Fluid Dynamics
A Scalable OpenLB LAMMPS Framework for Fully Resolved Simulations of Hindered Settling of Arbitrary Non-Spherical Particles
Varghese Babu, Adrian Kummerländer, Mathias J. Krause et al. (+2 more)
Hindered settling of non spherical particles remains significantly less understood than spherical particles due to the computational challenges in resolving the complex contact mechanics and hydrodynamic interactions. In this paper, we present a scaleable fluid structure interaction (FSI) framework coupling the open-source LBM software in OpenLB with the Discrete Element Method (DEM) implemented in LAMMPS to simulate arbitrary shaped rigid bodies in a fluid. Particle contacts are captured using a multi-sphere "clump" representation in DEM, while particle geometries are resolved on the fluid grid via voxelization. We validate our model against single and multi-particle
benchmarks, and proceed to study the hindered setttling of cubes in systems containing upto 100,000 cubes. Our simulations show distinct differences between the settling of cubes and sphere, as cubes form pronounced coordination shells without face-parallel contact, in contrast to the contact-dominated clustering observed in spheres. Our results find that correlation length in velocity fluctuations scale with system size even for the largest system studied. These findings highlight the role of particle morphology in suspension dynamics and demonstrate a robust framework for simulating large-scale geotechnical and particulate flows.
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All authors Varghese Babu, Adrian Kummerländer, Mathias J. Krause, Santosh Ansumali, Dilip Gersappe
Optics · Cross-listed from quant-ph
A heterogeneously integrated coupled-cavity frequency beam splitter
Lucas M. Cohen, Manuel H. Muñoz-Arias, Mohan Sarovar et al. (+6 more)
Frequency encoded photonic
qubits promise a scalable path towards high-dimensional
quantum information processing, but require efficient components for coherently mixing frequency modes. Coupled cavity modulators provide this functionality by using only a single driving microwave tone to couple hybridized optical supermodes. Here, we demonstrate a heterogeneously integrated thin-film lithium-niobate-on-silicon coupled-cavity modulator that realizes tunable bidirectional frequency mode transformations, including \(50/50\) beam splitting and near complete frequency swapping with \(>20~\mathrm{dB}\) pump extinction at a \(10~\mathrm{GHz}\) supermode splitting. Because the electro-optic film is bonded onto a foundry fabricated silicon photonics platform, the approach is compatible with co-integration of
photon pair sources, spectral filters, active tuning elements, and single
photon detectors. We also bond thin-film lithium tantalate onto the same coupled-cavity platform, demonstrating material flexibility for scalable integrated frequency bin
quantum photonic
circuits.
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All authors Lucas M. Cohen, Manuel H. Muñoz-Arias, Mohan Sarovar, Nicholas A. Boynton, Shawn C. Arterburn, Thomas A. Friedmann, Nils T. Otterstrom, Paul S. Davids, Michael Gehl
Plasma Physics
First Plasma Commissioning and Operational Highlights from India's First Spherical Tokamak at IPR
Kishore Mishra, Aditya Verma, N. Mansoori et al. (+73 more)
A compact Spherical Tokamak(ST) is commissioned at Institute for
Plasma Research (IPR) to explore low aspect ratio tokamak physics and technologies that complement to the existing high aspect ratio tokamaks namely ADITYA-U and SST-1 by enabling studies on non-inductive startup, current drive in over dense
plasmas, and shaped
plasma physics on a low cost platform. The device, India's first spherical tokamak has completed major mechanical,
magnetic, and electrical integration, and the coil system has been successfully tested with series of integrated commissioning. First
plasma experiments have been carried out with a modest Ohmic system assisted by a 2.45GHz microwave system, supported by a centralized control and data acquisition system. An initial diagnostic set comprising visible imaging, spectroscopy, magnetics, and radiation monitors required for machine operation has been installed. This paper presents the integrated commissioning experiences and first
plasma experiments of the newly installed machine.
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All authors Kishore Mishra, Aditya Verma, N. Mansoori, Saurabh Verma, Y. Paravastu, M. S. Khan, Arvind Kumar, S. G. Thatipamula, Vishal Verma, M Sheetal, Mohit, U. Thaker, Ayush, Jignesh Patel, Praveenlal, Jagabandhu Kumar, F. S. Pathan, S. Ranjithkumar, S. Sam, Prasada Rao P., A. Jaiswal, S. Jha, Neelam Ramaiya, Utsav Rajvanshi, Santosh Pandya, K. Tahaliyani, S. Purohit, Suraj Gupta, Kiran Patel, Suman Aich, D. Kumawat, Deepak Kumar, Saurav Kumar, Rahul Kumar, Vismaysinh Raulji, Pramila, Praveena Kumari, Minsha Shah, Arun Prakash, Narendra Bhupesh, Amit K. Singh, V. Menon, Deepti Sharma, A. Kundu, G. K. Rajan, Prakash Parmar, Ankit Kumar, Vishnu Patel, Imran Mansuri, M Bhandarkar, H. Chudasma, Atish Sharma, Kirit Patel, H Masand, Prem Kumar, Parmesh Kumar, S Kannaujiya, Pramod Parmar, Arvind L. Thakur, C. G. Virani, Ankit Kumar, K. K. Ambulkar, Vilas Chaudhari, C. Danani, Y. S. S. Srinivas, S. Nair, K. Mahajan, R. Rajpal, M. B. Chowdhury, Manoj Kumar, S. K. Pathak, P. K. Sharma, Z. Khan, E. Rajendrakumar, J. Ghosh, Team ST
Atmospheric and Oceanic Physics · Cross-listed from gr-qc
Atmospheric Newtonian noise in torsion-balance measurements of the gravitational constant $G$
Jyotirmaya Mohanta and Yutaka Shikano
Measurements of Newton's gravitational constant remain limited by environmental and apparatus-dependent systematics whose treatment is often less explicit than that of instrumental noise. Among these, atmospheric density fluctuations generate unshieldable
gravity gradients that couple directly to torsion-balance observables as atmospheric Newtonian noise. Here, we develop a GUM-consistent framework for propagating this contribution through the torque
estimator into the uncertainty budget of torsion-balance measurements. We derive closed-form spatial transfer functions for two
