescence and GeSn SAG photoconductor device, the latter with detection cutoff wavelength up to 2 um. In addition, we present a comprehensive study of GeSn SAG condition at different window sizes, from 2 um to 100 um, and shapes: circle, square, octagon, and rectangle. Presence of loading effect is revealed, where GeSn growth rate increases as pattern fill factor and window size shrink. It introduces a different growth condition compared to thin film growth, which can weaken or inhibit Sn incorporation at very small window size and induce Sn segregation in high Sn content SAG growth.
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All authors Hryhorii Stanchu, Quang Minh Thai, Rajesh Kumar, Fernando M. de Oliveira, Kushal Dahal, Xuehuan Ma, Sudip Acharya, Justin Rudie, Alexander Golden, Joshua M Grant, Matthew Cook, Stephen Margiotta, Xiaoxin Wang, Jifeng Liu, Perry C. Grant, Baohua Li, Wei Du, Gregory Salamo, Shui-Qing Yu
Medical Physics
Continuous 3-D Latent Diffusion for Medical Image Generation and Reconstruction
Youness Mellak, Antoine De Paepe, Dimitris Visvikis et al. (+1 more)
High-resolution three-dimensional (3-D) medical latent
diffusion remains limited by the cost of decoding full volumes, which existing latent autoencoders (AEs) handle through overlapping sub-volume decoding. We propose a continuous 3-D latent
diffusion model framework for computed
tomography (CT) and
magnetic resonance imaging (MRI) generation and measurement-guided reconstruction, built on a coordinate-rendering autoencoder (CoRe-AE). Its decoder applies its convolutional stage once on the compact latent grid and renders voxel intensities from spatial coordinates through a lightweight implicit head, evaluated in
memory-bounded chunks. This design avoids overlapping sub-volumes and remains differentiable with respect to the latent, so that the decoder can be used inside measurement-guided reconstruction. The framework was evaluated on CT volumes of 512\textsuperscript{3} voxels and MRI volumes of 256\textsuperscript{3} voxels. At 512\textsuperscript{3}, the decoder was 4–18 times faster than the MAISI and 3D MedDiffusion AEs and required the lowest peak graphics processing unit (GPU)
memory, including when its gradient was computed. CoRe-AE also achieved the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) among the evaluated AEs on both modalities. The resulting frozen 3-D latent prior generated full volumes without patch seams and, without retraining, reconstructed sparse-view CT and accelerated MRI through hard data consistency, within 2.4–4.9 dB PSNR of the pixel-domain decomposed
diffusion sampler (DDS). A single volumetric latent prior thus supports both generation and reconstruction of high-resolution medical volumes on one GPU. Our code will be made available at https://github.com/Mellak/continuous-3d-latent-
diffusion/.
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All authors Youness Mellak, Antoine De Paepe, Dimitris Visvikis, Alexandre Bousse
Fluid Dynamics
On the collapse of three point vortices on surfaces
Theodore D. Drivas, Boris A. Khanikati and Valeriya A. Khanikati
Point
vortices represent an important reduced model describing two-dimensional ideal fluid dynamics. It is well known that there exist three-
vortex configurations on the Euclidean plane $\mathbb{R}^2$ that exhibit finite-time singularities, i.e., collapse to a single point. Moreover, in $\mathbb{R}^2$, such collapses occur only self-similarly. Here, we investigate the extent to which this phenomenon persists on curved surfaces. We show that self-similar collapse is a universal feature of surfaces of nonnegative constant
curvature, namely the plane and the sphere. In contrast, on the
hyperbolic plane, it is shown that self-similar collapsing solutions do not exist with respect to any distance variable defined by an analytic function of the geodesic distance. Finally, we establish the existence of nearly self-similar collapse of three
vortices on arbitrary smooth surfaces embedded in $\mathbb{R}^3$.
