Plasma Physics · Cross-listed from math-ph
Laplace–King representations: density and spectral theory
Yanpeng Wang and Zhe Gao
Laplace–King representations combine spherical harmonics with King functions [Wang et al., Chin. Phys. B 34, 065201 (2025)], the radial kernels of shifted isotropic Gaussians. The radial parameters may vary across angular modes. We prove that, for every angular degree, fixed-width kernels with positive real shifts have dense complex linear span in a Gaussian-weighted radial \(L^2\) space. Finite Laplace–King representations are dense in the corresponding three-dimensional weighted space; allowing variable widths preserves density in the same reference norm. A generating identity connects the kernels to generalized Laguerre
polynomials. The self-adjoint King operator is unitarily equivalent to the free radial Schrödinger operator; its spectral resolution defines a continuous King mixture model (KMM) through imaginary-shift kernels in a distinct weighted
Hilbert space.
Read the full abstract ▾
General Physics
Hyperstatistical thermodynamics of the one-dimensional Klein-Gordon and Dirac oscillators: a closed-form q-generalized Boltzmann factor and a quantitative comparison with Beck's superstatistics
Abdelmalek Boumali
We revisit the thermodynamics of the one-dimensional Klein-Gordon (KGO) and Dirac (DO) oscillators within two frameworks of generalized statistics: Beck's asymptotic superstatistics and the recently introduced hyperstatistics. In hyperstatistics, a $\gamma$-distribution of domain Boltzmann factors yields, after Laplace transformation and averaging over a normalisable density $f(\beta)$, the closed-form q-generalized Boltzmann factor $B_q(\varepsilon) = \exp_q(-\langle\beta\rangle\varepsilon)$, independent of $f(\beta)$. We compute the partition function,
entropy $S$, and specific heat $C_v$ for both 1D oscillators using excitation energies $\varepsilon_n = E_n - E_0$ to remove the rest-energy shift and enforce third-law behaviour $C_v \to 0$ as $T = 1/\langle\beta\rangle \to 0$. Appropriate degeneracies ($g_n = 1$ for KGO; $g_0 = 1$, $g_n = 2$ for $n \geq 1$ for DO) are applied. Hyperstatistics successfully (i) reproduces the high-temperature Boltzmann limit $C_v \to 2k_B$, (ii) is structurally independent of $f(\beta)$, (iii) avoids the unphysical negative regions of the Beck
polynomial bracket, and (iv) systematically distinguishes KGO from DO by capturing the enhanced
entropy and sharper specific-heat structure caused by
spin-induced degeneracy. The frameworks agree quantitatively for $q - 1 \ll 1$ and $\langle\beta\rangle E \lesssim 2$, but diverge at high temperatures where Beck's
polynomial expansion loses validity and the exact hyperstatistical q-exponential remains positive, monotonic, and analytic. Ultimately, hyperstatistics provides a numerically stable and analytically tractable alternative to asymptotic superstatistics for relativistic oscillators, naturally extensible to higher dimensions and external
magnetic fields.
Read the full abstract ▾
Accelerator Physics
Analytic Derivation of Vertical Chromaticity in the Fermilab Muon $g{-}2$ Storage Ring
Eremey Valetov, Kyoko Makino and Martin Berz
We derive the vertical chromaticity $\xi_y$ of the Fermilab
Muon g-2 storage ring in closed analytic form. Expanding the
Hamiltonian as a Taylor
polynomial in the dynamical variables and integrating the equations of motion order by order, we obtain the vertical second-order aberrations of the homogeneous
magnetic dipole ($\mathtt{DI}$) and the combined-function dipole-and-electrostatic-quadrupole element ($\mathtt{DIQ}$) used in the
muon $g{-}2$ ring. Composing the per-element maps over the periodic dispersion orbit yields a closed-form expression for the vertical chromaticity $\xichromy$ of the continuous-ring $\mathtt{DIQ360}$ model, in direct functional analogy with the horizontal result of our earlier work on the same ring (Ref.~\refcite{ChromCPO11}). Comparison against COSY INFINITY differential-
algebra computation shows agreement at the $10^{-11}$ level across all three ring models ($\mathtt{DIQ360}$ closed form and the modular $\mathtt{DIEQ\_ON}$, $\mathtt{DIEQ}$ via per-element composition) for
muon $g{-}2$ electrostatic-quadrupole (ESQ) voltages $\Vesq \in [10, 26]\,\mathrm{kV}$.
Read the full abstract ▾
Optics
Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks
Dineth Jayakody and Dushan N. Wadduwage
Diffractive deep neural networks (D2NNs) promise miniaturized, power-efficient, light-speed optical front-ends for machine
vision, yet the most mature demonstrations remain in the terahertz regime, built from readily fabricated millimeter-scale
neurons. Translating D2NNs to the visible range, where nearly all
vision pipelines operate, was long blamed on the difficulty of fabricating nanoscale
neurons; but even after recent advances removed that barrier, visible-range D2NNs matching their terahertz counterparts remain out of reach. We identify the true obstacle as the thin-layer approximation underlying nearly all D2NN training, which treats each diffractive layer as an infinitely thin mask. It fails not because of the short wavelength, as is commonly assumed, but because the low-refractive-index materials (n approximately 1.3-1.5) used at visible wavelengths require relief structures thick enough that intra-layer diffraction and phase accumulation become significant. To overcome this, we introduce a differentiable beam-propagation ($\partial$BPM) layer that models each element as a finite-thickness volume and propagates light through it during training, keeping the fabrication-compatible height map end-to-end trainable without full-wave simulation in the loop. Across MNIST, Fashion-MNIST, and CIFAR-100 classification and imaging, $\partial$BPM training substantially reduces the design-to-device mismatch, and full-wave FDTD validation raises classification accuracy from 50% to 90% without re-optimization. The $\partial$BPM layer thus offers a scalable, physics-aware bridge between efficient optical neural-network optimization and fabrication-consistent diffractive design.