benchmark geometries: a two-mass dumbbell, which provides an upper-coupling reference, and a perfectly symmetric cross, which serves as an idealized rejection limit and exposes the trade-off between suppressing low-order environmental coupling and preserving signal response. We also separate stationary correlated inputs from non-stationary baseline drift, using the Ornstein–Uhlenbeck process only as a
benchmark for the former. Atmospheric pressure
benchmarks indicate that the resulting background contribution is below present reference uncertainty levels, but can become relevant as systematic floors approach the part-per-million regime. This framework provides a practical route for incorporating site-specific environmental
gravity gradients into future torsion-balance uncertainty budgets.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Watching a GaN Transistor Switch: Real-Time Nanoscale Strain and Heat Dynamics
C. Corley-Wiciak, N. T. Sammler, B. Butej et al. (+16 more)
Digital and energy technologies depend on microelectromechanical and power electronic components whose performance is critically impacted by rapid, cyclic deformations. Real-time information on their operation has remained inaccessible due to the need for nanosecond and nanometer resolution in fully integrated devices. We break this limitation by imaging the complete switching cycle of an industrial GaN high electron mobility transistor through stroboscopic dark field X ray microscopy at a fourth-generation synchrotron, resolving electromechanical and thermal micro strain fields across the entire device and correlating them with time dependent voltage characteristics. Coupled simulations benchmarked against the measurements reproduce electric field
evolution and transient thermal hotspots. This combined approach provides direct insight into device physics and informs design strategies for next generation energy and information processing technologies.
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All authors C. Corley-Wiciak, N. T. Sammler, B. Butej, M. Petersmann, A.A. Corley-Wiciak, L. Neumann, Juan M. Carrillo-Larrea, P. Schmiedeke, M. Strätgen, P. A. Douissard, S. J. Leake, P. Boesecke, M. Sievers, P. Imrich, M. Nelhiebel, M. Glavanovics, D. Pogany, M. Reisinger, T.U. Schulli
Optics
Nonconcentric Multi-shell Nanowires: Geometry-Induced Plasmon Hybridization and Near-Field Localization
Gino Wegner, Jer-Shing Huang and Kurt Busch
Localized surface plasmons (LSPs) in multi-shell nanostructures provide a versatile route for controlling optical fields at the nanoscale, yet the influence of deviations from concentric geometries remains insufficiently understood. Here, we investigate the impact of shell nonconcentricity on the quasistatic optical response of core-single-shell and core-multi-shell nanowires. Exploiting the conformal properties of bipolar coordinates, we derive analytical solutions for nonconcentric cylindrical interfaces and systematically analyze the
evolution of LSP resonances, absorption spectra, and near-field distributions. Starting from single-shell structures, we show that nonconcentricity enables finite coupling of incident radiation to higher-order plasmon modes that are optically inactive in the quasistatic concentric limit. Extending the analysis to multi-shell bull's eye wires, we identify how shell thickness, number of fixed-thickness shell units, each defining a set of a dielectric and metal shell, as well as interface nonconcentricness shape the hybridized plasmon spectrum. Increasing the number of metal-dielectric interfaces broadens the spectral response, while nonconcentric geometries additionally increase the density of accessible resonances and localize electromagnetic fields preferentially within and around the thinner shell sections. Eventually, comparison of concentric with bipolar and Doppler-grating-inspired nonconcentric bull's eye wires based on Mie theory and full-wave Discontinuous Galerkin Time-Domain simulations, respectively, allows to assess the impact of nonconcentricness for typical nanowire dimensions. These results provide insight into geometry-induced plasmon hybridization and suggest routes toward nanoscale control of optical energy localization for applications in active nanophotonics and plasmon-assisted photochemistry.
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History and Philosophy of Physics
Dark Matters of Principle: Principles in the Dark Matter Paradigm
Patrick Duerr and James Read
This paper re-examines the Dark Matter (DM) problem through the lens of physical principles. We first develop a functionalist account and taxonomy of principles. We propose to define principles in terms of their intended functions or purposes. Their argumentative force boils down to context-sensitive and often tentative reasons for believing them adequate for those purposes. In research contexts in which novel theories or models are sought which push the boundaries of knowledge into new domains, reliance on well-motivated principles as principal building blocks is a historically widespread, natural, and methodologically prudent research strategy—what we'll dub "principled inquiry". In a second step, we apply this methodological machinery to the DM problem. Through detailed analyses of four mainstream DM approaches—supersymmetric WIMPs,
axions, sterile
neutrinos, and
primordial black holes—we substantiate four claims. (C1) The DM paradigm is formed by the framework of possibilities, circumscribed by a shared cluster of regulative and constitutive principles of gravitational and non-gravitational physics. (C2) Besides those common principles, the four DM models also depart from other principles of orthodox physics in distinct ways. (C3) They do so to different degrees—displaying different forms and extents of "minimal mutilation" with respect to some of the principles of established non-gravitational physics. (C4) The guidelines of principled inquiry clarify the sense in which the DM problem constitutes an ever-more disconcerting crisis: worries are growing that the principles on which DM researchers have routinely and rationally relied may have run out of heuristic steam. Pressure is mounting to "leave no stone unturned" (Bertone and Tait 2018): scientists ought to increasingly explore ideas that stray more radically from the cautious explorations of principled inquiry.