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Optics
Measuring the dipole phase of Bloch-trajectory harmonics using a monolithic interferometer
Mohanad Awad, Julian Späthe, Paramjit C. Yadav et al. (+10 more)
Uncovering the dipole phase of gas-phase high harmonic generation was instrumental to understanding the recollision physics underlying attosecond pulse generation. Corresponding measurements in the condensed phase have not yet yielded a consistent picture. Here, we present a compact and inherently stable approach to high-harmonic interferometry in thin-film solids. We employ it to reveal the dipole phase of high-harmonic generation in polycrystalline ZnO, driven by broadband mid-IR
laser pulses. We demonstrate that, under the conditions of our experiments, recollisions facilitated by Bloch oscillations represent the dominant contribution to high-harmonic generation just above the bandgap.
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All authors Mohanad Awad, Julian Späthe, Paramjit C. Yadav, Mandakini Bhat, Zsolt Divéki, Eric Cormier, Bálint Kiss, Eszter Baradács, János Tomán, Zoltán Erdélyi, Thomas Siefke, Adrian N. Pfeiffer, Matthias Kübel
Chemical Physics
Stochastic Resetting: A Non-Equilibrium Framework for Prediction, Inference and Design
Tommer D. Keidar, Sagi Meir, Nir Sherf et al. (+4 more)
Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling
stochastic dynamics far from
equilibrium. Its defining features, i.e., the creation of non-
equilibrium steady states and the acceleration of first-passage kinetics, are increasingly relevant across physical chemistry, from biological restart mechanisms to molecular simulations and colloidal experiments. We review the renewal theory underlying
stochastic resetting and show how it enables prediction of reset dynamics from properties of the underlying process, while also allowing the latter to be inferred from the resetting-accelerated dynamics. We then discuss applications to state preparation, enhanced sampling, kinetic inference, and training and sampling of machine learning models. Finally, we review recent advances in adaptive resetting, environmental feedback, many-body dynamics, and thermodynamic costs of resetting. These developments establish new opportunities for controlling
stochastic dynamics with resetting across theory, simulations, and experiments.
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All authors Tommer D. Keidar, Sagi Meir, Nir Sherf, Rémi Goerlich, Shlomi Reuveni, Yael Roichman, Barak Hirshberg
Fluid Dynamics · Cross-listed from math.AP
On the impact of clusters of rigid balls on the motion of a viscous fluid
Marco Bravin, Eduard Feireisl, Arnab Roy et al. (+1 more)
We develop a new approach to the problem of the motion of a large number of rigid bodies immersed in a viscous fluid. The leading idea is the concept of cluster - a collection of individual rigid objects that may be grouped or even connected in such a way that their collective impact on the bulk motion of the system is similar to that of a single body. The applications of the new approach include: 1. Improving the critical value of the number of balls of small radius such that their cloud has no impact on the limit system represented by the incompressible Navier–Stokes equations. 2. The balls follow the fluid flow in the asymptotic limit of vanishing radius and increasing number even if a gravitational force is imposed.
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All authors Marco Bravin, Eduard Feireisl, Arnab Roy, Arghir Zarnescu
Chemical Physics · Cross-listed from quant-ph
Host-guest Crystal Engineering Tailors the Room Temperature Spin Dynamics in Molecular Quantum Devices
Ziqiu Huang, Angus Cowley-Semple, Irena Nevjestic et al. (+5 more)
Molecular materials that enable coherent control over an electron's
spin state at room temperature are promising candidates for
quantum technologies, including
quantum sensors and ultra-low noise microwave amplifiers, known as masers. Host-guest molecular
crystals enable independent control of
spin-active guests and their local environments to enhance molecular
spin properties and so improve device performance. Using electron paramagnetic resonance and optically-detected
magnetic resonance, we demonstrate the ability to tune triplet population, depopulation, and
spin-
lattice relaxation by modulating host-dependent
lattice rigidity and vibrational coupling to significantly reduce the operating requirements for building useful masers. Importantly, the most rigid host, picene, reveals the ability to slow
spin-
lattice relaxation without lengthening triplet lifetime, though at the cost of strain-induced line width broadening and reduced triplet
spin polarisation. We also find that deuteration reduces the triplet resonance line width and vibrationally-mediated triplet depopulation. Consequently, we find that perdeuterated pentacene in perdeuterated p-terphenyl is the most viable candidate for building a continuous wave maser. This work demonstrates host-guest engineering as an important and practical method for tuning the
spin-dependent performance of room-temperature molecular
quantum technologies.
