Chemical Physics · Cross-listed from cond-mat.soft
Linear odd electrophoresis of a sphere in a charged chiral active fluid
Reinier van Buel, Bogdan Cichocki and Jeffrey C. Everts
The electrophoresis of charged colloidal particles in fluids exhibiting odd viscosity represents a fundamental challenge in understanding transport phenomena within charge-stabilised
chiral active suspensions. Here, we consider a charged
chiral active fluid, where electrokinetics is coupled to odd Stokes flow, to explore how classical results from electrophoresis in Newtonian fluids are modified in the presence of odd viscosity. In particular, we derive a general expression for the electrophoretic mobility for particles of any shape, under weak external electric fields, using the Lorentz reciprocal
theorem for odd fluids. By applying this result to a charged sphere at low
zeta potentials, we obtain an exact, closed-form analytical expression for the electrophoretic mobility, valid for arbitrary values of the Debye screening length and the odd-viscosity coefficient. Similar to Newtonian fluids, we find that the electrophoretic mobility is proportional to the translational mobility of an uncharged sphere, modulated by the Henry function. However, unlike in Newtonian fluids, odd viscosity leads to directional asymmetries in the electrophoretic mobility tensor that persist even for thin electric double layers. This case contrasts significantly with a charged anisotropic particle suspended in an isotropic Newtonian fluid, where anisotropic effects would vanish under the same electrostatic-screening conditions.
Read the full abstract ▾
Optics
Annular beams for reliable intersatellite optical communications
Mario Badás Aldecocea, Edward Pauwels, Jasper Bouwmeester et al. (+2 more)
Free-space optical communications (FSOC) are a key enabling technology for future high-capacity space-based networks. Particularly, the backbone of global communication relies on intersatellite optical links. In a previous study, the authors proposed a method to mitigate the impact of transmitter pointing jitter by using a superposition of orthogonally polarized Gaussian and higher-order Laguerre-Gaussian (LG) beams. In this study, we experimentally characterize the proposed system using a spiral phase plate (SPP) to generate higher-order annular beams. We demonstrate that such superpositions can be reliably generated in a realistic optical setup, quantify the associated beam-shaping errors and losses, and assess their impact on intersatellite optical communication performance. It is found that the proposed beam-shaping approach can still yield power savings on the order of 20% compared to a conventional Gaussian beam under the considered conditions.
Read the full abstract ▾
All authors Mario Badás Aldecocea, Edward Pauwels, Jasper Bouwmeester, Pierre Piron, Jérôme Loicq
Fluid Dynamics
Efficient fluid extraction through hydraulic fracture in capillary fiber bundle model
Anjali Vajigi and Subhadeep Roy
We have simulated a one dimensional capillary fiber bundle model with fracking events while acted between a pressure gradient across the system. The hydraulic fractures are incorporated through a decreasing nature of capillary thresholds for each tube that replicates an increment in pore spaces due to fracking. An increment in flow rate is evident through the evolved rheology we observe in our study. Analytical approaches for certain limits are adopted to understand the rheology which matches well with the numerical results. The overall hydraulic power increases with pressure gradient as well as with the percentage decrease in capillary threshold due to a single event, defines as the fracking amplitude. This combined with the early onset of linear Darcy flow increases the quality of the fluid extraction. We successfully point towards an optimum pressure gradient at which the fracking events are most effective - maximum change in fluid extracting with a maximum rate. We observed that it is possible to extract the information regarding the change from non-linear to Darcy flow due to fracking as well as the optimum pressure for fluid extraction through local flow profile, something which in much superior from the point of view of computational cost. The former is done by correlating the maximum fluctuation in local flow profile to the onset of Darcy flow. The later is done through the relative change in Shannon
entropy with respect to the fracking amplitude that points towards the pressure associated with the maximum fluid extraction criterion.
Read the full abstract ▾
Optics
Free-space quasi-phase matched second harmonic generation in crystalline quartz
Nazar Kovalenko, Ankit Pai and Oleg Pronin
We report experimental results on second-harmonic generation in a z-cut quartz
crystal under conditions of free-space quasi-phase matching in a multi-pass
cell. In a 62-pass configuration, an efficiency of 0.027% or 1.4x10-4 %/MW/cm2 was achieved, delivering 1 uJ of the second harmonic at 3.7 mJ pump pulse. This corresponds to an enhancement factor of more than 1000 in conversion efficiency as compared to a single pass. The generated second-harmonic beam demonstrates high beam quality M2=1.1 and linear polarization. The scaling of the output power with the number of passes is in good agreement with the calculated values. Further increasing the pump intensity, number of passes, and amount of plates opens the way to scaling the conversion efficiency to values on the order of tens of percent.