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Plasma Physics
Machine learning methods for modelling local, linear gyrokinetic simulations of MAST-U pedestal turbulence
Anna Niemelä, Daniel Jordan, Aaro Järvinen et al. (+8 more)
Gyrokinetic (GK) stability strongly influences the performance of high-confinement-mode pedestals in spherical tokamak
plasmas. High-fidelity gyrokinetic codes such as GENE can model microinstability-driven transport, but the computational cost limits their routine use in integrated pedestal modeling workflows. Instead, present workflows often rely on reduced transport assumptions, such as the ballooning-critical pedestal model used in EPED. This work investigates machine-learning surrogate models for local linear gyrokinetic simulations in a MAST-U-relevant pedestal parameter space, with the aim of providing faster gyrokinetic-based inputs to reduced pedestal models. A sampling workflow is developed in which pedestal profile parameters are varied within experimentally motivated bounds and used to generate physically self-consistent Grad-Shafranov
equilibria. This reduces the dimensionality of the data-generation problem compared with sampling local gyrokinetic inputs directly, while maintaining physically plausible combinations of
plasma profiles, geometry, and local stability parameters. The surrogate models are trained to predict linear growth rates, real frequencies, and diffusivity-ratio transport fingerprints from local linear GENE simulations. A multi-head multilayer perceptron accurately reproduces the growth rate, while the diffusivity ratios and real frequency exhibit more clustered, regime-dependent behavior. A multi-head classification-regression model using frequency-based regime classes reduces the mean absolute error for these clustered targets and better captures sharp transitions associated with changes in the underlying instability regime, although errors near mode-transition regions remain a limitation.
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All authors Anna Niemelä, Daniel Jordan, Aaro Järvinen, Amanda Bruncrona, Adam Kit, Lorenzo Frassinetti, David Hatch, Leonhard Leppin, Samuli Saarelma, the MAST Upgrade team, EUROfusion Tokamak Exploitation team
Geophysics
Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion
Jaehong Chung, Andrew Lockwood and Jef Caers
Three-dimensional probabilistic inversion of time-domain airborne electromagnetic (AEM) data is limited by the cost of the forward solve. Even though one simulation takes only tens of seconds, a
Bayesian inversion of a survey of millions of soundings requires of order $10^{10}$ forward evaluations. To address this, we develop a continually learning neural-operator surrogate of the three-dimensional AEM forward operator that replaces the solver inside the
Bayesian inversion. We start from the point of view that regardless of what geological prior is specified, Maxwell's laws remain invariant. Secondly, we avoid the limitation of learning on a single prior by continual learning on consecutive priors, which means our surrogate becomes richer as it is applied in future case studies, either by the authors, or by the scientific community. We use a validity check built on ensemble disagreement to divert cases with measurements outside the training range to the solver. Driven by the surrogate, the identical Markov chain Monte Carlo sampler reproduces the full-solver posterior, and its credible intervals cover the truth within 2.6 percentage points. Applied to the 2013 Capricorn TEMPEST survey in Western Australia, the surrogate inverts over two million soundings in seconds, a computation infeasible for the solver. Testing the geological prior against the entire survey costs minutes. The framework delivers uncertainty-quantified conductivity imaging at survey scale, which we believe is essential to perform near real-time mineral-systems targeting with geophysics.
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Atmospheric and Oceanic Physics
A place for stabilization alongside tipping cascades: the AMOC-cryosphere system
Sacha Sinet
The Atlantic Meridional Overturning Circulation (AMOC) and cryosphere are central to tipping-point research, where their interactions are typically framed as pathways for cascading transitions. Here, I review evidence that these interactions can also oppose change, affecting its magnitude and timing or, in some cases, whether a transition occurs at all. I argue that a more complete assessment of tipping risk requires progress on three fronts: systematic
attention to stabilizing alongside destabilizing dynamics, conceptual frameworks that accommodate competing and context-dependent effects, and methods to detect, anticipate, and attribute stabilization.
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Atomic Physics · Cross-listed from quant-ph
Structured Non-Locality and Emergent Locality in Cavity-QED Many-Body Dynamics
Mark A. Oehlgrien, Błażej Jaworowski, Darrick E. Chang et al. (+1 more)
Cavity
quantum electrodynamics (QED) modifies many-body systems by combining cavity-mediated collective interactions with microscopic short-range interactions. The resulting dynamics lies between the local and fully collective limits, such that neither locality nor collectivity alone provides a complete organizing principle. In the clean conceptual limit of dominant collective coupling, we show that the effective dynamics within each energetically isolated subspace is generically controlled by whether that subspace admits a local product-state basis, and identify exceptions imposed by angular-momentum selection rules. Product-state subspaces generically retain the spatial structure of the microscopic interaction. Entangled subspaces instead generically dress local processes with global operators, generating non-local but highly structured dynamics. We illustrate this by deriving the corresponding effective
Hamiltonians in two representative cavity-QED
spin models. The cavity-isolated subspace thus becomes a resource for generating competing short-range interactions or globally conditioned local processes, opening a class of many-body dynamics without local or fully collective counterparts.