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All authors Ziqiu Huang, Angus Cowley-Semple, Irena Nevjestic, Yifan Yu, Mark Oxborrow, Sam L. Bayliss, Sarah K. Mann, Max Attwood
Medical Physics
Sensitivity of silicon-to-water dose conversion and Bragg-peak metrics to stopping-power datasets in proton dosimetry
F. Matias, J. M. B. Shorto, P. de Vera et al. (+5 more)
Accurate
proton dosimetry requires consistent stopping-power data for detector-to-water conversion and Monte Carlo radiation transport. We quantify the sensitivity of water-to-silicon stopping-power ratios, converted dose-to-water distributions, and Bragg-peak metrics to the stopping-power
dataset for a 67.5 MeV pristine
proton beam in water. PHITS simulations used SRIM-2013, PSTAR/NIST, ATIMA, and TDDFT-Penn stopping powers up to 10 MeV, together with a common SBETHE-based extension at higher energies. The water-to-silicon stopping-power ratio showed relative entrance-to-distal variations of 17.40%$-$22.10%. Conversion of normalized PTW silicon-diode percentage-depth-ionization data yielded relative percentage-depth-dose curves with a common maximum at 36.86 mm, consistent with the experimental Bragg-peak depth of (36.81 $\pm$ 0.15) mm. The converted curves were approximately 3% below the reference throughout the entrance and plateau regions, while deviations of up to 2.1% occurred near the Bragg peak. Direct PHITS calculations predicted Bragg-peak depths of 36.575$-$36.675 mm; only the TDDFT-Penn result lay within the quoted experimental uncertainty. These results show that stopping-power treatment affects the magnitude of the converted dose more strongly than the Bragg-peak position does, and that it should be harmonized or explicitly included in uncertainty budgets for
proton-beam calibration, commissioning, quality assurance, and Monte Carlo validation.
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All authors F. Matias, J. M. B. Shorto, P. de Vera, R. Garcia-Molina, I. Abril, T. F. Silva, J. Pereira, H. Yoriyaz
Optics
Two-color solitons in Kerr third harmonic generation model
Alexey Sukhinin, Curtis Menyuk, Natalia Litchinitser et al. (+2 more)
We investigate two-color, two dimensional spatially localized light modes in a resonant Kerr third-harmonic generation model. Using computational tools, we identify two distinct families of localized states. Unlike the single-component 2D nonlinear Schrödinger equation and previously studied non-resonant two-color systems, the dynamics are not dictated by a universal critical power, but depend on the distribution of power between the harmonics. The "fundamental-dominated" family acts as a "dynamical separatrix" between simultaneous collapse and joint diffraction, whereas the "third-harmonic-dominated" family does not. We further identify resonant collapse events accompanied by strong oscillations of the third harmonic, revealing a collapse mechanism absent from standard Kerr self-focusing.
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All authors Alexey Sukhinin, Curtis Menyuk, Natalia Litchinitser, Jean-Claude Diels, Alejandro B Aceves
Optics · Cross-listed from quant-ph
Remote Infrared Absorption Spectroscopy with Undetected Photons
Simon Neves, Fériel Armbruster and Jean-Pierre Wolf
We demonstrate a novel method for remote open-path
Fourier-transform infrared spectroscopy with undetected
photons. Similar to previous
quantum spectroscopy works, a mid-infrared spectrum is reconstructed by detecting a near-infrared radiation only, thus bypassing important limitations of infrared detectors. Our study however relies on the co-propagation of the
photon-pair and the pump-
laser over the same optical path, which allows the probing of the open atmosphere over long distances. By sending the
photons over unprecedented distances of up-to 43.4m in the outdoor atmosphere, we were able to detect butane released in the open-path, as well as natural atmospheric methane, thus demonstrating the first use of infrared spectroscopy with undetected
photons for atmospheric measurements.