Read the full abstract ▾
Chemical Physics
RLEASE: Reinforcement Learning Efficient Active Space Engine
Etinosa Osaro, Abhishek Mitra, Andrew J. Jenkins et al. (+6 more)
Selecting the active space for multireference electronic-structure calculations is a long-standing bottleneck that often requires expert chemical intuition and costly trial-and-error. We introduce RLEASE (Reinforcement Learning Efficient Active Space Engine), a low-cost method for automatic, geometry-dependent active-space selection. A neural network predicts per-orbital diagnostic scores ($\hat{s}_{1}$) from inexpensive Hartree-Fock orbital descriptors, and a learned threshold partitions orbitals into active and inactive sets. The threshold policy is optimized with proximal policy optimization, using the discrepancy between sc-NEVPT2 energies computed with the selected active space and DMRG reference energies as the reward. After training, the same RLEASE-selected active spaces can be used with multireference perturbation theory or composite coupled-cluster energy
estimators. Despite being trained on a small set of molecules and geometries, RLEASE transfers to chemically diverse test systems, producing compact active spaces and competitive potential-energy surfaces relative to established
entropy-based selectors. Because deployment requires only inexpensive orbital descriptors and neural-network inference, RLEASE enables high-throughput multireference workflows without molecule-specific retraining or target-system pilot DMRG calculations.
Read the full abstract ▾
All authors Etinosa Osaro, Abhishek Mitra, Andrew J. Jenkins, Kelsey A. Parker, Robert H. Lavroff, Verena A. Neufeld, Arpan Kundu, Arvin Kakekhani, Dario Rocca
Computational Physics · Cross-listed from cs.LG
Instrumented data for causal scientific machine learning
Daniel N. Wilke
Scientific machine learning is limited less by model size than by the data it is trained on. Observational data records what happened but not why; template synthetic data has a known generating process but only for the simulator's template, not the case a user faces. We argue a third option is now operationally feasible: instrumented data, in which every datum carries the mechanistic model that produced it, an explicit uncertainty over that model, and an executable family of counterfactuals. Verification-and-validation (V&V) instrumented image-to-simulation pipelines are one realisation: a sensor observation becomes a fully specified, solver-backed simulation with explicit, editable parameters and a propagated aleatoric/epistemic uncertainty. The substrate is case-specific, mechanistically supervised, and supports causal interventions through Pearl's do-operator. Near-term consequences for validation,
auditing, and surrogate training span computational biology,
climate, materials, fluid mechanics, and medical imaging; a longer-term, falsifiable implication concerns foundation models for scientific
reasoning.
Read the full abstract ▾
Fluid Dynamics
Scalar gradient structure and dynamics in turbulent mixing at high Reynolds and Schmidt numbers
Ruqaiya Islam Mishi and Dhawal Buaria
How well
turbulence mixes a scalar $\theta$ is governed by the scalar dissipation rate $\chi = 2D |\nabla\theta|^2$, making scalar gradients central to turbulent mixing. We study the structure and amplification of these gradients for passive scalars driven by a uniform mean-gradient in isotropic
turbulence, using DNS at grid resolutions up to $8192^3$. The $Re_\lambda$ spans $140-1000$, and $Sc\equiv\nu/D$ spans $1-512$. We analyze joint statistical correlations of velocity and scalar gradients that underlie scalar-gradient amplification. Unconditional statistics reaffirm earlier observations that production of $\chi$ is dominated by nonlinear amplification of scalar gradients by strain-rate. Scalar gradients preferentially align with the most compressive strain eigenvector and remain orthogonal to vorticity, with both trends virtually independent of $Re_\lambda$ and $Sc$. Conditional statistics reveal that this organization becomes dramatically enhanced in regions of intense scalar dissipation: scalar gradient becomes near-perfectly aligned with the most compressive eigendirection and orthogonal to other eigendirections and vorticity. This and visualizations suggest that intense scalar dissipation is organized in sheet-like structures formed in shear layers between
vortex tubes, where intense strain also generally resides. However, the effective strain acting along intense scalar gradients is comparatively much weaker, indicating intense scalar dissipation arises primarily from optimal
alignments rather than intense strain alone. Molecular
diffusion arrests intense scalar-gradient events primarily by redistributing scalar-gradient variance away from intense structures. The contribution from imposed mean-gradient is negligible,but still imprints anisotropy directly onto smallest scales via the strain field. The statistics broadly become universal as $Sc$ and $Re_\lambda$ increases
Read the full abstract ▾
Medical Physics
ULMShare: A Large-Scale In Vivo Ultrasound Localization Microscopy Dataset for Microvascular Imaging
Brice Rauby, Nin Ghigo, Gerardo Ramos-Palacios et al. (+13 more)
Ultrasound Localization Microscopy (ULM) enables microscopic imaging of the cerebral microvasculature in vivo, but relies on a multi-stage processing pipeline in which acquisition settings and reconstruction processes strongly influence the final output. Existing public
datasets remain sparse, restricting rigorous evaluation and slowing progress in
algorithm development, including emerging machine-learning approaches, which by design require large quantities of data to be robust and reliable. We introduce ULMShare, an open-access
dataset of 99 whole-
brain transcranial ULM acquisitions from 61 healthy mice (36 females, 22 males, 3 unknown; mean age: $8.2 \pm 5.5$ weeks; mean weight: $17.7 \pm 4.2$ g), for a total of 30TB of raw data. The
dataset spans three experimental procedures, multiple injection and anesthesia protocols, two ultrasound probes, and different imaging planes and orientations. Each acquisition includes raw ultrasonic data, detailed metadata, an illustrative reconstruction and the corresponding microbubble trajectories. Alongside the data, we report vascular saturation,
Fourier Ring Correlation, and track-length statistics, plus expert visual gradings. ULMShare provides a broad, standardized and publicly available resource for method development, validation, and benchmarking. The full
dataset is available on the
Federated Research Data Repository and additional resources are hosted on the ULMShare Github repository.