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All authors Mark A. Oehlgrien, Błażej Jaworowski, Darrick E. Chang, Charlie-Ray Mann
Optics
From rings to resonance: an inverse method links biophotonic structural color to inverse photonic glasses
Florin Hemmann, Matthias Saba, Ullrich Steiner et al. (+2 more)
Structural color arises from the interaction of light with nanoscale structures and is widespread in nature. As structural complexity increases, the mechanisms governing coloration become progressively less understood. The optical response of periodic photonic
crystals with a spatially periodic refractive index is well described by Bloch theory, whereas that of photonic glasses composed of randomly assembled uniform spheres is more subtle yet well studied. In contrast, disordered photonic networks found in many beetles are among the most complex natural photonic architectures, and the fundamental relationships between their structure and color remain unclear. Here, we use an inverse method to identify the structural features encoded in the reflectance spectrum. By comparing the spectrum of an unknown system with a database of simulated spectra from computer-generated photonic networks, we infer its structural properties. Applying this approach to both simulated networks and the biophotonic network responsible for the blue coloration of the weevil Pachyrhynchus congestus mirabilis, we identify rings and pores as the key local scattering motifs. Their characteristic sizes govern the spectral position of the reflectance peak, whereas short-range disorder controls its width. The inverse method reveals clear spectral signatures of short-range order, whereas the influence of hyperuniformity and primitive similarity appears comparatively weak. This suggests that blue structural coloration is governed by local scattering mechanisms rather than photonic band-gap effects. We propose an analogy to an inverse photonic glass, in which pores and rings act as correlated local resonators. This perspective provides new design principles for bio-inspired structural-color materials.
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All authors Florin Hemmann, Matthias Saba, Ullrich Steiner, Mauro S. Ferreira, Felipe A. Pinheiro
Optics
Room-temperature graphene sub-terahertz detector integrated on a silicon dielectric waveguide
A. Titchenko, K. Shein, M. Titova et al. (+6 more)
Terahertz (THz) photonics offers a promising platform for next-generation 6G wireless communications, on-chip spectrometers, and non-invasive biomedical sensors. In the near- and mid-infrared, monolithically integrated photodetectors are a cornerstone of photonic integrated
circuits (PICs). In the THz range, however, such integration on low-loss dielectric waveguides remains an open challenge: to date, room-temperature on-waveguide detection has been realized only through hybrid flip-chip assembly of discrete elements, which leads to poor mode coupling, larger footprint, and reduced mechanical stability. Here we address this gap by demonstrating a room-temperature
graphene sub-THz detector monolithically integrated on a high-resistivity silicon dielectric waveguide for D-band (110-170 GHz) operation. We couple an hBN-encapsulated
graphene channel to the guided mode using a tapered slot-line
antenna patterned directly onto the silicon surface. The detector achieves a voltage responsivity of 11.5 V/W and a 3 dB bandwidth of 1.94 GHz, with the latter currently limited by parasitic inductance of the readout interconnects rather than by the intrinsic
graphene response. This platform provides a practical pathway to fully integrated room-temperature THz photonic
circuits, where electrostatic gating and impedance matching can improve both responsivity and bandwidth by roughly an order of magnitude.
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All authors A. Titchenko, K. Shein, M. Titova, M. Kashchenko, O. Popova, R. Izmaylov, E.I. Titova, I. Gayduchenko, G. Goltsman
Chemical Physics · Cross-listed from cond-mat.soft
Residual semi-crystalline particles released during enzymatic degradation of plastics
Michael Schindler and Ludwik Leibler
Enzymatic recycling of plastics is limited by the presence of semi-crystalline spherulites that are recalcitrant to enzymatic depolymerization. Depending on the quality of the waste stream and its treatment history, a large volume fraction of the material actually remains in form of connected clusters of such spherulites. We build on a recently published numeric method to predict the number, the connectivity, and the morphology of these clusters as an outcome of enzymatic degradation. When applied to PET waste, our method predicts that the resulting aggregates are loosely connected, "fluffy" structures with a high surface-to-volume ratio, accompanied by smaller clusters following a continuous size distribution. By providing a quantitative framework for understanding the microparticle production during the depolymerization, these findings should assist choosing a suitable downstream treatment, such as filtering or flocculation. This work could thus help to advance the enzymatic depolymerization technologies.
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Fluid Dynamics
A Compensated Koopman Neural Operator with Selective State-Space Dynamics for Unsteady Flows
Tangying Lv, Yuanjun Dai and Zhenxu Sun
Stable prediction of unsteady flows requires accurate multiscale spatial representation and robust temporal propagation. We introduce the Compensated Koopman U-shaped Neural Operator (CoKo-UNO), which combines a U-shaped spectral backbone with Koopman-dominated latent propagation. Finite-dimensional Koopman truncation produces a state-dependent residual that is repeatedly reinjected during autoregressive rollout. CoKo-UNO models this residual with a selective state-space model (SSM), a principled input-dependent compensation mechanism, together with resolution-adaptive compensatory skip connections and an overlapping-warmup rollout strategy. \NEW{Across four
benchmark problems, CoKo-UNO achieves the lowest mean rollout error among all compared methods. Its largest gain is a $76.76\%$ reduction relative to the strongest baseline, while requiring about $41.40\%$ of RNO's training time.} These results show that explicit residual compensation improves stable autoregressive prediction of unsteady flows.