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Chemical Physics · Cross-listed from quant-ph
Quantum-Centric Geometry Optimization with Wave-Function-Based Embedding
Danil Kaliakin, Akhil Shajan, Fangchun Liang et al. (+2 more)
The EWF-(FCI,SQD) method, a wave-function-based
embedding approach combining full configuration interaction (FCI) and sample-based
quantum diagonalization (SQD), is a promising new tool for the simulation of molecular systems. However, applications of EWF-(FCI,SQD) have so far been limited to single-point calculations, whereas the study of complex chemical processes requires the ability to explore potential energy surfaces. In this work, we demonstrate geometry optimization with EWF-(FCI,SQD), scaling our simulations to molecules as large as menthone and benzidine within the STO-3G basis set. Without fragmentation, these systems comprise 73 and 82 molecular orbitals respectively, presenting an intractable
Hilbert space for conventional exact or high-level subspace solvers and establishing a clear necessity for fragmentation-based methodologies. The underlying fragment SQD simulations in the EWF-(FCI,SQD) geometry optimizations use up to 70
qubits. The resulting geometries show exceptional accuracy relative to the classical reference, with deviations below 4 picometers.
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All authors Danil Kaliakin, Akhil Shajan, Fangchun Liang, Zhen Li, Kenneth M. Merz Jr
Fluid Dynamics
From Triadic Interactions to Kolmogorov Scaling: A Deterministic, Scale-Resolved Formulation of Energy Flux
Erik Bertram
We develop a deterministic, scale-resolved formulation of energy transfer in the three-dimensional incompressible Navier-Stokes equations based on an explicit triadic decomposition of the nonlinear term in
Fourier space. Using a systematic dyadic localization of the velocity field, we derive an exact representation of the nonlinear energy flux across scales and organize it in terms of interactions between well-defined scale components. Under suitable smoothness assumptions, we obtain an absolutely convergent triadic expansion and quantitative bounds that distinguish local and nonlocal contributions in scale space. This framework provides a transparent and fully explicit description of how energy transfer is mediated by triadic interactions and how scale locality emerges as a structural property of the nonlinearity. Building on this formulation, we revisit the classical inertial-range picture of
turbulence from a deterministic perspective. We show that, under a scale-invariant flux assumption, the Kolmogorov $-5/3$ scaling is formally consistent with the triadic energy-transfer mechanism at a structural level. The result does not rely on statistical assumptions, but instead follows from the structural properties of the Navier-Stokes equations combined with a scale-resolved representation of the energy flux. The present work thus provides a coherent synthesis of triadic interaction analysis, dyadic scale decomposition, and classical
turbulence phenomenology, offering a deterministic framework that clarifies how Kolmogorov-type scaling constraints arise in the scale-resolved structure of the underlying equations.
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History and Philosophy of Physics
Realism about the external world: an adversarial collaboration
Kelvin J. McQueen and Markus P. Mueller
One of us is a realist and believes that reality fundamentally consists in an external physical world, governed by laws of physics. Observers are either emergent (physicalism) or exist in addition to physical reality. The other defends a version of idealism and believes that reality fundamentally consists in first-person states, unembedded into worlds, on which laws of nature act directly. Shared "physical worlds" are merely emergent. The disagreement ultimately centers on the overall coherence of a formal model, known as Algorithmic Idealism, and its ability to resolve observer paradoxes, such as the Boltzmann
brain paradox. Our aim is to confront this issue in the form of an
adversarial collaboration, where we avoid misunderstanding each other, so that readers can see the true source of the disagreement, and decide for themselves. Our work represents an attempt to establish a form of exchange that may help overcome the fragmentation of scholarly communities in philosophy, physics, and elsewhere. The collaboration was conducted using a discipline-neutral template for theoretical
adversarial collaboration developed in a companion paper [1], which we hope will be useful in other theoretical disciplines.
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History and Philosophy of Physics
Theoretical adversarial collaboration: a template
Kelvin J. McQueen and Markus P. Mueller
Adversarial collaborations have become increasingly prominent in empirical science, where proponents of competing theories jointly design experiments capable of discriminating between them. Much less
attention has been given to theoretical disputes that cannot readily be resolved by experiment. We distinguish theoretical
adversarial collaboration from its empirical counterpart and propose a five-step template for conducting it. The procedure is designed to move disputants beyond familiar critique-reply-rejoinder exchanges by requiring sustained clarification, supervised steel-manning of the target theory, and the collaborative development and refinement of objections. Its condition for success is that the collaboration produce insights that substantially exceed the existing literature: either novel, non-obvious arguments show that the theory can meet the objections, or the theory's advocate concedes that new conceptual resources or revisions are required. The template was developed through reflection on, and implemented in, our own
adversarial collaboration in philosophy and foundations of physics. We offer it here as a general framework for constructive and publishable progress in philosophy, foundational science, and other areas of inquiry in which disputes cannot readily be resolved by experiment.