Read the full abstract ▾
All authors Brice Rauby, Nin Ghigo, Gerardo Ramos-Palacios, Alexis Leconte, Stephen A. Lee, Alice Wu, Paul Xing, Oleksandra Gulenko, Louis Caron, Antoine Malescot, Eric Martineau, Jonathan Porée, Maxime Gasse, Ravi L. Rungta, Abbas F. Sadikot, Jean Provost
Computational Physics
PDE-Agents: An LLM-Orchestrated Multi-Agent Framework for Automated Finite Element Simulations with Knowledge Graph-Augmented Reasoning
Sayan Adhikari, Gulshan Noorsumar and Øyvind Jensen
We present PDE-
Agents, a multi-
agent ecosystem that automates the full lifecycle of partial differential equation (PDE) and finite element method (FEM) simulations through natural-
language interaction. Three large
language model
agents, Simulation, Analytics, and Database, are orchestrated by a LangGraph supervisor and run locally using Qwen3-Coder-Next and Llama 4 Scout on dual NVIDIA RTX PRO 6000 Blackwell GPUs. The model-agnostic architecture is validated across two generations of open-source LLMs. A GraphRAG knowledge base using Neo4j and 768-dimensional vector
embeddings provides material properties, failure patterns, and prior run lineage. We report seven contributions: (i) verification and validation showing second-order spatial convergence, O(h^2), for three heat-equation
benchmarks; (ii) a 50-task ablation with a frozen knowledge graph comparing KG On, KG Off, and KG Smart, where KG Smart achieves 100% success and the highest output quality, with physics score 0.933 versus 0.853 and material property fidelity (MPF) 0.926 versus 0.796 for KG Off; (iii) a novel-material study using three fictional materials known only to the knowledge graph, where KG Smart reaches MPF = 1.00 versus 0.34 without the graph; (iv) failure analysis tracing KG On's three failures to budget exhaustion and timeout, identifying warm-start injection as the main reliability factor; (v) an adaptive framework selecting
retrieval mode per task; (vi) production metrics from 1,369 runs showing 97.8% overall success and 85.4% first-try success; and (vii) a 100-task knowledge-graph growth study showing an 8.8% MPF gain on hard tasks while easy and novel tasks remain at ceiling. All code, models, and evaluation artifacts are openly released. These results show that integration pattern, rather than knowledge content alone, determines whether GraphRAG helps or hinders LLM
agents.
Read the full abstract ▾
Plasma Physics
Hybrid simulations of the proton beam instabilities in the young solar wind. The formation of hammerhead-like distributions
R. A. López, Shaaban M. Shaaban, M. Lazar et al. (+4 more)
Parker
Solar Probe (PSP) observations in the young
solar wind reveal new properties of both
plasma particle velocity distributions (VDs) and associated electromagnetic (EM) wave fluctuations. The quasilinear (QL) kinetic theory of
plasma wave instabilities has recently shown that new hammerhead (HH)
proton distributions can be generated by the relaxation of
proton beams through the instabilities of right-handed (RH) polarized waves. Such RH waves have indeed been reported in association with HH distributions. In this paper, new results from hybrid simulations of
proton-beam-
plasma systems with properties typical of those observed to excite EM-RH wave instabilities are presented. From the long-term
evolution of these systems, it is found that beam relaxation is driven by instabilities and growing wave fluctuations, leading to HH-type features in the velocity distributions. The production of these features, as well as their prominence, depends on the
magnetic power of the waves generated by the instabilities and, therefore, implicitly on the available free energy, quantified by the
plasma beta parameter and the relative beam drift. The simulation results capture the self-consistent
evolution of the instabilities and their nonlinear development. Linear theory, together with simulations, helps identify the nature of the unstable modes and the
plasma conditions under which they arise. The good agreement with quasi-linear (QL) theory further indicates that it can serve as a computationally efficient complementary framework for interpreting the associated wave-particle interactions.
