Optics
Silicon Photonics-based Heterodyne Interferometric Imager for free-space imaging
Humphry Chen, Mingye Fu, Shun-Hung Lee et al. (+6 more)
This paper reports on the design, fabrication, and demonstration of a silicon photonics based heterodyne interferometric imaging system. The photonic integrated
circuit (PIC) can perform one-dimensional spectroscopy for unique input spectrums using a single baseline within its 91 available baselines. The PIC uses polarization diversifying gratings to separate incoming light into two distinct polarizations, an on-chip 2x4 optical hybrid, and a strong local oscillator (LO) to perform the heterodyne measurements. The optical hybrids combine the input signals with the LO and splitting them into 2 components pairs for phase sensitive measurements. Furthermore, the PIC can perform 2-D image reconstruction by combining many baseline pairs to measure the visibility of a simple target. These demonstrations show the PIC's capabilities for 1-D spectroscopy and 2-D imaging applications.
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All authors Humphry Chen, Mingye Fu, Shun-Hung Lee, Shelbe Timothy, Lawrence Shing, Gopal Vasudevan, Tony Kowalczyk, Neal Hurlburt, Sung-Joo Ben Yoo
Fluid Dynamics
A Solid-Based Approach for Modeling Simple Yield-Stress Fluids: Rheological Transitions, Overshoot and Relaxation
Jehyeok Choi, Ju Min Kim and Kwang Soo Cho
Yield-stress fluids are ubiquitous and encountered in diverse fields ranging from natural muddy flows to industrial applications such as secondary battery electrode slurries and direct ink writing. Despite the proposal of various constitutive equations, few models have been shown to successfully predict both steady and transient rheological behaviors in yield-stress fluids. In this study, a constitutive equation is hereby proposed, offering a comprehensive description of the rheological characteristics observed in simple yield-stress fluids, excluding thixotropy, such as the Carbopol dispersion. The constitutive equation is derived from a Zener-type viscoelastic solid element combined with an additional linear dashpot connected in parallel, together with a nonlinear viscosity model, a flow rule, an
evolution equation for the back stress, and the Kroner-Lee decomposition. This combination satisfies the principle of material frame invariance. The proposed model successfully reproduces the rheological characteristics qualitatively in a manner consistent with experimental observations conducted during start-up shear, creep, and stress relaxation tests. In particular, the present viscoelastic solid-based constitutive equation is shown to accurately predict stress overshoot during start-up shear. Importantly, the overshoot is found to originate from a homogeneous mechanism in which normal stress difference enhances the stress invariant and thereby accelerates the plastic response, rather than from isotropic hardening or spatially heterogeneous microstructural
evolution. This study is expected to facilitate a deeper understanding of the intricate dynamics governing the flow of yield-stress fluids.
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Atomic Physics
Direct three body dynamics govern ion atom recombination and barrierless termolecular reactions
Rian Koots, Marjan Mirahmadi and Jesús Pérez-Ríos
For over a century, termolecular, or third order, chemical reactions have been explained by the Lindemann Hinshelwood mechanism, assuming sequential stabilization via bimolecular encounters. Here, we demonstrate that barrierless termolecular reactions are fundamentally governed by direct three body dynamics. Using classical trajectory calculations in hyperspherical coordinates, we quantitatively reproduce ion atom recombination kinetics across a wide temperature range without invoking intermediate complexes or steady state assumptions. Our results not only resolve longstanding discrepancies between theory and experiment, but also establish a general mechanistic framework for barrierless termolecular reactions, with implications spanning atmospheric chemistry,
plasma physics, and ultracold chemistry.