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Atomic Physics
Simulating neural network criticality and resource dynamics with Rydberg gases
Patrick Mischke, Herwig Ott, Michael Fleischhauer et al. (+1 more)
Efficient operation of neural networks has been linked to criticality in their underlying non-
equilibrium excitation dynamics. However, obtaining experimental evidence of this
conjecture remains challenging due to limited control and undersampling in biological systems. Here, we experimentally explore neural network criticality using an ultracold Rydberg gas as a highly controllable simulator. We highlight the similarity of the excitation spreading via Rydberg facilitation and the synaptic connection of spiking activity of
neurons, giving rise to distinct absorbing and active phases. We systematically explore and resolve criticality criteria, including power-law scaling of excitation avalanches and the emergence of universal avalanche shape collapse. Crucially, we implement a controlled gain mechanism to compensate for atom loss, mimicking metabolic resource replenishment and stabilizing the system in a controlled non-
equilibrium steady state. We find peak temporal correlations at the critical point and
stochastic oscillations with dragon king avalanches in the active phase, consistent with predictions for systems orbiting criticality. Our work establishes facilitated Rydberg gases as a platform for investigating criticality, resource dynamics, and emergent oscillations in neural networks.
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All authors Patrick Mischke, Herwig Ott, Michael Fleischhauer, Thomas Niederprüm
Fluid Dynamics
Effects of anisotropic confinement on droplet rebound from superhydrophobic surfaces
M. Feinberg, S. A. Hosseini and I. Karlin
On flat superhydrophobic surfaces, droplet rebound is well described by a single inertio-capillary time scale, yielding a contact-time that is independent of impact energy. This single-mode response reflects the radial
symmetry of flat-plate impacts. We demonstrate that an anisotropic
geometric constraint, imposing a fixed spreading length along one axis, breaks this degeneracy and splits the rebound into a reciprocal pair of inertio-capillary modes. The fixed length also couples the contact-time to the Weber-dependent maximum spread, introducing an impact-energy dependence absent on the flat plate. We realize this constraint with grooved substrates, simulated using a non-ideal, entropic, multiple-relaxation-time
lattice Boltzmann method and validated against the experiments of Chantelot et al. Extending their blob model from a single transverse scale to the reciprocal pair, we organize both modes through a
geometric blob number and relate their time scales to the Weber number and groove width. We show that on non-wetting grooves the reciprocal modes are recovered directly, and explore the effects of finite wall affinity, using competition between the two modes to explain an observed two-branch structure in the contact-time response on mildly wetting, superhydrophobic grooves. Predictions tied to global energy balance reproduce cleanly across all conditions, while those tied to the details of the droplet's spread morphology are approximate but directionally correct. These results show that anisotropic confinement turns contact-time reduction from a question of accelerating a single rebound mode into one of selecting between conjugate inertio-capillary modes.
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Applied Physics · Cross-listed from cs.LG
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Owen Lockwood, Jérémy Béjanin, Joost Bus et al. (+4 more)
To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack that leverages
stochastic analog processes in physical hardware. In this work, we focus on energy-based thermodynamic computing where the
stochastic process is well described by Langevin dynamics with tunable energy potentials. The implementation of such potentials in physical hardware enables us to generate and sample from basic parameterized energy-based models. We demonstrate how to construct and train popular classes of machine learning models based on these hardware-native energy-based models, using the framework of probabilistic graphical models. We analyze the runtime and energy consumption of different models in this thermodynamic paradigm based on theoretical considerations and numerical studies. As a preliminary experimental realization of such hardware, we present our
stochastic analog superconducting
circuits driven by thermal noise. Together, these results outline a path toward energy-efficient thermodynamic hardware for probabilistic machine learning.