Read the full abstract ▾
All authors R. A. López, Shaaban M. Shaaban, M. Lazar, L. Pezzini, S. Poedts, H. Fichtner, P. H. Yoon
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Agentic multi-fidelity learning of quasiparticle and excitonic properties
Arnab Neogi, Aaron Forde, Christopher A. Lane et al. (+2 more)
Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an
agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2
bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural
agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and
exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-
agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong
quantum confinement, such as
quantum dots, nanoribbons, layered two-dimensional
semiconductors, and hybrid perovskite nanostructures.
Read the full abstract ▾
All authors Arnab Neogi, Aaron Forde, Christopher A. Lane, Sergei Tretiak, Jian-Xin Zhu
Optics
Independent amplitude and phase control using a single phase-only SLM
Marius Constantin Chirita Mihaila, Mikuláš Fiala and Martin Kozák
Liquid-
crystal spatial light modulators (SLMs) are widely used for programmable wavefront control but are typically operated as phase-only devices, limiting applications that require independent amplitude and phase shaping. Here we demonstrate full complex-field modulation using a single phase-only SLM by implementing two sequential modulation planes on different regions of the same device. The phase retardance introduced by the first SLM region is converted into amplitude modulation by a polarizer placed in the beam path, while the second region compensates the associated phase offset and imposes the required phase distribution. The field from the first region is imaged onto the second, enabling complex-field synthesis without a second modulator. We validate the approach by generating Bessel–Gaussian beams, helical-phase fields, and arbitrary focal-plane intensity patterns. This single-SLM platform provides a compact route to programmable complex wavefront engineering for structured illumination, holography, and electron–light interaction experiments.
Read the full abstract ▾
Fluid Dynamics
Nonspherical gas bubble dynamics in viscoelastic soft materials
Sawyer Remillard and Mauro Rodriguez Jr
Nonspherical gas bubble dynamics in viscoelastic materials influence the stress transmission and energy dissipation of their surroundings and are difficult to predict. Their accurate prediction is essential in applications ranging from biomedical procedures to high-strain-rate rheological measurements. However, existing models do not sufficiently capture the nonspherical rotational dynamics. We formulate and superpose a rotational contribution to the perturbed deformation with a potential contribution. Linearised forward and inverse coordinate maps are formulated based on the deformation field which are used to compute velocities, accelerations, and stresses. The addition of the rotational degree of freedom satisfies the momentum balance equations and stress continuity at the bubble surface. The material surrounding the bubble is modelled with a Kelvin-Voigt constitutive model with Newtonian viscosity and quadratic strain-stiffening neo-Hookean elasticity. The model agrees with previous viscous fluids models when elastic effects are neglected and radial oscillations are small. When viscous effects are small relative to elastic, shear waves radiate from the bubble surface into the material. The resulting strain energy is delocalised and increases damping of the perturbation amplitude in time relative to potential-based models. We show agreement between the stability of the shape modes with previous ultrasound forced experiments and temporal
evolution of different shape modes with previous
laser-induced cavitation experimental data.
Read the full abstract ▾
Optics
Deep-level engineering in aluminum nitride (AlN) enables reliable photodetection in extreme conditions
Jiahao Dong, Zhenjing Liu, Sara Tulchinsky et al. (+1 more)
Semiconductor photodetectors are conventionally optimized for sensing weak optical signals, and they typically saturate at low-to-moderate light intensity. Here, we demonstrate AlN photodetectors that exhibit non-saturating linear response to ultra-bright, sub-bandgap light exceeding 40 $\mathrm{W/cm^2}$ (over 400 Suns). The photodetector further shows undistorted linear response at elevated temperature, up to at least 300 $\mathrm{^\circ C}$. This exceptional performance originates from photoresponse mediated by point defects with energy levels deep in the bandgap (deep levels) at the metal-AlN Schottky junction. Through dopant design and contact engineering, we demonstrate that a narrow space-charge region is essential for enabling ultra-bright light detection and accurate measurement. These results establish a strategy for engineering ultra-wide bandgap (UWBG)
semiconductor devices for reliable operation in extreme conditions to meet emerging needs in industrial process control, thermal and
nuclear power generation, and aeronautics and spaceflight.
Read the full abstract ▾
All authors Jiahao Dong, Zhenjing Liu, Sara Tulchinsky, Rafael Jaramillo
Atomic Physics · Cross-listed from quant-ph
Near-deterministic single-atom loading on a photonic integrated circuit
Xinchao Zhou, Ahreum Lee, Dipanjan Das et al. (+2 more)
Coupling identical
quantum emitters to a photonic integrated
circuit (PIC) is a key step for scaling up emitter-
photon interfaces for
quantum science and information processing. Neutral atoms are attractive candidates due to their indistinguishability and controllability. However, experimental realizations of efficient atom trapping on a PIC while achieving strong single atom-
photon coupling has so-far remained elusive. Here, we demonstrate near-deterministic single-atom loading on a microring resonator
circuit, reaching single-atom cooperativity parameter C > 1 for strong coupling in cavity
quantum electrodynamics. We utilize a precision optical conveyor belt, formed by a moving optical
lattice in an optical tweezer, to steadily deliver trapped atoms onto a PIC. By continuously monitoring the transmission of probe
photons through the
circuit, which is sensitive to the proximity of single atoms near a microring resonator, we detect mean occupancy of 1.5 from 70 occupied
lattice sites in a conveyor-belt transport of 4 nm position reproducibility. Based upon real-time feedback, we deterministically transfer the delivered atoms into a stationary trap on the microring, achieving 82% (18%) probability of single-(two-)atom transfer. Our technique can be extended to deterministic, highly efficient atom array assembly, providing a scalable route for neutral atom integration with PICs of complex functionalities.