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Chemical Physics
Electron dynamics mediate the water-carbon {\pi} bond
N. LeMessurier, E. Katz, R. Pant et al. (+5 more)
The intermolecular interaction between a water molecule and the electrons in aromatic {\pi} systems–the water-{\pi} bond–lies at the heart of many chemical processes, yet its properties remain challenging to measure experimentally and model computationally. Infrared spectroscopy of pyrene anions hydrated by a single water molecule reveals vibrational and electronic motions that are often hidden in condensed phase measurements. Results from new machine-learning approaches to potentials and dipole moments show that the electron dynamics of the aromatic {\pi} cloud quench signals from some of water's vibrations and amplify others. The observed interplay between electronic and vibrational motions has general implications for modeling intermolecular interactions between water and aromatic systems in clusters, solutions, and at interfaces.
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All authors N. LeMessurier, E. Katz, R. Pant, S. Ganley, H. Salzmann, L. M. McCaslin, J. M. Weber, J. D. Eaves
Chemical Physics
FermiLink: An Agent Harness for Multidomain Scientific Simulations
Gang Meng, Andres Felipe Bocanegra Vargas, Xinwei Ji et al. (+7 more)
Artificial-intelligence (AI)
agent harnesses have been developed for autonomous scientific simulations, but most current
agent harnesses are tested on single or a small set of software packages. Herein, FermiLink, an extensible open-source
agent harness is introduced for multidomain scientific simulations. Its key design principle is the separation of package knowledge bases from simulation workflows, so that simulation workflows in FermiLink, from figure-level simulations to full-paper-level research on high-performance computing clusters, operate uniformly among supported packages via a four-layer progressive disclosure mechanism. This
agent harness is specifically optimized for high performance computing (HPC) clusters, enabling multi-day multi-step scientific simulations in the absence of human intervention. Using OpenAI Codex as the
agent provider, the capabilities of FermiLink are demonstrated across approximately 50 scientific software packages spanning nine research domains from chemistry to engineering. Systematic
benchmarks on 132 real-world figure-level reproduction tasks with 44 packages show that FermiLink reproduces 74 (56.1%) of published figures with simulations, among which 30 (22.7%) achieve high-fidelity agreement and 35 reach qualitative agreement with the target figures. A smaller set of human expert-guided reproduction
benchmarks with 10 packages in physics and chemistry further highlights the importance of expert insights for improving the simulation fidelity. Beyond reproduction, a single-blinded study demonstrates that FermiLink can produce research-grade results on unpublished polariton physics problems when provided with sufficiently detailed research objectives. Overall, FermiLink provides a scalable research infrastructure that may accelerate the path from scientific questions to computational results across diverse domains.
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All authors Gang Meng, Andres Felipe Bocanegra Vargas, Xinwei Ji, Federico Garcia-Gaitan, Felipe Reyes-Osorio, Jalil Varela-Manjarres, Yafei Ren, Mohammadhasan Dinpajooh, Branislav K. Nikolić, Tao E. Li
Atmospheric and Oceanic Physics
Physics-Constrained Adaptive Flow Matching for Climate Downscaling
Kevin Debeire, Aytaç Paçal, Pierre Gentine et al. (+3 more)
Regional
climate information at kilometer scales is essential for assessing the impacts of
climate change, but generating it with global
climate models is too expensive due to their high computational costs. Machine learning models offer a fast alternative, yet they often violate basic physical laws and degrade when applied to climates outside of their training distribution. We present Physics-Constrained Adaptive Flow Matching (PC-AFM), a generative downscaling model that addresses both problems. Building on the Adaptive Flow Matching (AFM) model of Fotiadis et al. (2025) as our baseline, we add soft conservation constraints that keep the downscaled output consistent with the large-scale input for precipitation and humidity, and use gradient surgery via the ConFIG
algorithm to prevent these constraints from interfering with the generative objective. We train the model on Central Europe
climate data, evaluate it on a 10-time downscaling task (63km to 6.3km) over six variables (near-surface temperature, precipitation, specific humidity, surface pressure, and horizontal wind components) across a comprehensive set of metrics including bias, ensemble skill scores, power spectra, and conservation error, and test the generalization on two held-out
climate regions. Within the training distribution, PC-AFM reduces conservation errors and improves ensemble calibration while matching the baseline on standard skill metrics. Outside the training distribution, where unconstrained models develop large systematic errors by extrapolating learned statistics, PC-AFM halves precipitation wet bias, reduces conservation error and improves extreme-
quantile accuracy, all without any information about the target
climate at inference time. These results indicate that physical consistency is a practical requirement for deploying generative downscaling models in real-world applications.
