Geophysics
Correlative Microstructural Analysis of a Weathered Nantan Meteorite Fragment
Graeme J. Francolini, Brendan V. Dyck, Paul Mack et al. (+1 more)
The weathering of iron-rich phases within meteorites is a multi-stage process that significantly alters the microstructure and chemical composition based both on the environmental condition at the location of landing and the exposure time since the fall. This work investigates the resulting phases of this process in a correlative and comparative manner using a naturally weathered Nantan meteorite fragment. Techniques including X-ray Photoelectron Spectroscopy (XPS), Energy Dispersive X-ray Spectroscopy (EDS), and X-ray Fluorescence Spectroscopy (XRF) were used for chemical and compositional determination and X-ray Diffraction (XRD) and Electron Backscatter Diffraction (EBSD) for phase determination and microstructural analysis. The combination of these techniques revealed the meteorite
matrix to be predominantly composed of magnetite, with goethite and maghemite present as secondary phases. The meteorite had two pairs of distinct regions, one separated by Ni content and the other by grain size and Cl distribution. The Ni content regions are believed to have formed from a former Widmanstätten pattern, with the high ($\geq$2.6 at%) and low ($\leq$0.9 at%) Ni regions from plessite and kamacite, respectively. The kamacite and taenite within the plessite are suggested to have weathered through distinct mechanisms. The grain size regions were separated by a visible boundary 100-200 $\mu$m wide where grains transitioned rapidly from fine to elongated grains and the Cl content changed from low and dispersed to concentrated spots. Lastly, a brecciated phase was found within the sample and was identified as a large cohenite grain which exhibited signs of weathering, including vein-like extensions of the
matrix composed of NiO and magnetite, and deposits of iron and nickel carbonates.
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All authors Graeme J. Francolini, Brendan V. Dyck, Paul Mack, Ben Britton
Geophysics
Iron spin crossover in ferropericlase and its effect on lower-mantle thermal conductivity
Alexander F. Goncharov, Irina Chuvashova, Eric Edmund et al. (+29 more)
Thermal conductivity of Earths lower mantle controls heat transfer across the core-mantle boundary (CMB) and strongly influences mantle convection. We report direct measurements of the thermal conductivity of single-
crystal ferropericlase (Mg$_{1-x}$Fe$_x$O, $x = 0.09$-0.13), the second most abundant lower-mantle mineral, using optical
laser flash and X-ray free-electron
laser heating in diamond-anvil
cells up to $\sim2200$~K and 130~GPa. These experiments provide the first conductivity data for ferropericlase at simultaneous lower-mantle pressures and temperatures. A marked reduction in conductivity between 60 and 100~GPa at $\sim1700$~K is consistent with the iron
spin crossover. Combined with our previous results for Fe- and Fe,Al-bearing bridgmanite, the data define a lower-mantle conductivity profile that increases with pressure to $\sim10$~W\,m$^{-1}$\,K$^{-1}$ near the CMB, constraining mantle heat flux, plume buoyancy, and long-term geodynamic
evolution.
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All authors Alexander F. Goncharov, Irina Chuvashova, Eric Edmund, JungFu Lin, Zena Younes, Nicolas Jaisle, Axel Phelipeau, Carmen Sanchez-Valle, Christoph Otzen, Clemens Prescher, Hanns-Peter Liermann, Nico Giordano, James McHardy, Karen Appel, Michal Andrzejewski, S. V. Rahul, Minxue Tang, Jolanta Sztuk-Dambietz, Thomas Michelat, Torsten Laurus, Malcolm McMahon, Mark Robertson, Rachel Husband, Efim Kolesnikov, Sebastien Merkel, Silvia Boccato, Carsten Baehtz, Guillaume Morard, Emma S. Bullock, Cornelius Strohm, Zuzana Konopkova, Ryan Stewart McWilliams
Computational Physics
Learning noisy phase transition dynamics from stochastic partial differential equations
Luning Sun, Van Hai Nguyen, Shusen Liu et al. (+2 more)
The non-
equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but actively control kinetic pathways, including rare barrier-crossing events such as nucleation that are entirely inaccessible to deterministic models. Machine-learning surrogates for such systems must therefore represent stochasticity explicitly, enforce conservation laws by construction, and expose physically interpretable structure. We develop physics-aware surrogate models for the
stochastic Cahn-Hilliard equation in 3D that satisfy all three requirements simultaneously. The key innovation is to parameterize the surrogate at the level of inter-
cell fluxes, decomposing each flux into a deterministic mobility-weighted chemical-potential gradient and a learnable noise amplitude. This design guarantees exact mass conservation at every step and adds physical fluctuations to inter-
cell mass transport. A learnable free energy functional provides thermodynamic interpretability, validated by independent recovery of the bulk double-well landscape, interfacial excess energy, and
curvature-independent interfacial tension. Tests demonstrate accurate reproduction of ensemble statistics and noise-accelerated coarsening, with generalization to spatial domains 64 times larger in volume and temporal horizons 160x longer than those seen during training. Critically, the
stochastic surrogate captures thermally activated nucleation in the metastable regime, a qualitative capability that no deterministic surrogate can provide regardless of training, thus establishing flux-level stochasticity as an architectural necessity rather than an optional enhancement.