Read the full abstract ▾
Atomic Physics · Cross-listed from quant-ph
Operational criteria for quantum advantage in latency-constrained nonlocal games
Changhao Li, Seigo Kikura, Akihisa Goban et al. (+2 more)
Remote
entanglement enables coordinated decision making without communication and produces correlations beyond those achievable by any classical strategy, representing a practical
quantum advantage in time-critical distributed decision-making problems. However, existing analyses of
quantum-classical gaps in such latency-constrained tacit coordination (LCTC) have focused on idealized models that neglect the finite stationary window of the LCTC, finite operation times, and limited
entanglement generation rates, leaving fundamental constraints unaccounted for. In this work, we develop a comprehensive framework to quantitatively analyze
quantum advantage in LCTC that explicitly incorporates finite-duration and finite-rate operations, as well as generalized utility structures with a limited stationary window. These advances are made possible by adapting statistical certification methods for nonlocal games to the decision-making scenarios of LCTC, identifying operational criteria that must be satisfied by the hardware implementations to realize
quantum advantage with sufficient statistical significance. To meet the stringent criteria, we propose time-multiplexed, event-ready operations of cavity-assisted trapped-atom
quantum network nodes that provide a continuous stream of entangled
qubit pairs, with decision latencies of a microsecond and decision rates of $8\times 10^3~\text{s}^{-1}$ per channel for a representative metropolitan-scale $50$-km fiber network to keep up with the fast-changing environment, such as financial
markets and electric grid networks. These results bridge the gap between the theoretical notions of the
quantum-classical gap in nonlocal games and concrete implementations that meet the stringent operational criteria for achieving robust
quantum advantage in realistic coordination tasks.
Read the full abstract ▾
All authors Changhao Li, Seigo Kikura, Akihisa Goban, Hayata Yamasaki, Shinichi Sunami
Computational Physics · Cross-listed from cs.LG
Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences
Yuhao Zhang, Ti John, Matthias Stosiek et al. (+1 more)
Optimizing expensive black-box objectives over mixed search spaces is a common challenge across the natural sciences.
Bayesian optimization (BO) offers sample-efficient strategies through probabilistic surrogate models and acquisition functions. However, its effectiveness diminishes in mixed or high-cardinality discrete spaces, where gradients are unavailable and optimizing the acquisition function becomes computationally demanding. In this work, we generalize the probabilistic reparameterization (PR) approach of Daulton et al. to handle non-equidistant discrete variables, enabling gradient-based optimization in fully mixed-variable settings with Gaussian process (GP) surrogates. With real-world scientific optimization tasks in mind, we conduct systematic
benchmarks on synthetic and experimental objectives to obtain an optimized kernel formulations and demonstrate the robustness of our generalized PR method. We additionally show that, when combined with a modified BO workflow, our approach can efficiently optimize highly discontinuous and discretized objective landscapes. This work establishes a practical BO framework for addressing fully mixed optimization problems in the natural sciences, and is particularly well suited to autonomous laboratory settings where noise, discretization, and limited data are inherent.
Read the full abstract ▾
All authors Yuhao Zhang, Ti John, Matthias Stosiek, Patrick Rinke
Data Analysis and Statistics · Cross-listed from cs.LG
Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers
Jonas M. Schmid, Johannes D. Schmid, Martin Eser et al. (+1 more)
Accurate knowledge of acoustic surface admittance or impedance is essential for reliable wave-based simulations, yet its in situ estimation remains challenging due to noise, model inaccuracies, and restrictive assumptions of conventional methods. This work presents a physics-informed neural operator approach for estimating frequency-dependent surface admittance directly from near-field measurements of sound pressure and particle velocity. A deep operator network is employed to learn the mapping from measurement data, spatial coordinates, and frequency to acoustic field quantities, while simultaneously inferring a globally consistent surface admittance spectrum without requiring an explicit forward model. The governing acoustic relations, including the Helmholtz equation, the linearized momentum equation, and Robin boundary conditions, are embedded into the training process as physics-based regularization, enabling physically consistent and noise-robust predictions while avoiding frequency-wise inversion. The method is validated using synthetically generated data from a simulation model for two planar porous absorbers under semi free-field conditions across a broad frequency range. Results demonstrate accurate reconstruction of both real and imaginary admittance components and reliable prediction of acoustic field quantities. Parameter studies confirm improved robustness to noise and sparse sampling compared to purely data-driven approaches, highlighting the potential of physics-informed neural operators for in situ acoustic material characterization.
Read the full abstract ▾
All authors Jonas M. Schmid, Johannes D. Schmid, Martin Eser, Steffen Marburg
Instrumentation and Detectors
Long-term stability study of single-mask triple GEM detector: impact of continuous irradiation
S. Mandal, S. Gope, S. Das et al. (+1 more)
A study has been carried out to evaluate the performance stability of Gas Electron Multiplier (GEM) chamber prototypes in the laboratory using $^{55}$Fe radiation source with Argon and CO$_2$ gas mixture. This research focuses on the characterisation of the GEM detector's gain, efficiency (count rate with radioactive source), and energy resolution under varying operational conditions. A patch on the detector has been subjected to continuous and absolutely uninterrupted radiation for about 98 days. The gain and energy resolution of the detector are measured along with the ambient parameters temperature (t), pressure (p) and relative humidity (RH). In addition to that, the long-term behaviour of the count rate with a strong radioactive source are also studied. This work is very relevant for Micro Pattern Gaseous Detectors (MPGD) such as GEM before installing on large experiment. The experimental setup, methodology, and results are presented in this article.