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All authors Kevin Debeire, Aytaç Paçal, Pierre Gentine, Luis Medrano-Navarro, Nils Thuerey, Veronika Eyring
Computational Physics
A multiphysics deep energy method for fourth-order phase-field fracture with piezoresistive self-sensing
Aamir Dean and Betim Bahtiri
Piezoresistive materials can act as self-sensing media because deformation and cracking modify their electrical resistance. This paper presents a fracture-informed multiphysics framework for phase-field fracture using the Deep Energy Method. Mechanical deformation and fracture are solved first, after which the converged strain and damage fields determine a passive electrical-conduction readout. The electrical field does not contribute to the fracture-driving energy, and a control calculation confirmed that removing the electrical contribution produced no observable change in the crack morphology or global mechanical response. The mechanics-fracture formulation combines small-strain elasticity, a spectral tension-compression split, history-field irreversibility, and a fourth-order AT2-type regularization. The subsequent steady-conduction problem employs a conductivity law that accounts for linearized piezoresistivity and crack-induced degradation. The implementation uses admissible neural trial functions, quadrature-based energy evaluation, warm-started load stepping, and a bounded phase-field variable. Component-level verification includes analytical conduction tests, a reference-aligned single-edge-notched tension
benchmark, and a perforated tensile-plate example. The results show that substantial local damage does not necessarily cause an immediate global resistance increase; a pronounced signal emerges only when major current-carrying ligaments are disrupted. The framework thus provides a transparent forward model for interpreting resistance-based self-sensing in fractured piezoresistive materials.
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Optics · Cross-listed from cond-mat.mtrl-sci
Microwave-to-optical transduction using magnon-exciton coupling in a layered antiferromagnet
Pratap Chandra Adak, Iris McDaniel, Suvodeep Paul et al. (+12 more)
Coherent interfaces between microwave-frequency
quantum systems and low-loss optical links are essential for
quantum networks. However, existing microwave-optical transducers often trade conversion efficiency against added noise, bandwidth, and device integrability. Here, we demonstrate coherent microwave-to-optical transduction based on
magnon-
exciton coupling in the layered antiferromagnet CrSBr. Driving the
antiferromagnetic resonance with microwave signals imprints coherent modulation on a reflected optical probe, generating optical sidebands that are resonantly enhanced near excitonic transitions. While prior
magnon-based approaches to microwave-to-optical transduction have typically relied on intrinsically weak off-resonant magneto-optical effects (e.g., Faraday rotation), our scheme exploits strong light-matter interactions at
exciton resonances. Even in a bulk
crystal without cavity enhancement, we observe coherent conversion over an intrinsically broadband window of ~ 300 MHz. We further show that multiple
exciton-polariton resonances inherit the
magnon-coupled response, suggesting a route to broaden the usable optical detuning range and to mitigate optical dissipation. Our results establish
magnon-coupled
excitons in layered magnets as a scalable platform for broadband microwave-optical interfaces, with pathways to higher cooperativity via reduced
magnetic volume and cavity integration.
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All authors Pratap Chandra Adak, Iris McDaniel, Suvodeep Paul, Caleb Heuvel-Horwitz, Bikash Das, Vitali Kozlov, Kseniia Mosina, Arun Ramanathan, Xavier Roy, Zdeněk Sofer, Tian Zhong, Akashdeep Kamra, Arno Thielens, Andrea Alù, Vinod M. Menon
Plasma Physics
Resolution-Independent Machine Learning Heat Flux Closure for ICF Plasmas
M. Luo, A. R. Bell, F. Miniati et al. (+2 more)
Accurate modeling of heat flux in inertial confinement fusion
plasmas requires closures that remain predictive far from local
equilibrium and across disparate spatial and temporal resolutions. We develop a resolution-independent machine-learning heat flux closure trained on particle-in-
cell simulations using a
Fourier Neural Operator. Two nonlocal electron thermal conduction models are trained and tested. When embedded self-consistently into the electron energy equation, the learned closure faithfully reproduces the temperature
evolution and shows good temporal extrapolation and generalization capability. Remarkably, models trained on coarse-resolution data accurately predict heat flux when deployed in substantially finer-resolution implicit, iterative solvers of the energy equation, significantly enhancing the practicality of
embedding data-driven closures into partial differential equation solvers. These results establish a data-driven closure that bridges kinetic and fluid descriptions and provides a viable pathway for treating machine learning as an iterative solver within the radiation-hydrodynamic simulations of ICF
plasma.