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Optics · Cross-listed from quant-ph
Surpassing Gaussian optimality in multiparameter estimation with indefinite causal order
Sudipta Das, Rivu Gupta, Aditi Sen De et al. (+1 more)
We identify single-mode Gaussian probes, generated by displacement and squeezing operations on the
vacuum state, which are optimal for the simultaneous estimation of displacement and squeezing operations in continuous-variable
quantum systems. Importantly, our results reveal that the best precision at a fixed energy is achieved not by an experimentally costly squeezing resource, but rather by redirecting some of the energy towards displacement, thus allowing for more resource-effective operations. Furthermore, introducing indefinite causal order (ICO) in either the probe preparation or parameter encoding step can surpass the Gaussian precision bound, even though the optimal Gaussian probe state is agnostic to the ordering of the operations. Specifically, we observe that odd-parity superpositions of the two definite orders can enhance precision over optimal Gaussian probes in specific parameter regimes. Further, the observed advantage cannot be attributed solely to non-Gaussianity, as quantified by the relative
entropy of non-Gaussianity, highlighting ICO as an independent resource for enhancing multiparameter estimation.
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All authors Sudipta Das, Rivu Gupta, Aditi Sen De, Himadri Shekhar Dhar
Atmospheric and Oceanic Physics · Cross-listed from cs.CV
Precipitation Downscaling Using Foundation Model-Conditioned Diffusion
Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga et al. (+7 more)
High-resolution precipitation fields are essential for hydrological impact assessment, yet global
climate model outputs are too coarse and biased for direct use. AI-based statistical downscaling with
diffusion models offers a promising approach, but the mechanism by which large-scale atmospheric predictors condition generation remains largely unexplored. We investigate three conditioning strategies for a denoising
diffusion probabilistic model applied to daily precipitation downscaling: channel concatenation of upsampled coarse predictors, cross-
attention conditioning with a learned convolutional encoder, and cross-
attention conditioning with the frozen encoder of the pretrained Prithvi WxC weather foundation model. All strategies are evaluated against an unconditioned baseline under identical conditions using probabilistic, distributional, spectral, and extreme-event metrics for the Colorado River Basin. Concatenation conditioning achieves the lowest point-wise CRPS and MSE, but tends to produce over-smoothed fields that suppress high-intensity events. In contrast, cross-
attention conditioning provides substantially better distributional realism and modest improvements in spectral fidelity. Improvements are greatest for extremes: the Prithvi-WxC conditioned model retains over half of >100mm/day events, although estimates are uncertain due to limited samples. When trained on the full
dataset, the learned convolutional model performs similarly to the foundation model-conditioned approach while requiring lower computational resources. However, the Prithvi-WxC-conditioned model achieves comparable performance with only five years of training data. These results indicate that cross-
attention conditioning offers advantages over simple concatenation for probabilistic precipitation downscaling, and that pre-trained foundation model representations may offer benefits in data-limited settings.
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All authors Victor Nascimento Ribeiro, Jorge Guevara, Jorge Sebastian Moraga, Chris Lucas, Natalie Lord, Andrew Taylor, Edward Lockhart, Will Trojak, Johannes Schmude, Anne Jones
Chemical Physics
Core-valence double ionization of SF6 involving S2p, F1s and S1s inner shells
Veronica Daver Ideböhn, Daniel M. Pereira, Lucas M. Cornetta et al. (+11 more)
Core-valence double ionization electron spectra near the S2p, F1s and S1s edges of SF6 are presented, analyzed and compared with conventional valence photoelectron spectra and
quantum chemical calculations. The core-valence spectra are energetically stretched out, revealing salient structures between 15 and 40 eV that are sufficiently separated for molecular orbital analysis. The spectra offer new insights into the electronic structure, showing that the core hole substantially rearranges the molecular orbital order. This effect can be traced to orbital localization and nodal structure, as well as to valence-to-core penetration. The singlet and triplet splittings of the dicationic states progressively decrease in all three core-valence spectra towards deeper core levels, with larger splittings for S2p than S1s, reflecting greater valence-to-core penetration and exchange interaction for S2p. By large, the MO interpretation holds in frozen or self-consistent-field representations, except for the inner parts of the F1s spectra, which require analysis in terms of MO breakdown effects. An intensity model for CV spectra is derived using an independent-channel approach, where primary core ionization is treated by dipolar coupling to a continuum and the valence electron is promoted to a second continuum through shake-off. Full
spin coupling and
spin selectivity between the discrete doubly ionized states and the two continuum electrons are maintained. The primary molecular photoelectron intensity is expressed as a weighted sum of atomic subshell cross sections using a one-center atomic orbital projection of the relevant molecular orbital. The calculated spectra agree well with experiment and allow detailed assignment. In particular,
symmetry breaking at the F1s core-ionization site is identified and discussed.
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All authors Veronica Daver Ideböhn, Daniel M. Pereira, Lucas M. Cornetta, Richard J. Squibb, Andreas Hult Roos, Emelie Olsson, Måns Wallner, Marco Parriani, Nihar Ranjan Behera, Gunnar Öhrwall, Florian Trinter, John H.D. Eland, Hans Ågren, Raimund Feifel
Atmospheric and Oceanic Physics
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun et al. (+4 more)
AI weather prediction (AIWP) models rival physics-based models, yet the sources of their unexpected forecast accuracy and the degree of their physical fidelity remain unclear. Here, across a hierarchy spanning observation-based reanalysis, a general circulation model, and the multi-scale Lorenz system, we show that AI models can be trained to skillfully predict the past (backcast), though backcasts are systematically less accurate than forecasts. However, skillful backcasting appears to violate the second law of thermodynamics, and all these
forecasting and backcasting models miss the butterfly effect. We trace the surprising forecast accuracy, missing butterfly, and skillful backcasting to a single cause: inevitable coarse-graining of training data, which removes fast, small scales and/or some variables. From the Lorenz system to official Pangu-Weather models, reducing coarse-graining makes AI predictions more physics-like (arrow of time and butterfly-like effects emerge), but forecast accuracy declines. Results offer an explanation for AIWP models' forecast skill: unlike physics-based models, they implicitly learn how fast, small scales affect large scales without inheriting their rapid error growth. Broader implications are that AI models' proliferation calls for revisiting predictability theories and long-term
climate emulation strategies, and backcasting offers a useful, new lens for such analyses.