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All authors Luning Sun, Van Hai Nguyen, Shusen Liu, John Klepeis, Fei Zhou
Biological Physics · Cross-listed from q-bio.NC
Markovian dephasing has no entanglement-breaking threshold, and a fixed tolerance will report one anyway
Hikaru Wakaura and Taiki Tanimae
When modelling decoherence in a biological
spin system it is tempting to seek a critical rate beyond which the channel is
entanglement-breaking (EB) and
quantum resources are gone. We show that for the two channel families in which such a model would be posed, no critical rate exists. For uniform dephasing at rate $\gamma$ the partial transpose of the Choi state has
eigenvalue $-e^{-\gamma}/d$, so the channel is non-PPT – hence not EB – at every finite $\gamma$. Adding amplitude damping changes nothing: the
qubit map's partial transpose stays negative at all finite rates. What does vanish at a finite rate is the coherent information, exactly where the
qubit map turns antidegradable: the root of $c^{2}=p$, $\gamma_{\Ic}=0.668$ at $\kappa=0.1$. Antidegradability survives tensor products, so the single-letter and regularised thresholds coincide: of the three properties usually conflated here, two share one finite boundary and the third has none. Our main object is the numerical mechanism that manufactures a threshold where there is none. A quantity that decays exponentially to zero without reaching it, tested against a fixed absolute cutoff, yields a "threshold" set by the cutoff: the PPT test gives $\gamma=5.49$ at $\epsilon=10^{-10}$ and $6.58$ at $10^{-12}$, sliding by $0.55$ per decade. We reported the first number in three now-retracted preprints. Preparing this paper we made the same error three more times – most seriously in $\gamma_{\Ic}$ itself, which a bisection against a $10^{-7}$ cutoff placed at $0.62$ – and we document all four instances, with the closed forms and code that avoid them.
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Atmospheric and Oceanic Physics · Cross-listed from cs.CV
PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO$_2$ and SO$_2$ Using Satellite-Ground Data Fusion
Prasanjit Dey, Soumyabrata Dev and Bianca Schoen-Phelan
Accurate assessment of atmospheric nitrogen dioxide (NO$_2$) and sulfur dioxide (SO$_2$) is essential for understanding
climate-air quality interactions, supporting environmental policy, and protecting public health. Traditional monitoring approaches face limitations: satellite observations provide broad spatial coverage but suffer from data gaps, while ground-based sensors offer high temporal resolution but limited spatial extent. To address these challenges, we propose PollutionNet, a
Vision Transformer-based framework that integrates Sentinel-5P TROPOMI vertical column density (VCD) data with ground-level observations. By leveraging self-
attention mechanisms, PollutionNet captures complex spatiotemporal dependencies that are often missed by conventional CNN and RNN models. Applied to Ireland (2020-2021), our case study demonstrates that PollutionNet achieves state-of-the-art performance (RMSE: 6.89 $\mu$g/m$^3$ for NO$_2$, 4.49 $\mu$g/m$^3$ for SO$_2$), reducing prediction errors by up to 14% compared to baseline models. Beyond accuracy gains, PollutionNet provides a scalable and data-efficient tool for applied climatology, enabling robust pollution assessments in regions with sparse monitoring networks. These results highlight the potential of advanced machine learning approaches to enhance
climate-related air quality research, inform environmental management, and support sustainable policy decisions.
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General Physics
Effects of Schwarzschild's Black Hole Singularities on Complex Scalar Field
Z.E. Musielak, J.L. Fry and G.W. Kanan
Complex scalar fields described by a novel Klein-Gordon equation derived from gauge and group theories are considered at the Schwarzschild's black hole singularities. It is shown that the field is well-behaved in the vicinity of these singularities and that its value reaches zero at both singularities. The obtained results also demonstrate that the field forms a scalar hair that exists outside of the event horizon, and that the interior field is tachyonic and undergoes a tachyonic condensation to reach its true
vacuum at the central singularity. The described field's behavior is very different from that predicted by the Klein-Gordon equation minimally coupled to
gravity. Physical implications of these results for the interior structure of black holes are discussed.