Read the full abstract ▾
All authors S. Mandal, S. Gope, S. Das, S. Biswas
Applied Physics · Cross-listed from cond-mat.mes-hall
Quasicrystal Architected Nanomechanical Resonators via Data-Driven Design
Kawen Li, Hangjin Cho, Richard Norte et al. (+1 more)
From butterfly wings to remnants of
nuclear detonation, aperiodic order repeatedly emerges in nature, often exhibiting reduced sensitivity to boundaries and
symmetry constraints. Inspired by this principle, a paradigm shift is introduced in nanomechanical resonator design from periodic to aperiodic structures, focusing on a special class: quasicrystals (QCs). Although soft clamping enabled by phononic stopbands has become a central strategy for achieving high-$Q_m$ nanomechanical resonators, its practical realization has been largely confined to periodic phononic
crystals, where band structure engineering is well established. The potential of aperiodic architectures, however, has remained largely unexplored, owing to their intrinsic complexity and the lack of systematic approaches to identifying and exploiting stopband behavior. Here we demonstrate that soft clamping can be realized in quasicrystal architectures and that high-$Q_m$ nanomechanical resonators can be systematically predicted through a data-driven design framework. As a representative demonstration, the 12-fold QC-based resonator obtained numerically a quality factor $Q_m \sim 10^7$ and an effective mass of sub-nanograms at MHz frequencies, corresponding to an exceptional force sensitivity of $26.4$~aN/$\sqrt{\text{Hz}}$ compared to previous 2D phononic
crystals. These results establish QCs as a robust platform for next-generation nanomechanical resonators and open a new design regime beyond periodic order.
Read the full abstract ▾
All authors Kawen Li, Hangjin Cho, Richard Norte, Dongil Shin
Physics and Society
Temporal Structure Mediates the Robustness and Collapse of Plant-Pollinator Networks
Tom Clegg and Thilo Gross
Mutualistic networks provide a powerful way to describe and analyse plant-pollinator communities and their structure over time. While these networks capture the complex interdependencies that link population fates across the season, they can be hard to untangle, preventing us from understanding the emergence of community-scale properties and responses to perturbation. Here, we address this problem by developing a structural model of a plant-pollinator community that explicitly incorporates seasonal turnover and the temporal nature of species interactions. We analyse our model using percolation methods from network science to derive simple analytical solutions linking network structure to emergent community diversity. Our findings reveal that temporal structure organises community diversity into distinct ecological phases, creating the potential for alternative high- and low-diversity states and bistable regimes. We demonstrate how this temporal structure mediates the nature of transitions between these states, determining whether systems undergo gradual shifts or abrupt, catastrophic collapses. Crucially, we show how this temporal structure reduces the robustness of plant-pollinator systems, creating bottlenecks that inhibit species persistence and increase susceptibility to secondary extinctions. Our results demonstrate that the temporal dynamics of plant-pollinator networks are central to mediating their fragility, highlighting the importance of accounting for time when considering community resilience.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from astro-ph.CO
The Non-Gaussian Weak-Lensing Likelihood: A Multivariate Copula Construction and Impact on Cosmological Constraints
Veronika Oehl and Tilman Tröster
We present a framework to compute non-Gaussian likelihoods for two-point correlation functions. The non-Gaussianity is most pronounced on large scales that will be well-measured by stage-IV weak-
lensing surveys. We show how such a multivariate likelihood can be constructed and efficiently evaluated using a copula approach by incorporating exact one-dimensional marginals and a dependence structure derived from the exact multivariate likelihood. The copula likelihood is found to be in better agreement with simulated sampling distributions of correlation functions than Gaussian likelihoods, particularly on large scales. We furthermore investigate the effect of the non-Gaussian copula likelihood on posterior inference, including sampling the full parameter space of contemporary weak-
lensing analyses. We find potential parameter shifts in $S_8$ on the order of one standard deviation for $1 \ 000 \ \mathrm{deg}^2$ surveys but negligible shifts for areas of $10 \ 000 \ \mathrm{deg}^2$, suggesting Gaussian likelihoods are sufficient for stage-IV surveys, though results depend on the detailed mask geometry and data-vector structure.
Read the full abstract ▾
Chemical Physics
Explicit Electric Potential-Embedded Machine Learning Framework: A Unified Description from Atomic to Electronic Scales
Jingwen Zhou, Yawen Yu, Xuwei Liu et al. (+1 more)
To further develop accurate and large-scale simulations of electrochemical interfaces, we propose a unified explicit electric potential framework to simultaneously predict atomic forces and electron density distributions. The framework consists of three components: data generation, model training, and application. The data generation component, implemented in Hy-DFT, efficiently regulates the potential during constant-potential ab initio molecular dynamics (CP-AIMD), reducing the number of single-point calculations required for convergence. The model training component includes two modules: Potential-Embedded MACE (PE-MACE) and Potential-Embedded Electron Density Prediction (PE-EDP). PE-MACE implements an explicit electric potential machine learning force field (EEP-MLFF) based on the MACE architecture. We develop PE-EDP to overcome the limitation of EEP-MLFF in describing atom forces. PE-EDP, also based on equivariant graph neural networks, predicts electron density distributions under arbitrary potentials. Using the Pt(111)/water interface as a model system, both PE-MACE and PE-EDP show high accuracy on training and test sets. Radial distribution functions from CP-MLMD agree well with CP-AIMD, and long-timescale simulations reveal potential-induced reorganization of interfacial water. Planar-integrated charge profiles and Bader analysis from PE-EDP are consistent with DFT results. These results demonstrate that the framework can simultaneously describe atomic dynamics and electron density distributions under arbitrary potentials, providing a useful tool for studying electrochemical interfaces.