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All authors M. Luo, A. R. Bell, F. Miniati, S. M. Vinko, G. Gregori
Atomic Physics · Cross-listed from quant-ph
A High Motional Frequency Ion Trapping Regime for Quantum Information Science
A. J. Rasmusson
We investigate high frequency motional states of trapped atomic ions. Trapped ions in rf traps are confined by an approximate harmonic potential and exhibit
quantum motional states that mediate essential techniques in
quantum computing, simulation, networking, and precision measurement. However, motional state decoherence mechanisms, heating and dephasing, are broadly limiting: reduced two-
qubit gate fidelities; lower fidelity and lifetime of highly nonclassical bosonic states; long
laser cooling times; and large
recoil heating rates. These also challenge the scalability of increasingly sophisticated protocols. We propose high motional frequency ion trapping as an operating regime that addresses these challenges and reshapes the design landscape for
quantum information experiments and
quantum control techniques. We report an experimentally motivated investigation of realizing this high-frequency regime and discuss the consequences for
laser cooling, motional state coherence, fidelity and lifetime of nonclassical bosonic states, and scalability of experimental runtimes. We report clear design trajectories for ion traps to reach high motional frequency, a new limiting mechanism for
laser cooling at these high frequencies, and more than an order-of-magnitude speedup in experimental duty cycles with larger speed ups possible for
quantum error correction protocols. Taken together, high motional frequency ion trapping has broad implications for the future of
quantum information experiments.
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Medical Physics
Three-dimensional intraoperative light field fluorescence imaging system for PpIX-guided tumour resection
Yucheng Bian, Sebastien Ourselin and Yijing Xie
Conventional 2D fluorescence imaging in glioma surgery cannot separate intrinsic fluorophore strength from attenuation with depth, creating depth-intensity ambiguity that can compromise assessment of residual tumour and fluorescence based grading. This study develops and validates a dual mode light field imaging system that could capture 3D structure and depth corrected fluorescence in a single snapshot by adapting a commercial Lytro Illum camera. A custom 3D printed depth standard was used to optimise main lens focal length and to derive a grayscale - distance linearity from Lytro Desktop depth maps. CdSe/ZnS
quantum dot targets and fluorescent
brain phantoms were imaged to establish fluorescence intensity distance attenuation models and to recover intrinsic fluorescence. In system optimisation, the increasing FU strengthened grayscale depth linearity and achieved millimetre scale vertical resolution ($R^{2}$ > 0.95) for FU $\ge$ 60 mm. Higher concentration
quantum dot wells of the fluorescent target showed consistent attenuation. In fluorescence mode, the deviations of distance estimations across six regions of a fluorescent
brain phantom were 0.14 to 2.45% with intensity prediction errors from -11.73% to 6.08% based on the fluorescence intensity-distance model, enabling recovery of intrinsic
quantum dot concentrations which are mimicking PpIX characteristics in glioma. This research supports light field imaging as a practical approach for depth resolved quantitative fluorescence and improved intraoperative tumour characterisation.