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All authors Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun, Jiangdi Wang, Alexander Wikner, Justin Finkel, Jonathan Q. Weare
Fluid Dynamics
Fock-Space Representation of the Lebowitz–Frisch–Helfand Kinetic Model
Ilya Karlin
We develop an exact Fock-space representation of the Lebowitz–Frisch–Helfand kinetic equation. The familiar square-root Maxwellian transformation is used only as a convenient starting point: it maps the local Ornstein–Uhlenbeck relaxation sector to the bosonic number operator and thereby exposes an elementary grading. The main issue is the representation of the complete kinetic dynamics when the velocity realization depends on local macroscopic parameters. We formulate the pull-back through an arbitrary admissible realization map, show that external space–time derivatives acquire a differential connection, and prove an intertwining
theorem for first-order propagation operators. The physical velocity moments are represented by dual Fock-space functionals; a transport–moment intertwining
theorem then evaluates the complete propagation contribution without requiring the explicit differential connection. Compatibility between realization parameters and kinetic moments may be imposed algebraically or propagated dynamically by exact balance laws. In the hydrodynamic limit, the number grading selects the second and third Fock levels responsible for viscous stress and heat flux, leading directly to the Navier–Stokes–
Fourier constitutive terms. Finally, we prove covariance under local changes of coordinate realization and illustrate it explicitly for Hermite-
polynomial and Hermite-function coordinates. Thus the Hermite realization is computationally privileged, while the intertwined Fock dynamics, physical moments, and compatibility structure are representation-independent within the admissible similarity class.
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Fluid Dynamics
Vortex filament dynamics and vortex ring motion revisited
Andrew D Gilbert
The motion of a slender
vortex in ideal incompressible fluid is a classic problem in hydrodynamics. Formulae for the velocity of a
vortex ring go back to work of Kelvin, Helmholtz, Hicks and Dyson in the nineteenth century, while more recently a number of models have been created for simulating the motion of slender tubes of vorticity of general shape. For a
vortex tube or filament localised near to a curve C(t), the relevant small parameter to measure slenderness is the tube radius divided by the radius of
curvature of C. The present paper revisits this range of classic problems by introducing a coordinate system closely linked to the geometry of
vortex surfaces in a slender
vortex. The motion of fluid elements in this coordinate system has an action–angle form, and with this the vorticity equation simplifies radically. At the same time, however, most aspects of the shape or
evolution of a
vortex are thrown into the description of the coordinates, and in particular the corresponding metric and volume form. As the coordinate system is non-orthogonal and time-dependent, tools of differential geometry are most easily used to describe the structure of both vorticity and coordinate system, and a general mathematical framework is set out. This resulting system of equations is taken as far as possible with only the assumption of
vortex slenderness in place, but allowing arbitrary motions and distortions of the
vortex core and of the curve C(t). The modelling is applied to calculate the motion of a slender
vortex ring with arbitrary axial flow, solving directly for the shape of perturbed vorticity surfaces and giving results in agreement with earlier studies. The general framework set up in this paper is suitable for the development of simplified equations for
vortex motion and interaction in future studies.
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Applied Physics · Cross-listed from cs.ET
Simultaneous Digital Communication and Deformation Sensing over a Single Stretchable Interconnect
Yuji Isano and Hiroki Ota
Stretchable hybrid electronics integrate rigid solid-state electronics with stretchable materials and structures to achieve both high deformability and stable electronic performance. However, most existing systems treat stretchability only as a mechanical attribute without exploiting device deformation to encode its own mechanical state. This problem arises from adapting conventional rigid
circuit architectures to stretchable substrates, affording a loss in compatibility with the sensors required for strain measurement. This study addresses this issue by proposing a communication-integrated deformation sensing architecture for stretchable hybrid devices. In the proposed approach, standard universal asynchronous receiver-transmitter digital signals transmitted between rigid nodes are amplitude-modulated by strain-induced resistance changes in stretchable
liquid metal interconnects. By reading both amplitude changes and digital patterns, the system enables simultaneous digital communication and self-deformation sensing without requiring additional stretchable sensing elements. The architecture is demonstrated in a multi-node system and applied to wearable sensing and self-deformation mapping devices. By extending the integration of rigid
circuits and soft elements from the hardware level to the system level, this study provides a novel design paradigm for stretchable electronic systems that inherently utilize their own deformation as functional information.
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Biological Physics · Cross-listed from math.ST
Pooling Mobility Obscures Epidemic Invasion Routes
Tiandong Wang and Wei Yang
Epidemic models often pool air travel, commuting, and other mobility layers into a single weighted network. Pooling keeps the total imported infections into a region but discards the transport mode and route that delivered them, the information a mode-specific intervention needs. We make this precise for directed multilayer flows with heavy-tailed variation. Pooling acts as an asymmetric filter. The magnitude of an extreme importation, its radial tail index, is an exact invariant of sampling and nonnegative aggregation, whereas its composition across layers and routes, the angular part, is reweighted by layer-specific sampling and can be irrecoverably merged. We give a necessary and sufficient condition for recovering route composition, attach a surveillance decision cost to the loss, and show that the first established route need not be the busiest. A controlled metapopulation study calibrates the cost, and two contrasting reconstructions show where it bites. In US pandemic influenza, air adds fitted information beyond commuting, and a layer-resolved ranking targets more air-import burden than a pooled one. In the early Italian COVID-19 wave, commuting improves onset reconstruction while air attribution stays unresolved. Pooling can therefore support total-importation surveillance but cannot in general identify the mode or route behind an importation.