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Computational Physics · Cross-listed from math.NA
Diffusion models with physics-guided inference for solving partial differential equations
Yi Bing, Liu Jia, Fu Jinyang et al. (+1 more)
Diffusion models have recently emerged as powerful
stochastic frameworks for high-dimensional inference and generation. However, existing applications to partial differential equations (PDEs) predominantly rely on physics-informed training strategies, which tightly couple learning with specific governing equations and limit generalization across problem settings. In this work, we propose a
diffusion model with physics-guided inference for solving PDEs, in which the
diffusion model is trained using standard data-driven procedures, while physical laws are incorporated exclusively during the reverse inference stage. The reverse
diffusion dynamics is guided by a PDE residual energy function, combined with Gaussian smoothing and explicit boundary enforcement, yielding a physically consistent
stochastic iteration that is independent of the training process. From a numerical standpoint, the proposed framework can be interpreted as a
diffusion-inspired implicit solver that converges to the PDE solution even when initialized from random noise and perturbed by
stochastic fluctuations. The method is validated on classical PDE equation such as Poisson,
Diffusion, and Burgers equations with varying coefficients. Numerical results demonstrate robust convergence, high accuracy, and strong generalization without retraining, highlighting the proposed framework as a unified alternative to classical numerical solvers and physics-informed neural networks.
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All authors Yi Bing, Liu Jia, Fu Jinyang, Peng Xiang
Fluid Dynamics
Predictor-Driven Diffusion for Spatiotemporal Generation
Yuki Yasuda and Tobias Bischoff
Multiscale spatial structure complicates temporal prediction because small-scale spatial fluctuations influence large-scale
evolution, yet resolving all scales is often intractable. Standard
diffusion models do not address this problem effectively since they apply uniform decay across all
Fourier modes. We propose Predictor-Driven
Diffusion, a framework that combines renormalization-group-based spatial coarse-graining with a path-integral formulation of temporal dynamics. The forward process applies scale-dependent
Laplacian damping together with additive noise, producing a hierarchy of coarse-grained fields indexed by
diffusion scale ${\lambda}$. Training minimizes the Kullback-Leibler divergence between data-induced and predictor-induced path densities, leading to a simple regression loss on temporal derivatives. The resulting predictor captures how eliminated small-scale components statistically influence large-scale
evolution. A key insight is that the same predictor provides a path score for reverse-${\lambda}$ sampling, unifying simulation, unconditional generation, and super-resolution in a single framework. Our unified approach is validated through experiments on two multiscale turbulent systems.
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Optics
Zeroth-order-free holographic reconstruction with a nanoimprinted nonlocal metasurface
Teruyoshi Nobukawa, Shunsuke Murai, Ryo Higashida et al. (+4 more)
The undesired zeroth-order diffraction (ZOD) arising from imperfections in diffractive optical elements (DOEs) degrades the quality of target optical wavefronts. Herein, we propose a zeroth-order-free
holographic reconstruction method using a nanoimprinted nonlocal metasurface. By judiciously designing the metasurface structure and its angular selectivity based on guided mode resonance, the ZOD can be suppressed without relying on a bulky, conventional 4f setup. We designed and fabricated a nanoimprinted nonlocal metasurface using a high-refractive-index TiO2-composite resin. Using the metasurface, we demonstrated ZOD suppression in a surface-relief DOE and a spatial light modulator. Furthermore, we prototyped a 20-mm-square metasurface and verified its effectiveness in suppressing the ZOD in 3D
holographic projection.
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All authors Teruyoshi Nobukawa, Shunsuke Murai, Ryo Higashida, Yuta Yamaguchi, Masato Miura, Koichi Okamoto, Nobuhiko Funabashi
History and Philosophy of Physics · Cross-listed from quant-ph
On measurement, superdeterminism, free will, and contextuality
Mordecai Waegell
Superdeterminism has received recent
attention as a possible path toward a locally causal explanation of the
entanglement correlations that appear in experimental tests of Bell's
theorem. While the term `superdeterminism' was coined by Bell to refer to restrictions on the free will of experimenters, it was not rigorously defined until recently. It has now been defined as a property of any physical theory that produces systematic violations of statistical independence. Here we focus on formalizing the requirements that being nonsuperdeterministic places on a physical theory, and setting a standard that must be met before we can conclude that a given theory is not superdeterministic. We begin by carefully examining how a physical theory determines what outcomes we observe when performing measurements in terms of ontic states and response functions, and how this differs between superdeterministic and nonsuperdeterministic theories, in terms of the behavior of the types of vetted random sampling procedures that we use in experiments. The core result is that individual samples and measurement outcomes must be representative of the observed distributions, which is explained in detail. This also has a bearing on how measurement settings are chosen by
agents, whether freely or randomly, and we argue that this standard ultimately defines what freedom/independence actually mean. We then discuss contextuality, and show that in most cases, superdeterminism is contextual. Finally, we discuss how different physical theories, with different notions of ontic states and response functions, can give rise to the same empirical data, and how the same operational contextuality may appear in different forms.