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Chemical Physics
Universal Scaling and Many-Body Resurrection of Polaritonic Double-Quantum Coherences
Maxim Sukharev
The ultrafast nonlinear optical response of molecular ensembles is fundamentally altered under strong light-matter coupling. To rigorously isolate the genuine many-body contributions, an exact time-domain field-subtraction protocol is developed within a fully non-perturbative Maxwell-Liouville framework explicitly incorporating the two-
exciton manifold in real space and time. This approach reveals that while collective cavity delocalization drives the macroscopic nonlinear signal toward a severe harmonic cancellation (an effect termed "spectral starvation"), intrinsic many-body molecular interactions robustly resurrect genuine polaritonic double-
quantum coherences (DQCs). This many-body resurrection is governed by a universal two-
photon matching rule, $\Delta_B + 4J = \Omega_R$, linking molecular anharmonicity ($\Delta_B$) to the macroscopic Rabi splitting ($\Omega_R$) and excitonic coupling ($J$). Crucially, this resonance exploits the spatial mismatch between macroscopic polaritons and localized two-
exciton pairs to break harmonic cancellation. For J-aggregates ($J < 0$), this condition uniquely isolates the resonant many-body state below the dense
manifold of localized dark states, protecting the macroscopic coherence from spatial fragmentation. This predictive framework establishes a direct phase diagram to engineer and protect optical nonlinearities across diverse strongly coupled platforms.
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Plasma Physics
Nonlinear spectral back transfer limits the temporal coherency of zonal modes
Rameswar Singh and P H Diamond
Zonal modes are central to
magnetic confinement because their radial shears regulate
turbulence and transport. While the generation of these flows is well understood, the mechanisms limiting their persistence in collisionless regimes remain unresolved. In this paper, we demonstrate that nonlinear spectral back-transfer of free energy from zonal modes to
turbulence is a major nonlinear process that limits the temporal coherency of the shearing field. Back-transfer events induce
stochastic phase and amplitude scattering of zonal shear that limits its auto-coherence time. Using local gyrokinetic GENE simulations for collisionless electrostatic ion temperature gradient (ITG) driven
turbulence with adiabatic electrons for parameters representative of core conditions, we show that back-transfer is highly intermittent and occurs in bursts that co-exist with the zonal flow generation process. The probability distribution of the zonal free energy transfer is non-Gaussian, with positive triangularity (PT) exhibiting substantially higher kurtosis than negative triangularity (NT), reflecting the markedly more intermittent and heavy-tailed character of back-transfer bursts in PT. We find that NT
plasmas exhibit significantly reduced back-transfer compared to PT. This suppression increases the shear auto-coherence time $\tau_{E}$ and the shearing Kubo number $K_{u}$, leading to more resilient and effective
turbulence regulation despite lower absolute zonal kinetic energy. These results identify back-transfer as a key nonlinear damping mechanism and suggest that it must be explicitly treated in reduced models of drift-wave zonal-flow
turbulence.
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Chemical Physics · Cross-listed from cond-mat.soft
Anatomy of a Complex Crystallization Pathway
Charlotte Shiqi Zhao, Domagoj Fijan and Sharon C. Glotzer
Using molecular dynamics simulations, we investigate the crystallization pathways of two exemplary systems that form the same complex
crystal structure but differ fundamentally in the nature of their particle interactions. One system is composed of point particles interacting via an isotropic pair potential characteristic of metallic compounds, while the other system contains hard polyhedra whose interactions arise from emergent entropic forces. Despite the stark difference in the origins of the particle interactions, we find that both systems are polymorphic and share the same
crystal polymorphs. Moreover, the two systems follow the same multistep crystallization pathways, and by examining the complex crystallization pathways on the single particle level, we find that the local structure
evolution of the two systems is also similar. By mapping the hard particle system's interaction to an effective pairwise potential, we find that such resemblance arises from the particle interactions being effectively similar.
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Geophysics
Exceedance Probabilities for Large Earthquakes From DIY Local Earthquake Ensemble Nowcasting and Forecasting
John B Rundle, Ian Baughman, Andrea Donnellan et al. (+3 more)
This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is validated by comparison to the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco. The method is then applied to a 125-KM radius circular area around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake, and short term forecasts (1 year and 5 year ) are computed.