Read the full abstract ▾
All authors Jingwen Zhou, Yawen Yu, Xuwei Liu, Chungen Liu
Instrumentation and Detectors
Behavioral-Level Simulation of Digital Readout for COFFEE at LHCb Upstream Pixel Tracker
Xiaoxu Zhang, Yang Zhou, Xiaomin Wei et al. (+9 more)
COFFEE series is a HVCMOS pixel sensor using the advanced 55 nm process, currently being developed for the Upstream Pixel (UP) tracker of the LHCb Upgrade II. To ensure that COFFEE will be able to handle the particle hit rates at UP tracker, which reach a maximum of 322.5 MHz/chip, detailed simulation of the digital readout circuitry was performed. Simulation results show that the column-drain readout mechanism achieves nearly 100% efficiency when the single readout cycle does not exceed 100 ns. Meanwhile, the buffer depth and
memory resources required for the peripheral readout adapted to the BXID-sharing data format are also evaluated. These provide guidance for the design of COFFEE. The column-drain readout mechanism was used in COFFEE3 (fabricated in 2025), while the peripheral readout architecture adapted to the BXID-sharing data format is implemented in CHiR (taped out in early 2026).
Read the full abstract ▾
All authors Xiaoxu Zhang, Yang Zhou, Xiaomin Wei, Anqi Wang, Leyi Li, Yu Zhao, Zexuan Zhao, Huimin Wu, Mingjie Feng, Lei Zhang, Jianchun Wang, Yiming Li
Fluid Dynamics · Cross-listed from math.NA
Slip optimization on arbitrary 3D microswimmers: a reduced-dimension and boundary-integral framework
Marc Bonnet, Kausik Das, Shravan Veerapaneni et al. (+1 more)
This article presents a computational framework for determining the optimal slip velocity of a microswimmer with arbitrary three-dimensional geometry suspended in a viscous fluid. The objective is to minimize the hydrodynamic power dissipation required to maintain unit speed along the net swimming direction. By exploiting the linearity of the Stokes equations and the Lorentz reciprocal
theorem, we derive an explicit linear operator that maps the tangential surface slip velocity to the resulting rigid-body translational and rotational velocities, effectively decoupling the hydrodynamic boundary value problem from the optimization loop. The a priori infinite-dimensional search space for the slip optimization is reduced to the finite dimension $r$ of rigid-body motions by finding an appropriate subspace of the operator's domain. This reduces the PDE-constrained optimization to a low-dimensional programming problem that can be solved at negligible computational cost once the system
matrices are assembled. The optimization
algorithm requires 2$r$ auxiliary flow problems that are solved numerically using a high-order boundary integral method. We validate the accuracy of the proposed method and present optimal slip profiles and swimming trajectories for a variety of microswimmer shapes. We investigate the effect of some common geometrical
symmetries of the swimmer shape on the resulting optimal motion, and in particular present a modified version of the slip optimization
algorithm for axisymmetric shapes, where tangential rigid-body velocities may occur
Read the full abstract ▾
All authors Marc Bonnet, Kausik Das, Shravan Veerapaneni, Hai Zhu
Plasma Physics
Phase-Selective Excitation of Betatron Oscillations by Nonadiabatic Magnetic-Field Switching
R.S. Anandu and B. Ramakrishna
Nonadiabatic removal of an external transverse
magnetic field provides a phase-selective mechanism for controlling betatron oscillations in
laser wakefield accelerators. When the field is switched off on a timescale shorter than the betatron period, the
equilibrium orbit shifts abruptly and acts as an impulsive transverse drive. The induced motion interferes coherently with the preexisting betatron oscillation, leading to phase-dependent enhancement or suppression of the oscillation amplitude. A theoretical model shows that the excitation is governed by the dimensionless switching parameter $\chi=\omega_\beta L_s/c$, which distinguishes nonadiabatic and adiabatic regimes. Particle-in-
cell simulations confirm the predicted scaling and demonstrate controllable modulation of the betatron radiation spectrum while leaving longitudinal acceleration largely unaffected. These results establish
magnetic-field switching as a direct mechanism for phase control of relativistic betatron oscillations in
plasma-based accelerators.