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Geophysics · Cross-listed from cs.LG
SAGE: Subsurface AI-driven Geostatistical Extraction with proxy posterior
Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco et al. (+2 more)
Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. However, these approaches predominantly rely on the availability of large-scale
datasets of high-quality, geologically realistic subsurface velocity models, which are often difficult to obtain in practice. We introduce SAGE, a novel framework for statistically consistent proxy velocity generation from incomplete observations, specifically sparse well logs and migrated seismic images. During training, SAGE learns a proxy posterior over velocity models conditioned on both modalities (wells and seismic); at inference, it produces full-resolution velocity fields conditioned solely on migrated images, with well information implicitly encoded in the learned distribution. This enables the generation of geologically plausible and statistically accurate velocity realizations. We validate SAGE on both synthetic and field
datasets, demonstrating its ability to capture complex subsurface variability under limited observational constraints. Furthermore, samples drawn from the learned proxy distribution can be leveraged to train downstream networks, supporting inversion workflows. Overall, SAGE provides a scalable and data-efficient pathway toward learning geological proxy posterior for seismic imaging and inversion. Repo link: https://github.com/slimgroup/SAGE.
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All authors Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco, Thales Souza, Felix J. Herrmann
Plasma Physics
Generalized multi-dimensional conservation laws for stimulated Raman and Brillouin scattering in a density gradient
Vijay Patel, Sarah Chase, Frank S. Tsung et al. (+3 more)
Generalized local and multi-dimensional conservation laws of action, energy, momentum, and angular momentum are derived for stimulated Raman (SRS) and Brillouin backscattering (SBS) in a density gradient within the paraxial ray approximation. A Lagrangian density is found that reproduces the well known envelope equations for SRS and SBS in density gradients in the absence of damping. Using Noether's
theorem, the
symmetries of the Lagrangian density are used to obtain local conservation laws for quantities that can easily be identified as the action, energy, and momentum. These multi-dimensional conservation laws reduce to the well known one dimensional Manley-Rowe relations, and frequency and wavenumber matching conditions. Additional
symmetries of the action lead to conversation laws for new quantities that are identified as orbital angular momentum and contributions to the energy and momentum of the wave from frequency and wavenumber shifts.
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All authors Vijay Patel, Sarah Chase, Frank S. Tsung, John P. Palastro, Denise E. Hinkel, Warren B. Mori
Computational Physics
Geometry-informed neural atlas for boundary value problems of complex 3D geometries
WaiChing Sun
When three-dimensional bodies contain thin features, non-trivial
topology, or scan-derived surfaces, volumetric meshing can become the dominant bottleneck in simulation workflows. We replace this step with a learned
geometric representation: overlapping volumetric coordinate charts, each equipped with a neural decoder and Jacobian, trained from point-cloud or level-set data to form a differentiable atlas. Governing equations are pulled back to chart-local reference coordinates via the Piola identity, and local solutions are coupled through multiplicative Schwarz iterations on the overlap graph. Because the atlas is constructed independently of the downstream discretization, one frozen
geometric substrate can support fundamentally different solvers (for example, a meshfree physics-informed neural network and a conventional finite-element method) without re-meshing or re-parametrization.
Benchmark and verification studies show that the learned atlas preserves expected finite-element convergence behavior and enables both forward and inverse analyses on geometries that would otherwise require solver-specific volumetric meshing.
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Chemical Physics
Optical frequency comb Fourier transform spectroscopy of the CH$_2$$^{79}$Br$^{81}$Br, CH$_2$$^{79}$Br$_2$, and CH$_2$$^{81}$Br$_2$ isotopologues in the 1180-1210 cm$^{-1}$ region
Ibrahim Sadiek, Aleksandr A. Balashov, Adrian Hjältén et al. (+3 more)
Quantitative spectroscopic detection of dibromomethane, CH$_2$Br$_2$, for environmental monitoring, workplace safety, and exoplanetary studies is limited by the lack of accurate absorption cross-section data and rigorous spectroscopic models. We report the first high-resolution (6.3 MHz point spacing) absorption cross-section of CH$_2$Br$_2$ in the 1180-1210 cm$^{-1}$ region measured using optical frequency comb
Fourier transform spectroscopy. This region is dominated by the strong CH$_2$ wagging ($\nu$$_8$) fundamental vibration, which is about 50 times stronger than the fundamental C-H stretch around 3077 cm$^{-1}$. The measurements resolve isotopologue-specific rovibrational features of CH$_2$$^{79}$Br$^{81}$Br, CH$_2$$^{79}$Br$_2$, and CH$_2$$^{81}$Br$_2$, and we assign rovibrational transitions of the $\nu$$_8$ fundamental and the overlapping $\nu$$_4$+$\nu$$_8$-$\nu$$_4$ hot bands using two methods. First, an empirical non-linear least square fit implemented in PGOPHER provides high-precision line assignment and spectroscopic constants, including accurate band origins, rotational constants, and quartic centrifugal distortion parameters, for the three isotopologues, covering rotational levels up to K$_a$ = 25 and J = 144, with an average RMS residual of 0.00037 cm$^{-1}$ (11.1 MHz). Compared with previously reported band parameters retrieved from a fit to narrowband (1.78 cm$^{-1}$) supersonically cooled spectra (B. E. Brumfield et al., J. Mol. Spectrosc., 2011, 266, 57-62), our fit provides much improved global agreement between measured and simulated spectra. In parallel, an ab initio-based effective
Hamiltonian approach was used to model the complete rovibrational polyads, including weak hot-band transitions and polyad interactions inaccessible to purely empirical fits, and provided the first ab initio-based line intensities of CH$_2$Br$_2$ in the 8 $\mu$m spectral region.