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All authors John B Rundle, Ian Baughman, Andrea Donnellan, Lisa Grant Ludwig, Geoffrey Fox, Kazuyoshi Nanjo
Atmospheric and Oceanic Physics
Multidecadal Cycles Study in the Climate Indexes Series Using Wavelet Analysis in North/Northeast Brazil
Cleber Souza Corrêa, Roberto Lage Guedes, Karlmer Abel Bueno Corrêa et al. (+1 more)
This study investigates the climatic index time series over the most recent 80 years, using monthly mean values from the Pacific Decadal Oscillation Index (PDO), Southern Oscillation Index (SOI), and monthly
solar activity represented by sunspot numbers (MS), obtained from the National Weather Service
Climate Prediction Center and the World Data Center SILSO, Royal Observatory of Belgium, Brussels. The statistical software R was used with the WaveletComp package to generate Morlet wavelet power spectra, and bivariate cross-wavelet analysis using the biwavelet package. The results show predominant cycles with variability scales of 32, 64, 128, and 256 months, corresponding approximately to 2.66, 5.33, 10.66, and 21.33 years. These frequencies are observed in the period from January 1933 to September 2016, totaling 993 months (82.75 years), characterizing decadal and multidecadal variability. These multidecadal cycles (of the order of 10.66 and 21.33 years) suggest a possible association with
solar activity variability and
climate variability in the ocean-atmosphere system. Rainfall data from January 1951 to September 2017 were analyzed for Belém, São Luiz, Fortaleza, Natal, and Fernando de Noronha, forming a north to northeast Brazilian transect. These series show similarity with the decadal and multidecadal cycles observed in the SOI, PDO, and sunspot series.
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All authors Cleber Souza Corrêa, Roberto Lage Guedes, Karlmer Abel Bueno Corrêa, Felipe Gustavo Pilau
Atmospheric and Oceanic Physics
Multidecadal Cycles of the Climatic Index: Sunspots that Affect North and Northeast of Brazil
Cleber Souza Corrêa, Roberto Lage Guedes, André Muniz Marinho da Rocha et al. (+1 more)
Using the 1951–2017 historical series of the Atlantic Meridional Mode (AMM) index and the monthly number of sunspots, it was possible to observe a significant association between them. The use of wavelet and cross-wavelet analysis showed the presence of multidecadal cycles pronounced in eleven years, as well as cycles of 2.66 and 5.33 years. The AMM index showed, in the Sea Surface Temperature (SST) component, the presence of a weak signal of 21.33 years. The influence and association of sunspot variability on surface temperature in Northern and Northeastern regions of Brazil were investigated. Using a non-parametric statistical correlation test, the historical series of surface temperature anomalies in five locations (Belém, São Luiz, Fortaleza, Fernando de Noronha, and Natal) were compared with the monthly
solar-series anomalies. The temperature series used were the minimum monthly average, the monthly average, and maximum monthly average temperatures, with their respective anomalies in relation to the mean. However, among all the series (except for São Luiz), the analyzed minimum temperature anomalies showed a negative correlation with sunspots. As a preliminary result, the analyzed climatic indices present an apparent degree of
memory associated with the variability of sunspot activity.
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All authors Cleber Souza Corrêa, Roberto Lage Guedes, André Muniz Marinho da Rocha, Karlmer Abel Bueno Corrêa
Atmospheric and Oceanic Physics · Cross-listed from stat.AP
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis et al. (+2 more)
General Circulation Models (GCMs) are widely used for future
climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studies. This limitation is particularly critical for wind-related applications, such as wind energy, which require spatially coherent, multivariate, and physically plausible near-surface wind fields. Classical statistical downscaling and bias correction methods partly address this issue. Still, they struggle to preserve spatial structure, inter-variable consistency, and robustness under
climate change, especially in high-dimensional settings. Recent advances in generative machine learning offer new opportunities for downscaling and bias correction, eliminating the need for explicitly paired low- and high-resolution
datasets. However, many existing approaches remain difficult to interpret and challenging to deploy in operational
climate impact studies. In this work, we apply SerpentFlow, an interpretable, generative, domain
alignment framework, to the multivariate downscaling and bias correction of wind variables from GCM outputs. This is a method that generates low-resolution/high-resolution training data pairs by separating large-scale spatial patterns from small-scale variability. Large-scale components are aligned across
climate model and observational domains. Conditional fine-scale variability is then learned using a flow-matching generative model. We apply the approach to multiple wind variables downscaling, including average and maximal wind speed, zonal and meridional components, and compare it with widely used multivariate bias correction methods. Results show improved spatial coherence, inter-variable consistency, and robustness under future
climate conditions, highlighting the potential of interpretable generative models for wind and energy applications.