Read the full abstract ▾
Instrumentation and Detectors
Multispectral representation of Distributed Acoustic Sensing data: a framework for physically interpretable feature extraction and visualization
Sergio Morell-Monzó, Dídac Diego-Tortosa, Isabel Pérez-Arjona et al. (+1 more)
Distributed Acoustic Sensing (DAS) enables continuous monitoring of dynamic strain along tens of kilometers of optical fiber, generating massive
datasets whose interpretation and automated analysis remain challenging. DAS measurements often lack a standardized visual representation, and their physical interpretation depends strongly on acquisition conditions and signal processing choices. This work introduces a systematic framework for visualization and feature extraction of DAS data based on a multispectral signal representation. The approach decomposes strain-rate measurements into predefined frequency bands and computes band-limited energy images that describe the spatial and temporal distribution of acoustic energy across distinct spectral regimes. The framework is evaluated using DAS recordings containing Fin Whale (Balaenoptera physalus) and Blue Whale (Balaenoptera musculus) vocalizations. Three experiments are conducted to assess the approach: enhanced visualization of bioacoustic signals, unsupervised clustering of acoustic patterns, and supervised event detection using a convolutional neural network. Using multispectral composites as input, a ResNet-18 classifier achieves an accuracy of 97.3% in whale vocalization detection, demonstrating that the proposed representation captures biologically meaningful spectral structure and provides an effective feature space for automated analysis of DAS data.
Read the full abstract ▾
All authors Sergio Morell-Monzó, Dídac Diego-Tortosa, Isabel Pérez-Arjona, Víctor Espinosa
Atomic Physics
Two-dimensional shelving spectroscopy of ultraviolet ground state transitions in dysprosium
Kevin S. H. Ng, Paul Uerlings, Fiona Hellstern et al. (+5 more)
The open inner-shell electronic structure of lanthanides with large
magnetic moments gives rise to a rich spectrum of transitions available for
laser cooling, trapping, and coherent control. Despite this, the large number of ultraviolet (UV) transitions below 400nm have so far been rarely utilized in dipolar atom experiments. Here, we investigate multiple UV ground state transitions in dysprosium. Several of these UV excited states have the largest decay strengths to the ultralong-lived, low-lying first excited state which are comparable to the most commonly used strongest transitions found in dipolar atoms. Using two-dimensional shelving spectroscopy which improves detection sensitivity and provides a straightforward way to determine the hyperfine-isotope structure and excited state total angular momentum $J$, we measure isotope shifts, hyperfine coefficients, and create King plots to determine their electronic nature. Such knowledge of these UV transitions which analogously exist in other
magnetic atoms is important for optically populating the first excited state and can be used towards creating an optical clock, high resolution imaging in
quantum gas microscopy, and probing lanthanide
nuclei with enhanced Schiff moments in search of physics beyond the standard model.
Read the full abstract ▾
All authors Kevin S. H. Ng, Paul Uerlings, Fiona Hellstern, Jens Hertkorn, Luis Weiß, Stephan Welte, Tilman Pfau, Ralf Klemt
Optics
TurPy: a physics-based and differentiable optical turbulence simulator for algorithmic development and system optimization
Joseph L. Greene, Alfred Moore, Iris Ochoa et al. (+3 more)
Developing optical systems for free-space applications requires simulation tools that accurately capture
turbulence-induced wavefront distortions and support gradient-based optimization. Here we introduce TurPy, a GPU-accelerated, fully differentiable wave optics
turbulence simulator to bridge high fidelity simulation with end-to-end optical system design. TurPy incorporates subharmonic phase screen generation, autoregressive temporal
evolution, and an automated screen placement routine balancing
Fourier aliasing constraints and weak-
turbulence approximations into a unified, user-ready framework. Because TurPy's phase screen generation is parameterized through a media-specific power spectral density, the framework extends to atmospheric, oceanic, and biological propagation environments with minimal modification. We validate TurPy against established atmospheric
turbulence theory by matching 2nd order Gaussian beam broadening and 4th order plane wave scintillation to closed-form models with 98% accuracy across weak to strong
turbulence regimes, requiring only the medium's refractive index structure constant and power spectral density as inputs. To demonstrate TurPy as a gradient-based training platform, we optimize a dual-domain diffractive deep neural network (D2NN) in a two-mask dual-domain architecture to recover a Gaussian beam from a weakly turbulent path and achieving over 58% reduction in scintillation relative to an uncompensated receiver in simulation. TurPy is released as an open-source package to support synthetic data generation,
turbulence-informed
algorithm development, and the end-to-end design of optical platforms operating in turbulent environments.
Read the full abstract ▾
All authors Joseph L. Greene, Alfred Moore, Iris Ochoa, Emily Kwan, Patrick Marano, Christopher R. Valenta
Physics and Society
Emergence of cooperation in nonlinear higher-order public goods games
Jaume Llabrés, Onkar Sadekar, Federico Malizia et al. (+1 more)
Evolutionary game theory has provided substantial contributions to explain the emergence of cooperation under unfavorable conditions in ecology, economics, and the social sciences. Recently, inspired by newly available empirical evidence on group interactions, higher-order networks have emerged as a natural framework to encode multiplayer games in structured populations. Here, we study the emergence of cooperation in nonlinear public goods games on hypergraphs, where collective reinforcement captures the synergistic or discounting effect associated with each additional cooperator. In well-mixed populations, when all games have the same number of players, the system displays a transition whose nature changes from continuous to discontinuous depending on the form of nonlinearity. By contrast, when games involving different numbers of players coexist, an additional bistability region between an active coexistence state and full cooperation may emerge. We further find that scale-free hypergraphs promote cooperation, highlighting the crucial role played by both the initial placement of cooperators and the presence of hyperdegree correlations. Overall, our results provide a comprehensive characterization of nonlinear public goods games on hypergraphs and open avenues for richer models of evolutionary dynamics of multiplayer games on structured populations.