Read the full abstract ▾
All authors Xinchao Zhou, Ahreum Lee, Dipanjan Das, Saivirinchi Prabandhakavi, Chen-Lung Hung
Biological Physics
The Chemotactic Index for Spatial Gradient Sensing
Nicholas A. Licata
We consider the problem of quantifying the chemotactic efficiency of single
cells as measured by the chemotactic index $\Psi$. Previous work in a model framework for direct sensing of spatial gradients indicated that $\Psi$ depends on a single dimensionless group $s$, which plays the role of the square of the signal to noise ratio in the problem. We revisit this problem theoretically and demonstrate that the cumulants in the model can be calculated exactly. We derive explicit results for the multivariate cumulants up to third order in terms of the diffusive current density and Gaunt coefficients. We discuss the machinery required to translate Burg-Purcell style limits on concentration gradient uncertainty into results for the chemotactic index. We compute the leading corrections to $\Psi$ in an Edgeworth expansion, and identify a dimensionless group $\lambda$ in the problem which is a ratio of concentrations that captures the effects of the non-Gaussianity. By careful consideration of experimental results on slime mold chemotaxis, we demonstrate that the explanatory success of the original Gaussian approximation for the chemotactic index stems in part from the fact the concentration gradients were shallow, $|\lambda| \ll s$.
Read the full abstract ▾
Fluid Dynamics
Cascades in the Kinetic Equation for the Majda-McLaughlin-Tabak model
Gregorio Tibone, Giorgio Krstulovic and Miguel Onorato
The Majda-McLaughlin-Tabak (MMT) family of models has proven to be an efficient ground for benchmarking wave
turbulence theory, thanks to the low computational cost required to test theoretical ideas and the possibility of tuning nonlinearity and dispersive properties of the equations. Here, we study numerically the wave kinetic equation (WKE) associated with the MMT model and perform simulations to study turbulent cascades. We confirm numerically the predictions of wave
turbulence theory, both in the parameter space region where the wave kinetic equation was proven to be well posed and outside of it. We also observe a new stable stationary state in a region where no cascade solutions are expected, a region that, to the best of our knowledge, has not been explored before. Moreover, following recent work, we study next-to-leading-order corrections to the wave kinetic equation; we uncover incurable divergences in the one-dimensional MMT model and, more generally, in higher-dimensional systems with concave power-law dispersion relations.
Read the full abstract ▾
Geophysics
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Nick Teutschmann, Laura Crocetti, Fanny Lehmann et al. (+2 more)
Displacement time series from Global
Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data are freely available. We present GNSS-FM, a self-supervised foundation model for daily GNSS time series. The model uses a dual-stream input combining displacement and velocity-like increments, and is pretrained using a masked latent prediction objective with vector-quantized targets adapted from wav2vec 2.0, with several modifications for geodetic data. Pretrained on data from over 17,000 globally distributed GNSS stations, an analysis of the learned codebook suggests that the representations capture the main signal types in GNSS displacement data, including seismic offsets, tectonic drift, and seasonal patterns. The foundation model is later fine-tuned on two downstream tasks, namely 90-day displacement
forecasting and seismic step localization, where it outperforms strong task-specific baselines in both cases. These results show that self-supervised pretraining is a promising approach for GNSS time series analysis.
Read the full abstract ▾
All authors Nick Teutschmann, Laura Crocetti, Fanny Lehmann, Leonardo Trentini, Benedikt Soja
Atmospheric and Oceanic Physics
TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research
Haoluo Zhao, Hongchun Zhang, Nan Li et al. (+6 more)
As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a main challenge in atmospheric chemistry. Yet mechanism validation based on complex numerical models still relies heavily on expert knowledge: mechanistic hypotheses must be operationalized into executable experiments, and model outputs must be organized into traceable evidence. We present TianJi-Environ, an auditable AI Scientist for atmospheric-chemistry mechanism validation. TianJi-Environ establishes the first WRF-Chem-based multi-
agent framework that autonomously drives complex atmospheric-chemistry simulations, converting mechanistic hypotheses into executable configurations, testing experiments, and evidence criteria. Using ozone response and particulate-matter feedback as two representative examples, we demonstrate TianJi-Environ's capability for mechanism validation. In a summertime ozone case over the North China Plain, the system detects directionally consistent aerosol-radiation-interaction signals in shortwave radiation and boundary-layer height, but judges the evidence for ozone response to NOx control to be incomplete. In a wintertime PM2.5 case over the Guanzhong Basin, it localizes the unsupported link to insufficient propagation from black-carbon perturbation to particulate response and missing diagnostics of vertical absorptive heating. These results show that TianJi-Environ makes expert-driven mechanism validation explicit, structured, and auditable, offering a reproducible paradigm for multi-
agent systems coupled with complex atmospheric-chemistry models.