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All authors Ibrahim Sadiek, Aleksandr A. Balashov, Adrian Hjältén, Michael Rey, Oleg Egorov, Aleksandra Foltynowicz
Fluid Dynamics
Distinct transverse-response signatures of retained-spin, eliminated-spin, and polynomial Burnett-type surrogate closures
Satori Tsuzuki
High-
curvature observables in incompressible flows, including $k^4$-weighted spectra, can arise from explicit internal rotation, elimination of a fast
spin variable, or
polynomial higher-gradient closure. Building on a retained-
spin micropolar closure derived separately from the Boltzmann–Curtiss equation, we show that these mechanisms are dynamically distinguishable in transverse linear response. In a fast-
spin regime the retained-
spin theory reduces to a one-field model with a rational $k$-dependent kernel whose low-$k$ expansion generates $k^4$ and $k^6$ terms, while preserving the large-$k$ roll-off of the eliminated degree of freedom. We compare four closures: incompressible Navier–Stokes, a
polynomial Burnett-type surrogate, the explicit-
spin micropolar theory, and the eliminated-
spin rational-kernel theory. The explicit-
spin theory has two poles, the eliminated-
spin theory retains only the slow pole, and finite
polynomial truncations fail qualitatively: a strict $k^4$ truncation becomes over-damped, while a matched $k^6$ truncation develops near-critical amplification and finite-$k$ instability. Many-particle event-driven simulations of perfectly rough spheres show that these observables are measurable and, in targeted campaigns, discriminating at the microscopic level: fixed-$k$ and multi-$k$ harmonic forcing resolve a finite
spin-to-vorticity phase lag that strongly favors retained-
spin dynamics over instantaneous adiabatic elimination, while the stronger-drive multi-$k$ vorticity response rejects a pure $k^2$ closure and favors the rational eliminated-
spin kernel over a
polynomial surrogate. Transverse response thus provides a practical diagnostic for separating retained rotational microphysics, eliminated-
spin effective dynamics, and ordinary
polynomial higher-gradient closures.
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Instrumentation and Detectors
Design and performance of a large-area scintillator-based chamber for the MID subsystem of ALICE 3
Ruben Alfaro Molina, Juan Carlos Cabanillas Noris, Edmundo García Solis et al. (+14 more)
This paper reports on the design and construction of a chamber for the
muon identifier detector (MID) of the ALICE 3 upgrade project. The chamber consists of two sensitive layers separated by a 1 cm air gap. Each layer holds 24 scintillator bars ($1\times4\times100$ cm$^3$) manufactured by FNAL-NICADD. The bars are equipped with Kuraray wavelength shifting fibers and the readout is provided by a silicon photomultiplier from Hamamatsu. The bars in the second layer are orthogonal to the bars in the first layer, thus providing an overlapping
cell size of 4$\times$4 cm$^{2}$. The bar assembly as well as the design of the mechanical structure is described. The design of the chamber is close to that considered in the ALICE 3 letter of intent. The chamber was tested at the CERN T10 beamline using 3 GeV/$c$ pion-enriched and
muon beams. The chamber was placed behind an iron absorber, with different absorber lengths considered in the test. The
muon identification is performed using a Machine Learning
algorithm, which was trained and tested using
muon (signal) and pion (background) data (50% of the available statistics). The trained ML
algorithm was applied to
muon data, yielding a
muon efficiency above 99% for the OR condition (detection in either layer 1 or 2). The implementation in the pion-beam data gives the fake-
muon efficiency as a function of the absorber length that is well described by an exponential function with a slope parameter of 18.79 cm. The next steps towards finalizing the optimization are outlined.