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All authors Owen Lockwood, Jérémy Béjanin, Joost Bus, Christopher Chamberland, Patrick Huembeli, Frank Schäfer, Guillaume Verdon
Optics
Quantum scanning synthetic optical holography
Josué R. León-Torres, Byron Caiza, Nadia Baumann et al. (+7 more)
Synthetic optical holography (SOH) introduced
holographic reconstruction into scanning optical microscopy, enabling quantitative phase imaging with sequential acquisition and point detection. Here, we extend this concept to the
quantum regime by implementing SOH within
quantum imaging with undetected light (QIUL). By integrating a controlled synthetic phase carrier into a scanning QIUL implementation, we retrieve amplitude and phase images of objects probed by mid-infrared (MIR)
photons while detecting only their visible partners. We demonstrate the method on binary, transparent, and biological samples, showing complex-field reconstruction in a scanning
quantum imaging system. This approach decouples spatial resolution from
photon-pair spatial correlations and establishes a route toward diffraction-limited, label-free MIR phase imaging with visible-wavelength detection.
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All authors Josué R. León-Torres, Byron Caiza, Nadia Baumann, Sebastian Töpfer, Anna Mühlig, Orlando Guntinas-Lichius, Karin Burger, Frank Setzpfandt, Markus Gräfe, Valerio Flavio Gili
Geophysics
Broadband Multi-Aperture Passive Scholte-Wave Imaging Using Seabed Distributed Acoustic Sensing
Anna Titova and Andrey Bakulin
Shallow-water ocean forcing provides strong broadband passive Scholte-wave illumination along a seabed distributed acoustic sensing cable on the Texas Gulf Coast. We exploit this illumination through direct processing of the uncorrelated wavefield, long-duration frequency-wavenumber stacking, and a multi-aperture strategy that preserves high-frequency spatial localization while resolving low-frequency modes. Constant-wavenumber slices enable spatially consistent multimode tracking across successive array centers. The resulting dispersion spans approximately 0.3-4.5 Hz and supports 400 one-dimensional inversions along a 51-km cable segment. These profiles form a pseudo-2D shear-wave velocity model extending from the near seafloor to approximately 2 km depth. Broad shallow low-velocity intervals are consistent with softer incised-valley fill within stiffer Pleistocene deposits. The results demonstrate that existing seabed fiber infrastructure, when coupled with strong shallow-water passive illumination, can deliver broadband regional shear-wave imaging, while higher-frequency active sources remain necessary to resolve the uppermost several meters.
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Instrumentation and Detectors
Radiopurity material assays and radiation exposure projections for superconducting qubit measurements at SNOLAB
Y. Ahmed, B. Binoy, R. Bunker et al. (+21 more)
Interactions of
cosmic rays and other forms of ionizing radiation pose a significant challenge to the reliable operation of state-of-the-art
quantum devices and error correction in
quantum computing based on superconducting
circuits which are typically fabricated on
semiconductor substrates. Shielded by 2 km of rock overburden, the Cryogenic Underground TEst facility (CUTE) at SNOLAB provides a unique ultra-low radiation environment to probe the performance of
quantum technologies with a particular interest in
quantum coherence studies. In this article, we present the findings of an extensive material assaying program in preparation for the first underground operation of superconducting
qubits at SNOLAB. The radioactivity levels identified by the material assays enter a thorough Monte Carlo study based on the Geant4 particle physics tracking code. From these simulations, we estimate the rates of energy deposits from radiogenic sources expected for a
quantum-device assembly operated in the CUTE facility. We further characterize the spectral components of the projected background and identify the dominant particle interaction types. Finally, we outline how
crystal dynamics simulations using the G4CMP solid-state physics extension for Geant4 can inform the community-wide efforts to identify effective strategies to mitigate the effects of high-energy particle impacts.