Read the full abstract ▾
All authors Haoluo Zhao, Hongchun Zhang, Nan Li, Jing-Jia Luo, Kaikai Zhang, Mengyang Yu, Nan Chen, Tao Song, Fan Meng
Medical Physics
Time-Resolved Stress Analysis of Tissue Simulants During Needle-Free Jet Injection
Kohei Yamagata, Prasad Sonar, Kazuhiro Terai et al. (+2 more)
Needle-free jet injection generates transient internal stress fields that can influence tissue deformation, pain-related stimulation, and cellular-level mechanical responses. However, the penetration mechanics have often been inferred from cavity deformation and interpreted mainly as shear-dominated behavior. In this study, high-speed photoelastic measurements were used to visualize and quantify optically integrated stress responses in a 5 wt% gelatin tissue simulant during penetration by two needle-free injectors with different actuation mechanisms: the Actranza Lab, a pyro-drive injector driven by cartridge-based combustion, and the Biojector 2000, a commercially available CO$_2$-driven injector. A polarization camera operated at 60,000 fps was used to obtain the phase difference and principal stress orientation, allowing evaluation of the photoelastic stress-intensity response and its decomposed normal- and shear-stress-related components. Under the same injection volume of 20 $\mu$L, the Actranza Lab formed a narrow, depth-oriented cavity, whereas the Biojector 2000 produced a wider, bulged cavity. In both cases, a clear normal-stress-difference component developed around the cavity. This component became comparable to or greater than the shear-stress component for the Actranza Lab and became dominant during the later cavity-bulging stage for the Biojector 2000. These results show that needle-free jet penetration cannot be described solely by shear stress; instead, injector-dependent cavity dynamics generate multi-component tissue loading. The findings provide an engineering basis for evaluating needle-free injector performance and for designing systems that improve delivery while reducing mechanical burden on tissue.
Read the full abstract ▾
All authors Kohei Yamagata, Prasad Sonar, Kazuhiro Terai, Yuto Yokoyama, Yoshiyuki Tagawa
Optics · Cross-listed from hep-ex
A homodyne detection scheme for all-optical photon-photon scattering experiments using 2D detectors
Timo Pohle, Leonard Doyle, Paul Ihde et al. (+3 more)
Low signal-to-noise ratios are a common problem in experiments attempting to measure
photon-
photon scattering. In the optical regime, where petawatt
lasers with femtosecond pulse durations are used, the large beam sizes cause the major contribution of the background to be spread over up to 100 ps in arrival time, whereas the signal is confined to the femtosecond scale. We present a balanced homodyne measurement scheme, which exploits this property to suppress the background. By interfering the signal with a short reference pulse, the measurement becomes effectively gated to the pulse duration and is therefore only sensitive to the co-timed part of the light, reducing the effective background by 3-4 orders of magnitude. Additionally, increasing the reference pulse energy increases the amplitude of the measured quantity without changing the intrinsic signal-to-noise ratio. Using this property, other external noise sources can be made negligible by boosting the measured quantity above the noise floor. Using two-dimensional detectors further enhances the scheme by improving sensitivity and enabling self-referenced single-pulse measurements. In addition, an evaluation procedure based on maximum-likelihood estimation is presented and demonstrated. The robustness and performance of this scheme are demonstrated on simulated data, where a more than 100-fold reduction of measurement time compared to conventional
photon-counting methods under realistic conditions is found.
Read the full abstract ▾
All authors Timo Pohle, Leonard Doyle, Paul Ihde, Felix Karbstein, Jörg Schreiber, Matt Zepf
Optics
Collective emission of subwavelengths atom-like emitter arrays in the presence of inhomogeneous broadening
Uri Israeli, Shahar Levi, Sagi Ben-Avi et al. (+4 more)
Quantum metasurfaces comprised of subwavelength atomic arrays emerged as a promising platform for enhanced atom-
photon interaction. However, realizing such a system with solid-state emitters has been considered impractical due to strong inhomogeneous broadening, which was expected to suppress the
photon-mediated interactions that underpin collective emission. Here we report the observation of collective emission from subwavelength arrays of silicon-vacancy centres in diamond – solid-state emitters whose inhomogeneous broadening exceeds the natural linewidth by two orders of magnitude – demonstrating that collective effects such as resonance shifts, modified decay rates and directional coherent emission survive this disorder. A crucial enabling element is the implantation of a high density of silicon ions at each array site. This creates so-called superatoms, local ensembles that probabilistically achieve frequency matching across the array and enhance the collective response. We support our observations with a theoretical analysis explaining the mechanisms that preserve the collective effects even in the presence of inhomogeneity. These observations have direct implications for the realization of subwavelength arrays in any solid-state system, paving the way for
quantum-emitter metasurfaces that are naturally integrated into nanophotonic environments.