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All authors Ruben Alfaro Molina, Juan Carlos Cabanillas Noris, Edmundo García Solis, Laura Helena González Trueba, Varlen Grabski, Gerardo Herrera Corral, Jesús Eduardo Muñoz Méndez, Ildefonso León Monzón, Antonio Ortiz, Antonio Paz, Ian Pérez García, Ricardo Rodríguez Pineda, Solangel Rojas Torres, Guillermo Tejeda Muñoz, Paola Vargas Torres, Victor Vázquez Campos, Yael Antonio Vásquez Beltran
Optics
Bent optical waveguide finite element analysis with a 3D envelope Maxwell model
Jaime Mora-Paz, Stefan Henneking, Leszek Demkowicz et al. (+1 more)
With the goal of accurately extracting the optical field losses in a three-dimensional (3D), circularly coiled waveguide (e.g., bent optical fiber), this effort presents the numerical methodologies that are implemented for an envelope Maxwell model that propagates electromagnetic fields as an entirely boundary value problem. Our unique modeling approach includes an ultraweak variational formulation of the envelope Maxwell model in the curved geometry of the bending, which is discretized by the discontinuous Petrov-Galerkin (DPG) method, which permits residual-driven mesh and
polynomial-order adaptivity. This also, then, requires a unique approach for constructing perfectly matched layers (PMLs) as absorbing boundary conditions in both the direction of optical field propagation and in the tangential directions, where unguided energy escapes the waveguide. Our coiled waveguide modeling technology extracts the mode confinement losses from the propagation of the coherent optical field through the bent waveguide. We verify our simulations against the semi-analytical results from the analogous bent slab waveguide problem, and we successfully demonstrate stable convergence to loss values for the 3D coiled optical fiber problem, which has never been done previously for our specific modeling approach.
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All authors Jaime Mora-Paz, Stefan Henneking, Leszek Demkowicz, Jacob Grosek
Computational Physics
Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
Ken Deng, Xiangfei Wang, Guijing Duan et al. (+7 more)
While large
language models (LLMs) promise to revolutionize automated scientific discovery, their application in rigorous real-world physical research is stalled by two critical barriers: a lack of realistic evaluation
benchmarks and systemic LLM
hallucinations. Here, we address both problems. We introduce QMP-Bench, a pioneering end-to-end research-level
benchmark in
quantum many-body simulation consisting of $100$ tasks extracted from $21$ high-impact prestigious journals, presenting a challenge even for current frontier LLMs. To establish a paradigm for reliable and transparent AI physicists, we present PhysVEC, a multi-
agent framework that enforces self-verifiable and error correction in AI research. PhysVEC seamlessly integrates programming and scientific verifiers to guarantee coding correctness and principle-based physical validity, yielding interpretable evidence and error correction at each step. PhysVEC significantly outperforms existing LLM baselines on various scenarios in QMP-Bench and presents a favorable inference-time scaling, successfully transforming unreliable AI generations into accurate physical reproductions, paving a robust and trustworthy path towards future automated scientific discovery.
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All authors Ken Deng, Xiangfei Wang, Guijing Duan, Chen Mo, Junkun Huang, Runqing Zhang, Ling Qian, Zhiguo Huang, Jize Han, Di Luo
History and Philosophy of Physics · Cross-listed from hep-th
The Observer Paradigm in Semiclassical Gravity: A Postmodern Perspective
Sean McBride
Recent advances in semiclassical
gravity, both in our understanding of the gravitational path integral and the algebraic structure of a gravitating subregion, rely on the presence of an observer to obtain a nontrivial
Hilbert space for closed universe backgrounds. Here I examine this proposal from a postmodern lens, identifying attempts to define “observer rules” as manifestations of metanarrative breakdown: the observer both supplies and undermines the perturbative gravitational
Hilbert space. Rather than resolving this tension, I advocate for a post-postmodern acceptance of the incompatibility between observer-dependent and observer-free descriptions of closed universes, treating the ambiguity as a feature of
quantum gravity's fundamental degrees of freedom. To my knowledge, this is the first reference to post-postmodernism in concert with physics.
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Plasma Physics
An explicit multiscale pseudo orbit-averaging time integration algorithm
Maxwell H. Rosen, Manaure Francisquez and Gregory W. Hammett
We present an explicit multiscale
algorithm for solving differential equations for problems with high-frequency modes that can be averaged over by separating and scaling the fast and slow dynamics within a single equation. We introduce a phased time integrator for cases where the boundaries of dynamical scales are known: one phase solves the unmodified equation, while the other freezes part of phase-space and slows down the
evolution of the fast dynamics. This
algorithm is applied to reduced kinetic models of
plasmas in
magnetic mirrors, which feature a distinct boundary between a region dominated by rapid particle transit and a region characterized by slow collisions. Two representative model problems are presented that decompose the dynamics of the
magnetic mirror into a simpler, computationally inexpensive form. The model problems demonstrate a speedup by a factor of order $\omega / \nu_c$, where $\omega$ is the fast oscillation frequency and $\nu_c$ is the slow damping rate. This is a 30,000$\times$ speedup for a case of practical interest.