Read the full abstract ▾
All authors Jaume Llabrés, Onkar Sadekar, Federico Malizia, Federico Battiston
Fluid Dynamics
Viscous Bending Mitigates the Spontaneous Meandering of Rivulets in Hele-Shaw Cells
Grégoire Le Lay and Adrian Daerr
We investigate the spontaneous meandering of slender rivulets in Hele-Shaw
cells and identify the physical mechanism that selects the most unstable wavenumber, a quantity that has remained elusive even since the identification of the instability threshold [Daerr et al., Phys. Rev. Lett. 106, 184501 (2011)]. Earlier criteria did not distinguish between wavelengths and thus predicted an undiscriminated amplification of arbitrarily short perturbations. By incorporating viscous bending into the depth-averaged Navier-Stokes equations, we show that this effect is responsible for the selection of a fastest-growing mode, answering a question that has remained open for 15 years. We answer the open question of whether the meandering instability is absolute or convective. Our analysis also provides a simpler alternative derivation of the instability criterion, based on a low-viscosity assumption, and finally it yields a new physical interpretation of the mechanism: the destabilization arises directly from friction effects, instead of being caused by inertial forces. Together, these results complete the linear-stability picture of rivulet meandering in confined geometries, and establish viscous bending as a key parameter governing wavelength selection. They lay the groundwork for future exploration of the nonlinear features of the spontaneous meandering instability.
Read the full abstract ▾
Atomic Physics
Defect-free arrays at the thousand-atom scale in a 4-K cryogenic environment
Desiree Lim, Hadriel Mamann, Grégoire Pichard et al. (+18 more)
We report on a cryogenic platform at 4 K incorporating high numerical aperture optics for the generation of large-scale tweezers arrays, and compatible with Rydberg-state manipulation. We achieve trapping lifetimes of around 5000 s, significantly extending the available experimental time for the preparation of large-scale arrays. By combining two trapping
lasers at different wavelengths and by minimizing other atom losses during the rearrangement and imaging processes, we demonstrate the preparation of defect-free arrays with up to 1024 atoms. Our cryogenic design opens exciting prospects for analog and digital
quantum computing.
Read the full abstract ▾
All authors Desiree Lim, Hadriel Mamann, Grégoire Pichard, Lilian Bourachot, Arvid Lindberg, Clotilde Hamot, Hugo Le Bars, Florian Fasola, Siddhy Tan, Gwennolé Cournez, Sylvain Dutartre, Thierry Cartry, Sylvain Lemettre, Richard Hostein, Julien Paris, Franck Ferreyrol, Andréa Collardey, Adrien Signoles, Thierry Lahaye, Corentin Monmeyran, Bruno Ximenez
Applied Physics
Dual-Tuned 31P-1H Dual-Row Loop/Dipole 32-element Transceiver Array for Human Brain Spectroscopy at 9.4T
G. A. Solomakha, R. Pohmann, F. Glang et al. (+8 more)
Purpose The goal of this work is to develop and evaluate a single-layer tight-fit 32-element double-tuned loop/dipole transceiver (TxRx) array for human
brain 31P MRS at 9.4T, achieving reasonable transmit and receive performance and full-
brain coverage at both frequencies. Methods First, we developed numerical models of dual-row TxRx arrays for 31P (loop array) and 1H (coaxial-end folded-end dipole array) frequencies at 9.4T. Next, a multi-tissue voxel model was used to simulate Tx-performance of the arrays and define optimal CP-mode excitation. Following this, the proposed array performance was evaluated by MR measurements both on a phantom and a healthy volunteer. Finally, we compared the proposed array to a previously reported dual-tuned single-row loop-based TxRx array. Results The developed 32-element double-tuned array demonstrated full-
brain (including the cerebellum and
brain stem) imaging capabilities, reasonable SNR and transmit performance at both frequencies at 9.4T. Conclusion As a proof of concept, we developed a 32-element double-tuned UHF tight-fit TxRx human head array coil for 31P MRS with sufficient 1H performance using a combination of loop and dipole array elements. The proposed array design could also be adapted to higher fields, i.e., 10.5T, 11.7T, and 14T.
Read the full abstract ▾
All authors G. A. Solomakha, R. Pohmann, F. Glang, S. Mueller, P. I. Valsala, T. Platt, S. Orzada, M. E. Ladd, A. Korzovski, K. Scheffler, N. I. Avdievich
Optics
Symmetry-Engineered Magnetic Dipole Emission in Plasmonic Core-Satellite Resonators
Joshua Davis, Sébastien Bidault, Mathieu Mivelle et al. (+2 more)
Magnetic dipole (MD) transitions are intrinsically weak and highly sensitive to emitter orientation and position, making their controlled enhancement at optical frequencies particularly challenging. Here we show that structural
symmetry provides a powerful route to robust
magnetic light-matter interactions. We systematically investigate plasmonic core-satellite resonators composed of N metallic nanoparticles arranged on a dielectric core. We evaluate their performance using a unified figure of merit that accounts for
magnetic Purcell enhancement, electric dipole suppression,
quantum efficiency, and robustness to emitter orientation and fabrication tolerances. We find that the optimal structures correspond to the highest-
symmetry geometries, which naturally produce spatially homogeneous and orientation-independent
magnetic Purcell enhancement. In particular, the dodecapod configuration yields strong
magnetic emission with Purcell factors approaching 250, high radiative efficiency, and suppressed electric dipole contributions. Quasinormal-mode and complex mode-volume analysis reveal that
symmetry enforces uniform
magnetic modal confinement within the core, explaining both the enhancement and its robustness. These results establish
symmetry as a guiding principle for designing nanophotonic resonators with controlled multipolar light-matter interactions and provide a practical route toward bright and selective
magnetic dipole emitters.