Read the full abstract ▾
All authors Uri Israeli, Shahar Levi, Sagi Ben-Avi, Ada Kransnovsky, Daniel Silvian, Shlomo Winberg, Rivka Bekenstein
Optics
Optical Poling Reveals Hidden Molecular Restructuring in Multimode Fibers, Unlocking Ultra-Efficient Third-Order Nonlinearities
Maxime Jonard, Thomas Larqué, Dan-Esli Bouyou Bouyou et al. (+10 more)
Optical poling is a well-established technique for inducing \chi^{(2)} nonlinearity, yet its impact on silica's molecular structure remains unexplored. Here, we report the first direct observation of molecular restructuring in large-core graded-index multimode fibers (MMFs) induced by optical poling, transforming the silica tetrahedral ring network. Through coherent light beating, this process converts large rings of more than four SiO_4 tetrahedra into smaller ones, altering both linear and nonlinear optical susceptibilities. Contrary to the assumption that poling efficiency stems solely from charge displacement, we show that structural modifications dominate, leading to record enhancements in third-order nonlinear processes, including
geometric parametric instabilities (GPIs) and Kerr self-cleaning, despite a low modification of the Kerr coefficient. High-energy poling acts as an in situ annealing process, dynamically modulating the refractive index for unprecedented spatiotemporal light control. These findings provide fundamental insights into silica's molecular dynamics under intense optical fields and open avenues for ultra-efficient nonlinear optical devices, enabling next-generation fiber-based photonics for high-power
lasers, broadband light generation, and all-optical signal processing.
Read the full abstract ▾
All authors Maxime Jonard, Thomas Larqué, Dan-Esli Bouyou Bouyou, Tigran Mansuryan, Alessandro Tonello, C. Sandt, Jean-René Duclère, Julie Cornette, Yago Arosa, Marc Fabert, Claire Lefort, Vincent Couderc, Maggy Colas
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization
Shi-Yang Chen and Xiao-Ming Cao
The dynamic structural
evolution of catalyst surfaces under operating conditions dictates catalytic performance, yet capturing these reconstructions atomically remains challenging. Global optimization based on machine learning interatomic potentials (MLIPs) is promising, but scaling to large-scale, low-
symmetry operando systems is hindered by expansive search spaces and potential energy surface (PES) inaccuracies. Herein, we present Harmony-search-based Atomic Structural Global Optimization (HASGO), a framework integrating universal MLIPs with a harmony search
algorithm. HASGO overcomes the problem of PES softening by incorporating a multi-head replay fine-tuning protocol. Moreover, the
stochastic structural perturbation step in its
algorithm offers a fault-tolerant strategy to enhance the robustness of global convergence. These enable HASGO to identify intricate surface
oxide overlayers that align with atomic-resolution microscopy, thereby resolving the square-pyramidal subsurface O5 motif on Ag(100) during ethylene epoxidation. This scalable framework provides a robust approach for uncovering operando structures, accelerating the rational design of industrial catalysts.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from nlin.CD
Unified Geometry-Guided ML-FTLE for Tracking Transient Chaos from Scalar Time Series
S. V. Manivelan, Andrei Velichko and I. Manimehan
Detecting transient chaos from scalar observations without governing equations represents a fundamental challenge in nonlinear dynamics. We propose a geometry-guided machine learning framework that unifies predictive trajectory divergence with macroscopic attractor morphology to track abrupt regime shifts. The methodology extracts a local instability scale via out-of-sample k-nearest neighbor forecast errors to establish the ML-FTLE
estimator, subsequently mapping this temporal divergence onto a structural closeness
matrix derived from a minimal dictionary of Poincare occupancy grids. By employing partial least squares regression, we extract a latent
geometric component calibrated directly to the empirical finite-time Lyapunov spectrum, yielding the Poincare-based
geometric-guided FTLE. Validation against analytical QR-FTLE baselines confirms that fusing topological state spaces with predictive divergence systematically improves continuous transition tracking. The Structural Similarity Index optimally resolves gradual damping, while Hausdorff Distance exhibits extreme resilience during abrupt phase-space collapses. Furthermore, macroscopic spatial discretization acts as a robust topological regularizer against additive Gaussian noise, preserving deterministic signatures even at moderate signal thresholds. This equation-free framework provides a highly accurate, noise-resilient diagnostic for monitoring structural transitions in complex non-stationary systems.
Read the full abstract ▾
Chemical Physics
Correcting basis-set errors due to the absence of the electron-electron cusp using an adiabatic correction
Anthony Scemama and Andreas Savin
This article proposes an analytical method to address the slow convergence of electronic structure calculations caused by the inability of finite one-particle basis sets to describe the electron-electron cusp. An equivalence is established between a calculation using a finite basis set with the physical Coulomb interaction and a calculation using a complete basis set with a model interaction (specifically, the error-function screened Coulomb potential characterized by a range-separation parameter $\mu$). By leveraging the adiabatic connection formalism, a simple, parameter-free correction formula is derived. It depends only on the on-top pair density and a locally defined range-separation parameter ($\mu$) derived from the basis set itself. This `adiabatic connection based basis-set error correction' (ABC) is derived from the asymptotic expansion of the wave function at large $\mu$ for small inter-electronic distances. Therefore it is applicable to both ground and excited states without the restriction imposed by the Hohenberg-Kohn
theorem. Numerical tests illustrate that the method achieves chemical accuracy using smaller basis sets than typically required.