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Optics
Observation of Floquet erratic non-Hermitian skin effect in photonic mesh lattice
Yeyang Sun, Shu Yang and Yi Yang
In ordered, translationally invariant non-Hermitian systems, the skin effect is understood as a boundary phenomenon: nonreciprocal hopping drives an extensive accumulation of eigenstates towards the edges, whereas the periodic-boundary spectrum remains Bloch extended. Here we experimentally reveal the opposite limit – a disorder-enabled, boundary-independent, and intrinsically bulk form of skin localization – the recently predicted erratic non-Hermitian skin effect (ENHSE), realized in a driven photonic platform. Using a time-multiplexed photonic mesh
lattice with programmable gain, loss, and phase modulation, we engineer spatially fluctuating imaginary gauge fields and realize a Floquet non-Hermitian
lattice whose global reciprocity can be tuned independently of strong local nonreciprocity. We observe a disorder-driven non-Hermitian topological transition between two oppositely directed disordered skin phases through a critical point of global reciprocity. At this transition, boundary skin accumulation disappears, yet the wave dynamics self-organizes into bulk-localized patterns without any interface, providing direct evidence of ENHSE. The measured localization profiles agree with simulations and exhibit the defining feature that distinct eigenstates share a common bulk-localized envelope determined by the disordered imaginary gauge fields. By further introducing controllable on-site disorder, we reveal the competition between ENHSE and Anderson localization, and show how increasing scattering progressively suppresses erratic skin dynamics. Our results help establish ENHSE as a unique disorder-induced non-Hermitian phenomenon and open a route to engineering localization, transport, and
topology beyond conventional Bloch and boundary-based paradigms.
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Atmospheric and Oceanic Physics
Deep-Learned Observation Operators for Artificial Intelligence Weather Forecasting Models
Kelsey Lieberman, Laura Slivinski, Matt Bender et al. (+6 more)
Satellite observation operators play an essential role in atmospheric data assimilation by translating model state variables into observation space. Previous work has shown that deep-learned emulators can effectively predict the outputs of classic observation operators, like the Community Radiative Transfer Model (CRTM), with reduced inference time. This study expands previous work to show the potential for integrating observation operators into artificial intelligence (AI) weather
forecasting models. Specifically, this study shows that (1) deep-learned models can effectively predict the innovations (or differences between the simulated and observed radiances) used by data assimilation models and (2) deep-learned observation models suffer only minor degradations in performance when the model state is represented with fewer vertical levels, as is commonly used by AI
forecasting models. Experiments were performed using the Unified Forecast System (UFS) replay
dataset, including Gridpoint Statistical Interpolation (GSI) observational data for the Advanced Technology Microwave Sounder (ATMS) sensor from 2022 and 2023. Code is available at https://github.com/mitre/deep-obs.
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All authors Kelsey Lieberman, Laura Slivinski, Matt Bender, Chris Miller, Josh DaRosa, Nick Krall, Mohammad Ridhwaan Alam, Nick Silverman, Sergey Frolov
Physics and Society
Urban scaling and the dynamics of individual cities
Ulysse Marquis and Marc Barthelemy
Cities are often compared through scaling laws, usually expressed as power-law relations between population size and aggregate urban quantities related to infrastructure, socioeconomic activity, or environmental impacts. These laws are influential because their exponent is often interpreted as describing what happens when a city grows, with implications for urban theory, planning, and policy. Here, we show that this interpretation is generally misleading. An exponent measured by comparing many cities at one date does not, in general, describe the trajectory of any individual city. Instead, it reflects a statistical pattern produced by cities with different histories, constraints, institutions, and growth paths. Apparent sublinear or superlinear scaling can therefore arise even when individual cities follow simpler dynamics, as we show for the area–population relation. Cross-sectional scaling laws can reveal system-level regularities, but should not be used alone to infer growth mechanisms or guide policy for a given city.
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Computational Physics
Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints
Yucheng Zhang, Chayanon Wichitrnithed, Shukai Cai et al. (+3 more)
Godunov-type methods, which obtain numerical fluxes through local Riemann problems at
cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics. Exact Riemann solvers faithfully solve the underlying equations, but can be computationally expensive due to the iterative root-finding procedures they often require. Consequently, most practical computations rely on classical approximate Riemann solvers, such as Rusanov and Roe, which trade accuracy for computational speed. Neural networks have recently shown promise as an alternative for approximating exact Riemann solvers, but most existing approaches are data-driven or impose weak constraints. This may result in problems with maintaining balanced states,
symmetry breaking, and conservation errors when integrated into a Godunov-type scheme. To address these issues, we propose a hard-constrained neural Riemann solver (HCNRS) and enforce five constraints: positivity, consistency, mirror
symmetry, Galilean invariance, and scaling invariance. Numerical experiments are carried out for the shallow water and ideal-gas Euler equations on standard
benchmark problems. In the absence of hard constraints, violations of the well-balanced property, mass conservation, and
symmetry are observed. Notably, in the Euler implosion problem, the exact Riemann solver with MUSCL-Hancock captures the jet structure well, whereas the Rusanov flux is too diffusive and smears it out. HCNRS accurately reproduces the solution obtained by the exact Riemann solver. In contrast, an unconstrained neural formulation lacks mirror
symmetry, which makes the solution depend on the choice of flux normal direction. As a result, the jet is either shifted or lost, along with diagonal
symmetry.