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All authors Y. Ahmed, B. Binoy, R. Bunker, D. Chauhan, P. Delsing, R. Germond, J. Hall, Z. Hong, A. Iqbal, V. Iyer, A. Klepikova, A. Kubik, S. P. Mantry, A. C. Masuskapoe, C. C. Monk, G. Peng, P. Qin, W. Rau, T. Reynolds, M. Stukel, C. M. Wilson, B. Zatschler, S. Zatschler, A. Zuniga
Fluid Dynamics
Data-driven identification of multiscale self-similarity and asymptotic matching
Kaixin Zhu, Nikos Bempedelis and Konstantinos Steiros
A central problem in fluid mechanics is the identification of self-similarity, which reveals the underlying scaling behaviour of a flow. Recently, a data-driven framework proposed by Bempedelis et al. (2025 J. Fluid Mech., vol. 1020, A11) has enabled the extraction of single-scale self-similarity from data, without prior knowledge of the governing equations. However, many physical systems are inherently multiscale and therefore require more general approaches. Building on this foundation, we develop an
algorithm for the systematic identification of multiscale self-similarity. The
algorithm is first applied to two canonical fluid-mechanical problems: turbulent channel flow and late-stage homogeneous decaying
turbulence characterised by classical dissipation laws. In both cases, the
algorithm successfully identifies inner and outer self-similarity from numerical data and recovers the corresponding similarity expressions. In the intermediate region of both problems, the
algorithm identifies similarity expressions that are independent of both the inner and outer scales, providing a novel data-driven route to recovering the corresponding scaling laws: the logarithmic law in turbulent channel flow and the -5/3 law in late-stage homogeneous decaying
turbulence. Moreover, the
algorithm uncovers corrections to both laws: one linked to the power-law scaling proposed by Barenblatt (1993 J. Fluid Mech., vol. 248, 513–520), and the other yielding an improved approximation of the energy spectrum. The
algorithm is then applied to early-stage homogeneous decaying
turbulence, characterised by the nonclassical dissipation law. It identifies the same inner similarity as in the late stage, but different outer similarity expressions.
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Chemical Physics
Nonadiabatic excited-state dynamics with quantum Monte Carlo-trained machine learning: azomethane as a stringent test
Alfonso Annarelli, Emiel Slootman and Claudia Filippi
We introduce
quantum Monte Carlo (QMC)-trained multi-state machine-learned (ML) force fields for nonadiabatic excited-state dynamics, targeting photochemical processes in which the electronic character changes along the reaction path and a consistent correlated description is required. In this framework, variational Monte Carlo wave functions combine compact selected configuration-interaction expansions with a Jastrow factor that explicitly accounts for dynamical correlation, while neural networks convert the
stochastic QMC data into smooth potential energy surfaces for large surface-hopping ensembles. We apply this approach to azomethane, a demanding test case involving torsional relaxation through conical-intersection regions and C–N bond dissociation on the hot ground state.
Benchmark calculations support the accuracy of the QMC reference data and show robust force convergence across isomerization and dissociation geometries. The QMC-trained dynamics preserves the expected photoisomerization mechanism, strongly reduces the excessive C–N breaking obtained with complete active space self-consistent field, and predicts a small but non-negligible
prompt dissociation component after internal conversion, with a timescale consistent with femtosecond-resolved mass-spectrometry experiments. These results establish QMC-ML as a practical route to nonadiabatic photochemical dynamics with accurate wave-function reference data.
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Computational Physics
CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery
Jorge Bravo-Abad
Autonomous discovery systems couple a resource that answers queries (a simulator, instrument, or analytic model) to an
algorithm that selects what to query next. Most software frameworks for this loop inherit the control structure of numerical optimization: campaigns run for a fixed number of iterations, query costs are absent from the programming interface, and decision-making is treated as free. In practice, queries may differ in cost by orders of magnitude, and planners built on large
language models or expensive surrogates consume resources of their own. Here we present CADAQUES, an open-source Python framework built on one architectural principle: cost is a first-class primitive of the discovery loop. CADAQUES separates the loop into two structural protocols, an Oracle that answers queries and a Driver that proposes them, and charges both evaluations and decisions against a common vector-valued budget spanning wall time, CPU hours, monetary cost, and
language model
tokens. An append-only ledger records, for each transaction, the cost declared before execution and the cost settled afterwards, making their discrepancy an observable property of the campaign. We evaluate the architecture by locating the critical temperature of the two-dimensional Ising model from noisy finite-size estimates against the exact thermodynamic-limit reference. In this noisy setting, strategies that concentrate around the best observed result can be misled by noise-induced peaks, whereas a schedule that explores with cheap low-fidelity queries and refines with higher-fidelity ones yields lower and less variable errors than high fidelity throughout, at the studied budget scale. Metering adds tens of microseconds per iteration, three orders of magnitude below the cheapest oracle query. The framework is MIT-licensed and archived at Zenodo (doi:10.5281/zenodo.21293589).