Read the full abstract ▾
All authors Joshua Davis, Sébastien Bidault, Mathieu Mivelle, Mona Tréguer-Delapierre, Alexandre Baron
Atmospheric and Oceanic Physics
Exploring coupled tropical Pacific variability within a Multi-branch $\beta$-Variational Autoencoder
Emily F. Wisinski, Maria J. Molina, Kyle J. C. Hall et al. (+4 more)
This study explores what is encoded in the latent space of a multi-branch $\beta$-variational autoencoder ($\beta$-VAE) trained on coupled tropical Pacific
climate fields. We assess the reconstruction skill and physical interpretability of the latent space trained on monthly sea surface temperature, ocean heat content, and outgoing longwave radiation across the tropical Pacific from a 500-year preindustrial control simulation. The model generalizes well, with only modest degradation from training to test performance, and preserves the dominant basin-scale structure of all three fields. Latent-space diagnostics show that variability is organized unevenly across dimensions: sea surface temperature is concentrated in a smaller subset of latent dimensions, whereas ocean heat content and outgoing longwave radiation are more broadly distributed across multiple dimensions. Comparisons with conventional tropical Pacific diagnostics further show that several latent dimensions align with known El Niño and La Niña variability, while others capture related coupled ocean-atmosphere variability on decadal or longer timescales. Sensitivity experiments and latent traversals identify dimensions associated with eastern-Pacific-like, central-Pacific-like, coastal, subsurface-dominant, and atmosphere-dominant variability. Together, these results show that the multi-branch $\beta$-variational autoencoder yields a skillful and physically informative reduced representation of coupled tropical Pacific variability.
Read the full abstract ▾
All authors Emily F. Wisinski, Maria J. Molina, Kyle J. C. Hall, Hannah Bao, Salil Mahajan, Nan Rosenbloom, John Fasullo
Fluid Dynamics
A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
Elsa Cardoso-Bihlo and Alex Bihlo
Numerical simulation of wave propagation and run-up is a cornerstone of coastal engineering and tsunami hazard assessment. However, applying these forward models to inverse problems, such as bathymetry estimation, source inversion, and structural optimization, remains notoriously difficult due to the
rigidity and high computational cost of deriving discrete adjoints. In this paper, we introduce AegirJAX, a fully differentiable hydrodynamic solver based on the depth-integrated, non-hydrostatic shallow-water equations. By implementing the solver entirely within a reverse-mode automatic differentiation framework, AegirJAX treats the time-marching physics loop as a continuous computational graph. We demonstrate the framework's versatility across a suite of scientific machine learning tasks: (1) discovering regime-specific neural corrections for model misspecifications in highly dispersive wave propagation; (2) performing continuous
topology optimization for breakwater design; (3) training recurrent neural networks in-the-loop for active wave cancellation; and (4) inverting hidden bathymetry and submarine landslide kinematics directly from downstream sensor data. The proposed differentiable paradigm fundamentally blurs the line between forward simulation and inverse optimization, offering a unified, end-to-end framework for coastal hydrodynamics.
Read the full abstract ▾
Optics
Photon density of states engineering with generative inverse design for scalable 3D photonic metamaterials
Zesen Zhou, Jeevan Rois and Matias Kagias
The
photon density of states (pDOS) governs fundamental light matter interactions and is a critical parameter for designing next generation light driven technologies such as photocatalysis and
solar energy harvesting. Achieving a target pDOS in 3D nanoarchitected structures remains challenging due to the nonlinear and non unique relationship between geometry and spectral response. Here, we present an end to end inverse design framework for tailoring the pDOS of 3D photonic metamaterials fabricated via the scalable nanofabrication approach of metasurface-based
holographic lithography. A data driven forward surrogate model is constructed to predict frequency resolved pDOS spectra from metasurface diffraction parameters and lithographic thresholds. Inverse design is performed using a conditional generative
adversarial network (cGAN) that generates candidate metasurface diffraction parameters for target pDOS features. 3D structures featuring high local pDOS were obtained across a broad normalized frequency range and consistently outperformed those in the original
dataset. Structural analysis revealed that these high pDOS architectures fall into two predominant structural categories with similar rotational
symmetry characteristics. Our work establishes the first inverse design strategy for 3D photonic metamaterials fabricated via
holographic lithography.