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All authors Yucheng Zhang, Chayanon Wichitrnithed, Shukai Cai, Sourav Dutta, Kyle Mandli, Clint Dawson
Medical Physics
Renal Blood Flow Quantification During Standard Myocardial Perfusion Imaging with Rubidium-82 Positron Emission Tomography
Hamid Abdollahi, Robert Doot, Raul Porto et al. (+6 more)
Background: Renal blood flow (RBF) is an important marker of kidney health, but noninvasive assessment is not routinely used in clinical imaging. We evaluated the feasibility and physiologic validity of quantifying renal transport of Rubidium-82 (K1) during standard myocardial perfusion imaging (MPI) PET. Methods: We studied 126 patients (age 60 +/- 12 years; 48% male; 51% Black) undergoing clinically indicated rest and stress Rb-82 MPI, in whom at least one kidney was partially visualized within the axial field of view. Volumes of interest were drawn over the visible renal cortex. K1 was estimated using a one-tissue compartment model with arterial input functions (AIF) derived from either the left ventricle (LV) or abdominal aorta. Results: LV-derived AIF produced physiologic and internally consistent flow estimates, whereas aorta-derived AIF systematically overestimated K1 and flow. LV-based measurements were therefore used for all analyses. K1 demonstrated nonlinear flow dependence consistent with the Renkin-Crone extraction model, plateauing at higher perfusion states. Renal K1 and flow declined progressively with worsening kidney function, from 1.24 +/- 0.35 ml/min/g (eGFR >= 60) to 0.53 +/- 0.22 ml/min/g (eGFR < 15; P < 0.0001). Only patients with preserved eGFR showed significant hyperemic augmentation. ROC analysis demonstrated excellent discrimination for reduced kidney function (AUC > 0.90). Conclusion: Opportunistic renal K1 quantification during routine Rb-82 PET is feasible, physiologically consistent, and strongly associated with kidney function.
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All authors Hamid Abdollahi, Robert Doot, Raul Porto, Anthony Young, Abdel K Tahari, Raymond Townsend, Jacob Dubroff, Arman Rahmim, Paco E. Bravo
Applied Physics
Enhanced nanocomposite susceptibility by field-alignment of superparamagnetic particles
Mathias Zambach, Miriam Varón, Thomas Veile et al. (+7 more)
Nanocomposites comprised of insulated
magnetic single-domain particles are promising candidates for high-frequency, eddy current free, soft
magnetic materials, but tend to suffer from low
magnetic susceptibility ($<20$). Particle
alignment has been proposed to increase nanocomposite susceptibility and reduce
magnetic losses but experimental verification has been lacking. Here,
magnetic nanocomposites containing 3-57 vol% field-aligned 11$\pm$3 nm maghemite particles in a poly-vinyl
matrix were investigated for potential use as high-frequency inductor core materials. The particles were aligned by a homogenous static
alignment field during nanocomposite drying, fixating the particle orientation. Particle aggregation was disproved by small-angle scattering. The dependence of the
alignment field strength and particle concentration on the nanocomposite's susceptibility and hysteresis losses were investigated from DC up to 922 kHz by vibrating sample magnetometry, AC-susceptibility and high-frequency hysteresis measurements. Nanocomposite susceptibility increased super-linearly with particle fraction due to weak particle interactions.
Alignment of the particles increased the nanocomposite susceptibility from 21 to 50 for samples with a particle content of 57 vol%. Hence, the synergy between particle
alignment and interaction allows for a higher than expected susceptibility of nanocomposites. The results show that magnetically aligning particles in a nanocomposite reduces
magnetic losses when using well-dispersed single-domain superparamagnetic nanoparticles. Measured nanocomposite susceptibility could be modelled by a combination of directional dependent Debye-models including mean-field interaction effects and partial particle
alignment. Measured susceptibility of 50 is among the highest obtained for nanocomposites, making it a relevant candidate for applications in power electronics.
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All authors Mathias Zambach, Miriam Varón, Thomas Veile, Matti Knaapila, László Almásy, Tomás S. Plivelic, Christer Johansson, Ziwei Ouyang, Marco Beleggia, Cathrine Frandsen