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Atmospheric and Oceanic Physics
Global kilometer-scale climate simulations: new opportunities for climate services
David Saint-Martin, Olivier Geoffroy and Gildas Dayon
Climate services are essential for risk management and economic planning. This paper presents a century-long simulation performed with the ARP-GEM global atmosphere model at 2.6 km horizontal resolution. This simulation was specifically designed to complement and extend the set of simulations used in the latest version of the French
climate services. A key feature is its global coverage at kilometer-scale resolution, enabling the representation of phenomena that depend on such fine scales. This is especially relevant for French
climate services, as many processes - particularly over islands and resolution-sensitive regions - are currently overlooked in global simulations. Additionally, France's overseas territories are globally dispersed, and this approach allows their representation within a single framework. A limitation of this simulation is the use of prescribed sea surface temperatures due to the lack of ocean coupling; this will be addressed in future work. The present study demonstrates the feasibility of this approach and highlights the benefits of this new generation of
climate modeling for
climate services.
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Plasma Physics
Non-invasive monitoring of process-relevant plasma parameters by Fiber PROES in capacitively coupled Ar/CF$_4$ plasmas
Florian Beckfeld, Maximilian Ryppa, Constantin Neuroth et al. (+3 more)
Monitoring changes of process-relevant
plasma parameters, such as the electron density and ion flux to the wafer, is essential for the development and control of
plasma processes. However, invasive
plasma diagnostics, such as probe measurements, typically cannot be applied to commercial reactors. At the example of a low pressure capacitive
radio frequency discharge operated in different mixtures of CF$_4$ and Ar, we demonstrate that changes of such
plasma parameters can be monitored non-invasively by phase resolved optical emission spectroscopy via optical fibers (Fiber PROES), for which the ports are usually available at industrial
plasma sources. In this way, the spatio-temporally resolved dynamics of energetic electrons are tracked by observing a selected emission line. By measuring the electron density and ion flux directly via probe and retarding field energy analyzer diagnostics as a function of driving voltage and pressure, changes of these
plasma parameters, including hysteresis effects, are found to be correlated with transitions of the electron power absorption mode revealed by Fiber PROES. Such mode transitions cause the electron energy distribution function (EEDF) to change and, thus, affect such
plasma parameters. Based on these findings, Fiber PROES can be used as a non-invasive diagnostic for the monitoring and knowledge-based development of
plasma processes.
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All authors Florian Beckfeld, Maximilian Ryppa, Constantin Neuroth, Li Wang, Ihor Korolov, Julian Schulze
Chemical Physics · Cross-listed from quant-ph
Operator Entanglement in Quantum Dynamics Simulations: Formalism and Analysis Tools
Tzu Yu Wang, Michael Schuurman and Simon Neville
We review the framework of operator
Hilbert space and introduce the one- and two-particle super reduced density
matrices (1-SRDMs and 2-SRDMs), as well as the super mutual information (SMI). The eigenvectors of the 1-SRDMs define what we term natural single particle operator bases, and provide a way to compress vibrational and vibronic
Hamiltonians with controlled error. The SMI is defined from the operator
entanglement entropy of the 1-SRDMs and 2-SRDMs, and captures the correlation between operators acting on different one-mode subspaces, which may be used to reveal and quantify both direct and indirect couplings that might otherwise be difficult to extract. Efficient numerical approaches for the calculation of SRDMs and the SMI are developed and applied to a set of prototypical vibrational and vibronic
Hamiltonians, as well as approximations to the corresponding time-
evolution operators. Through this, we demonstrate that: (i) commonly used vibronic
Hamiltonians are amenable to extremely high levels of compression without compromising accuracy, and; (ii) SMI analysis can be used to systematically and quantitatively reveal both direct and indirect couplings that might otherwise be difficult to extract, including indirect couplings of vibrational modes via intermediary electronic-vibrational interactions.
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