Read the full abstract ▾
Physics and Society
Boundary crossings as a minimal mechanism for global connectivity in structured landscapes
Fabio Nelli
Global connectivity in complex systems often emerges from local interactions, yet it remains unclear whether sophisticated
agent strategies are required for such reorganisation. Here we identify a minimal mechanism by which heterogeneous mobility can generate system-wide connectivity when
agents move within a structured multi-attractor landscape. In an
agent-based model, a small fraction of higher-mobility
agents explores the environment while the majority remains locally constrained. Configurational transitions are strongly localised at inter-attractor boundaries, where local crossing events accumulate over time to produce an expanding transition network and a percolation-like shift from fragmented to globally connected regimes. The core phenomenon persists under pure random-walk dynamics, showing that neither optimisation nor goal-directed behaviour is necessary. Interface-sensitive exploration nevertheless increases the transition rate by approximately four- to fivefold per exploratory movement relative to the random-walk baseline, thereby accelerating network integration. A novelty term produces only a small and non-uniform additional rate increase and no consistent improvement in unique-edge production. By contrast, when landscape structure is removed, transition-network connectivity arises through unconstrained spatial mixing, eliminating the boundary-mediated interpretation of the process. These results identify a minimal mechanism through which simple
stochastic movement and spatial constraints generate large-scale connectivity. Interfaces between basins emerge as critical mediators linking local dynamics to global organisation, with potential implications for systems in which movement occurs across structured environments.
Read the full abstract ▾
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
Isabel Creed, Tim Rein, Ingvars Vitenburgs et al. (+21 more)
Machine-learned interatomic potentials (MLIPs) have had a profound impact on molecular modelling in recent years, promising to resolve the long-standing tension between the scale and accuracy of simulations. There has been a proliferation of new models and designs, and recently the paradigm of “foundational” MLIPs has become prevalent. Broadly speaking, foundation models are trained on large diverse
datasets and promise to work well for new systems with minimal updates required. However, in such a new and fast moving field, there are many unanswered questions. In this article, we set out to articulate and explore what we see as the most important among these questions. We start by developing a working definition for foundational MLIPs and use this definition to frame the subsequent open questions. Despite the rapid progress in the field of MLIP models, we believe that these are fundamental questions which will continue to define cutting edge research in MLIPs in the years to come.
Read the full abstract ▾
All authors Isabel Creed, Tim Rein, Ingvars Vitenburgs, Wojciech G. Stark, Viktor Ellingsson, Ahmed Y. Ismail, Guangyu Liu, Yuchen Lou, Bradley A. A. Martin, Cyprien Bone, Matthew A. H. Walker, Mueen Taj, Shirui Wang, Kelvin Wong, Ruiqi Wu, Prakriti Kayastha, Bingqing Cheng, Aditi Krishnapriyan, Michele Ceriotti, Marcel F. Langer, Jarvist Moore Frost, Alex M. Ganose, Venkat Kapil, Keith T. Butler
Atomic Physics
Anomalous Autler-Townes Splitting in Resonant Multiphoton Ionization Driven by Bright Squeezed Vacuum
Xu Zhang, Liding Li, Yutong Deng et al. (+5 more)
Bright squeezed
vacuum (BSV) light has a vanishing mean optical electric field yet can strongly enhance strong-field nonlinear responses beyond the conventional semiclassical paradigm. Here we examine this scenario in the light-matter strong-coupling regime by investigating resonant multiphoton ionization of atoms driven by BSV, using a fully
quantum treatment of both the electron and the field. Our results show that the photoelectron energy spectrum exhibits an anomalous Autler-Townes splitting whose magnitude grows with the Above-threshold-ionization (ATI) order, rather than remaining essentially ATI-order independent as in the case of coherent driving. This behavior reflects a general scaling with the number of absorbed
photons and originates from the broad
photon-number fluctuations of the driving field together with the resulting electron-field
entanglement. We further show that the BSV-induced enhancement of ionization yields evolves with intensity, crossing over from the $g^{(p+1)}$ limit to the $g^{(p)}$ limit as Rabi oscillations become established. These results identify a
quantum regime of strong-field ionization governed by the interplay of
photon statistics, nonlinear transitions, strong coupling, and nonseparable light-matter dynamics.
Read the full abstract ▾
All authors Xu Zhang, Liding Li, Yutong Deng, Xinyou Lv, Yang Li, Marcelo F. Ciappina, Peixiang Lu, Yueming Zhou
Fluid Dynamics · Cross-listed from cond-mat.soft
Microswimmers create bicontinuous emulsions in binary fluids
Harinadha Gidituri, Sotiris Samatas and Juho S. Lintuvuori
We consider a generic case of neutrally wetting microswimmers in symmetric mixtures of two phase separating fluids, using hydrodynamic simulations. The swimmers spontaneously emulsify the two fluids into bicontinuous foam-like state. The two principal activity components: source dipole (self-propulsion) and force dipole (active mixing), create a twofold mechanism to stabilise the structures. When the self-propulsion is too strong, the swimmers cross the interfaces rapidly and the two fluids will phase separate. Below this threshold, the active stresses from the force dipoles, stabilise a dynamic and bicontinuous foam-like state. When the activity is turned off, the system relaxes into a kinetically trapped bicontinuous state, with particles permanently trapped at the interfaces. Our results provide a microscopic route to tunable active emulsions, with implications for bacterial suspensions and synthetic active matter.
Read the full abstract ▾