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Space Physics
A quiet STEVE disturbs navigation satellites' signals in the Antarctic
Daria Kotova, Luca Spogli, Yaqi Jin et al. (+1 more)
Strong Thermal Emission Velocity Enhancement (STEVE) is a narrow optical phenomenon that occurs equatorward of the auroral oval and is associated with intense subauroral
plasma flows and thermospheric heating. Although these conditions can produce
plasma irregularities, direct evidence of STEVE effects on
radio wave propagation during geomagnetically quiet conditions has so far not been observed. Here we report for the first time a STEVE event observed over Antarctica during quiet geomagnetic conditions and show that it produced measurable fluctuations in the Global
Navigation Satellite System (GNSS) signals. Using coordinated optical observations and high-resolution (50 Hz) GNSS scintillation measurements from two Antarctic stations, we identify enhanced phase and amplitude scintillation coincident with intersections between GNSS signal paths and the STEVE arc. The observations indicate the presence of
plasma irregularities and suggest substantial temporal variations in the apparent altitude of the optical structure (vertical motion within the 130-270 km range). We find that even a relatively weak STEVE event can affect the GNSS signal propagation in the absence of major geomagnetic disturbances, extending previous studies that associated such effects primarily with storms and intense auroral activity. This finding expands the understanding of STEVE's geophysical impact and highlights potential vulnerabilities in satellite-based
navigation systems during seemingly benign space weather conditions. The fact that this STEVE event occurred just off the Antarctic coastline, where scientific expeditions and seagoing vessels rely heavily on precise positioning indicates that STEVE-related
plasma structuring should be considered in assessments of the GNSS performance under otherwise quiet geomagnetic conditions in subauroral regions.
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All authors Daria Kotova, Luca Spogli, Yaqi Jin, Wojciech Miloch
Biological Physics
Where Energy Is Spent Sets the Depth of Kinetic Proofreading
Uğur Çetiner
Kinetic proofreading buys accuracy with energy. Where the energy is spent, we show, caps how much accuracy it can buy. Drive layer $j$ of a $K$-checkpoint cascade and the error never falls below $\eeq^{K-j+1}$, where $\eeq$ is the error the same network reaches at
equilibrium. The exponent is one plus the first Betti number of the downstream subgraph, which counts its independent cycles. Driving the first layer can recruit all $K$ checkpoints, whereas driving the final layer restricts the best possible scaling to $\eeq^2$, independently of network depth. We also derive the exact dependence on driving strength and show that the error either decreases monotonically or has a unique global minimum, beyond which stronger driving worsens accuracy. Drive placement therefore imposes a topological limit on nonequilibrium error correction.
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Space Physics
Empirical Relationship for Geomagnetically Induced Currents (GIC) and Solar Wind Conditions
Dean Thomas, Lucy A. Wilkerson, Robert S. Weigel et al. (+2 more)
Geomagnetically induced currents (GICs) during extreme space weather events represent a critical hazard to modern infrastructure, including electrical power grids, telecommunication systems, pipelines, and railways. This study presents empirical models designed to provide rapid, site-specific estimates of the maximum GIC, $|\text{GIC}|_{\text{max}}$, expected during a storm. Utilizing data from 67 sites across nine major geomagnetic storms ($K_p \ge 8$) occurring between 2020 and 2025, we performed multivariate linear and
quantile regressions incorporating
solar wind parameters and local scaling factors. The
solar wind parameters address storm intensity and are the minimum interplanetary
magnetic field $z$-component, $(B_{z}^{\tiny{\text{IMF}}})_{\text{min}}$, and the maximum magnitude of the
solar wind velocity $x$-component, $(V_{x})_{\text{max}}$. The scaling factors account for local differences due to geomagnetic latitude ($\alpha$) and ground conductivity ($\beta$). A 6-parameter model was identified via best subset selection and the corrected Akaike Information Criterion (AICc) as the optimal predictor for mean $|\text{GIC}|_{\text{max}}$, yielding a Pearson correlation of $r = 0.73$ and a coefficient of determination of $R^2 = 0.54$. To examine peak GICs during storms, a 7-parameter
quantile regression model was developed to estimate the upper 80% bound of $|\text{GIC}|_{\text{max}}$. Statistical analysis of 39 severe storms since 1995 shows that the $|\text{GIC}|_{\text{max}}$ upper bounds are consistent with a lognormal distribution. These relationships offer a computationally efficient alternative to complex physics-based simulations, enabling space weather practitioners to estimate site-specific GICs in high-level analyses.