Read the full abstract ▾
Fluid Dynamics · Cross-listed from math.NA
A new high-order finite-volume advection scheme on spherical Voronoi grids and a comparative study in a mimetic finite-volume moist shallow-water model
Luan F. Santos, Jeferson B. Granjeiro and Pedro S. Peixoto
Spherical centroidal Voronoi tessellations (SCVTs), currently used in numerical weather
forecasting models such as the Model for Prediction Across Scales (MPAS), are a type of spherical grid that is highly flexible, allowing the construction of locally refined regions with higher resolution without requiring modifications to the numerical discretization or its implementation. However, the irregularity of SCVT grids makes the construction of robust high-order schemes challenging. In particular, in atmospheric modeling, high-order advection schemes are desirable since they reduce numerical
diffusion and improve the representation of fine-scale tracer structures. Therefore, in this work, we propose a new class of high-order advection schemes on the sphere based on the $k$-exact reconstruction approach, extending their successful use on planar domains to the spherical surface. We assess the performance of the proposed method and compare it with existing advection schemes for SCVT grids used in MPAS. The evaluation includes classical advection test cases on the sphere as well as simulations with a mimetic finite-volume moist shallow-water model, in which the advection scheme is applied to the transport of moisture tracers. Grid-related robustness was investigated using locally refined spherical grids with a local focus on the Andes topography. Our results show that the proposed schemes achieve high-order accuracy in the advection tests, exhibit little sensitivity to grid distortion, and produce comparable results to existing schemes in the moist shallow-water model. Overall, grid robustness is therefore limited to the sensitivity of the discretization of the shallow-water model, irrespective of the advection scheme.
Read the full abstract ▾
Optics
Orbital and spin current density backflow in unidirectional monochromatic electromagnetic fields in vacuum
Peeter Saari and Ioannis Besieris
In this study, energy backflow in the Poynting vector, as well as its orbital and
spin current density components, has been examined for a 2-dimensional causal unidirectional vector-valued monochromatic electromagnetic wave. Linear transverse electric (TE), transverse
magnetic (TM), and circular polarization cases are considered and studied in detail, including both electric and
magnetic contributions to the current density components.
Spin current backflow has been found to be unexpectedly strong. A study of the energy backflow is also presented in the scalar version of the 2-dimensional monochromatic wave. A detailed study has been carried out of the correlation of the positions of energy backflows with local wavenumbers and their signs, the zeros of appropriate intensities and the presence of
vortices.
Read the full abstract ▾
Applied Physics · Cross-listed from quant-ph
Quantum coherent transceivers toward Holevo-limited communications
Volkan Gurses, Suraj Samaga, Elianna Kondylis et al. (+1 more)
The Holevo limit bounds the channel capacity of a communication channel in which information is encoded in
quantum states in a
Hilbert space at the transmitter and decoded using
quantum measurements at the receiver. Saturating the Holevo limit requires
quantum-limited transceivers that either generate
quantum states of light or employ
quantum-limited measurements. Here, we demonstrate an integrated photonic-electronic
quantum-limited coherent receiver (QRX) achieving 14.0 dB shot noise clearance (SNC), 520 $\mu$W knee power, 2.57 GHz 3-dB bandwidth, 3.50 GHz shot-noise-limited bandwidth, and 90.2 dB common-mode rejection ratio ($\mathrm{CMRR}$). We scale this design to a 32-channel QRX array with median 26.6 dB $\mathrm{SNC}$, and automatic $\mathrm{CMRR}$ correction yielding a median 76.8 dB $\mathrm{CMRR}$ at minimum. Using the integrated QRX and fiber-optic transmitter, we measure $0.15\pm0.01$ dB of squeezing below the shot noise limit, limited by off-chip losses. We propose a squeezed light communication scheme that can surpass the Shannon limit, with a path toward the Holevo limit.
Read the full abstract ▾
All authors Volkan Gurses, Suraj Samaga, Elianna Kondylis, Ali Hajimiri
Computational Physics
Granular mixing and flow dynamics in horizontal stirred bed reactors
Sahar Pourandi, Igor Ostanin and Thomas Weinhart
Horizontal stirred bed reactors (HSBRs) are used in gas–phase polyolefin production, where efficient solids mixing and controlled residence time distributions are essential for product quality and stability. Despite their industrial relevance, the influence of operating conditions on granular flow and mixing in HSBRs is not well understood. Discrete Element Method (DEM) simulations are used to study the effects of rotation speed and fill level on particle motion, mixing, and axial transport in a lab–scale HSBR. An industrial–grade polypropylene powder is modelled using calibrated contact parameters. Mixing is quantified using the Lacey index in axial (z) and cross–sectional (xy) directions. Particle circulation is characterised via cycle–time analysis and a coarse–grained angular velocity field. Axial dispersion coefficients are obtained from particle trajectories using both Einstein–type and cycle–based approaches, and validated with a
diffusion model predicting the axial Lacey index. Results show that axial mixing depends strongly on rotation speed and fill level: higher rotation speeds accelerate homogenization, while higher fill levels slow mixing. Cross–sectional mixing is mainly sensitive to rotation speed, with fill–level effects diminishing at higher speeds. Cycle time decreases with increasing rotation speed and fill level, indicating enhanced circulation. Axial dispersion increases with rotation speed but decreases with fill level, with consistent results across methods. These findings reveal trade–offs between axial mixing, circulation, and dispersion, highlighting the need to balance operating conditions and demonstrating the capability of DEM to support HSBR optimisation.
Read the full abstract ▾