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All authors Dean Thomas, Lucy A. Wilkerson, Robert S. Weigel, John Haman, Edward J. Oughton
Optics
Robust Unidirectional Edge States in the Continuum in non-Topological Floquet Photonic Crystals
Hairong Huo, Yongyou Zhang and Bingsuo Zou
Robust unidirectional edge propagation is conventionally attributed to topological protection. Whether edge states in the continuum (EICs) can exhibit such robustness in non-topological systems remains an open question. Here we demonstrate robust unidirectional EICs in Floquet photonic
crystals (PhCs) composed of a honeycomb
lattice of helical waveguides, where both time-reversal and spatial inversion
symmetries are broken. Within a topologically trivial parameter regime of this system, where the Chern, valley Chern, and winding numbers all vanish, the EIC robustness is decoupled from
topology. Instead, the robustness originates from a z-periodic Floquet artificial gauge field geometrically locked to the helical
lattice. Numerical simulations show that the EIC survives 120 bent edges, 6% on-site potential noise, and 27% hopping phase noise. This work establishes a paradigm for robust light propagation in non topological systems and broadens the physical basis for unidirectional EICs.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Tropospheric temperature and humidity profile retrieval from Meteosat Flexible Combined Imager based on deep learning
Alejandro Salgueiro, Johannes Rausch, Julie Thérèse Villinger et al. (+1 more)
The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) offers new opportunities for tropospheric temperature and humidity profiling, at higher spatio-temporal resolutions and expanded spectral coverage relative to its predecessor. Vertically resolved
retrievals from broadband imagers are inherently challenging, and operational
retrieval algorithms typically rely on numerical weather prediction (NWP) background fields to compensate for limited infrared spectral resolution, reducing the
retrievals' independence. We develop a spatially aware deep learning framework to retrieve all-sky tropospheric temperature and humidity profiles from FCI, without forecast profiles as input. A Residual U-Net that exploits spatial context across all 16 FCI channels was trained on 14 months of collocated FCI observations and CERRA reanalysis targets over Europe. Validated against independent radiosondes, retrieved temperatures show biases below 0.4 K and standard deviations of 1.5-1.9 K. Retrieved relative humidity standard deviations range from 12-20 %, compared to 9-19 % for CERRA. Performance degrades modestly under clouds, with standard deviation increases below 0.4 K and 3 % RH beneath cloud tops despite limited direct radiative information. Ablation experiments show that spatial context improves
retrievals, with the largest gains below cloud tops. Feature sensitivity analysis indicates broad consistency with FCI bands' established radiative transfer characteristics. Visible and near-infrared channels contribute despite not being commonly used in physics-based profile inversions. These results demonstrate that spatially aware deep learning models can extract statistically reliable tropospheric profiles from geostationary imager observations, independent of NWP forecast fields, enabling more rapid autonomous monitoring of the atmosphere.
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All authors Alejandro Salgueiro, Johannes Rausch, Julie Thérèse Villinger, Angela Meyer
Instrumentation and Detectors
Signal formation and induction-gap optimization in a THGEM coupled to a resistive plate anode
Arpan Maity, Luca Moleri, Maryna Borysova et al. (+1 more)
A Thick Gaseous Electron Multiplier (THGEM) detector coupled to a resistive plate anode was investigated for the first time, using an Ar:CO$_2$ (93:7) gas mixture. The resistive anode concept enabled stable operation with induction gaps significantly smaller than those typically employed in THGEM detectors, opening the possibility of improving signal formation and timing performance. The effect of the induction gap on the detector current- and charge-signal characteristics was systematically investigated. An optimal induction gap of 0.2 mm was identified based on several key signal parameters. Subsequently, detailed studies were carried out for this optimal configuration under different electric-field settings. The detector achieved a highest peak amplitude of $\sim$56 $\mu$A with a rise time of $\sim$5 ns at $\Delta\text{V}_{\text{THGEM}}$=1900 V, $\Delta\text{V}_{\text{induction}}$=100 V, and a drift field of 1 kV/cm when irradiated with 5.9 keV X-rays. Under the same operating conditions, a time resolution of $\sim$6.0 ns was measured detecting
cosmic muons. This study establishes a new THGEM detector configuration and provides guidance for its potential application in particle-detection systems, such as
muon spectrometers and sampling elements of digital hadronic calorimeters.
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All authors Arpan Maity, Luca Moleri, Maryna Borysova, Shikma Bressler
Applied Physics
Stringent Constraints on Spin-Spin-Velocity-Dependent Exotic Interactions with a Levitated Magnet Force Sensor
Kenan Tian, Siwen Chen, Lei Wang et al. (+12 more)
Exotic
spin-
spin-velocity-dependent interactions, predicted in extensions of the Standard Model involving new bosonic fields, could resolve fundamental puzzles from dark matter to
cosmic asymmetry. However, exploring these weak potential interactions at centimeter scales presents formidable challenges, primarily due to the overwhelming dominance of electromagnetic backgrounds that can easily obscure the weak exotic signals. Here, we utilize a levitated magnet force sensor with ultrahigh electron
spin density to probe these interactions. We constrain two interactions individually through a designed
spin source and a multi-layer
magnetic shielding system that suppresses electromagnetic backgrounds. In this study, we constrain two types of interactions: the V_6 potential at force ranges from $10^{-3}$ m to $6 \times 10^{-2}$ m and the V_{14} potential at ranges greater than $10^{-3}$ m. Our measurements establish 95% confidence-level bounds of $|f_6| \leq 2.12 \times 10^{-13}$ and $|f_{14}| \leq 2.34 \times 10^{-23}$ at $\lambda = 1.6 \times 10^{-2}$ m, improving prior limits by up to 12 and 13 orders of magnitude, respectively. Our result demonstrates the levitated magnet as a highly sensitive probe for detecting new bosonic fields in extensions of the Standard Model.
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All authors Kenan Tian, Siwen Chen, Lei Wang, Yuanji Sheng, Dingjiang Long, Rui Li, Han Xie, Yiming Chen, Xiang Bian, Hao Wang, Ruoyu Ding, Chang-Kui Duan, Peiran Yin, Xi Kong, Pu Huang