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All authors Julie Keisler, Boutheina Oueslati, Anastase Charantonis, Yannig Goude, Claire Monteleoni
Applied Physics
A scalable infrastructure for strontium optical clocks with integrated photonics
Zheng Luo, Travis C. Briles, Zachary L. Newman et al. (+16 more)
Optical atomic clocks provide exceptionally accurate and precise signals for timekeeping and precision measurements, but they require high-power, free-space
laser configurations that limit scalability. We introduce and explore a scalable infrastructure for strontium (Sr) optical-
lattice clocks that incorporates co-design of atomic-beam slowing and a magneto-optical trap (MOT) from an effusion source, generation of complex, three-dimensional free-space
laser configurations with a photonic integrated
circuit (PIC) and metasurface (MS) optics, and
laser stabilization to a frequency-comb supercontinuum generated with integrated nonlinear photonics. With these elements, we realize MOTs of all stable strontium isotopes ($^{84}$Sr, $^{86}$Sr, $^{87}$Sr, $^{88}$Sr) with populations commensurate with natural abundances, demonstrating precise beam control and robustness. Access to
laser-cooled alkaline-earth atoms with scalable integrated photonics enables system engineering for optical clocks,
quantum sensing, and
quantum information, and our experiments demonstrate extensible technologies that advance toward a Sr optical clock largely free of bulk optics.
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All authors Zheng Luo, Travis C. Briles, Zachary L. Newman, Aidan R. Jones, Andrew R. Ferdinand, Sindhu Jammi, Grisha Spektor, David R. Carlson, Akash Rakholia, Dan Sheredy, Parth Patel, Martin M. Boyd, Chad Ropp, Daron Westly, Vladimir A. Aksyuk, Wenqi Zhu, Junyeob Song, Amit Agrawal, Scott B. Papp
Computational Physics
Fast and Accurate Inverse Blood Flow Modeling from Minimal Cuff-Pressure Data via PINNs
Sokratis J. Anagnostopoulos, Georgios Rovas, Lydia Aslanidou et al. (+2 more)
Accurate assessment of central hemodynamics is essential for diagnosis and risk stratification, yet it still relies largely on invasive measurements or on indirect reconstructions built from population-averaged transfer functions. While conventional methods are valuable in clinical practice, they face limitations, particularly in personalized medicine. Physics-informed methods address these by integrating physical principles, reducing the need for extensive data. In this work, a fully noninvasive, patient-specific framework is developed that combines a validated 1-D model of the systemic arterial tree with physics-informed neural networks (PINNs). This model performs the inverse solution of the flow and pressure fields within the arterial network, given minimal noninvasive measurements of pressure from a cuff reading and trains in 4000 iterations, at least 10x faster than the current state-of-the-art models due to several model enhancements. We validate the model predictions against our 1-D solver, yielding a near perfect correlation, and perform additional tests on a clinical
dataset for the identification of important central hemodynamic parameters of cardiac output $CO$ and central systolic blood pressure $cSBP$, with correlations of $r=0.847$ and $r=0.951$, respectively. Moreover, the model is able to tune the patient-specific coefficients of the terminal resistance $R_T$ and compliance $C_T$ while training, treating them as learnable parameters. The inverse PINN model is able to solve the entire tree of 8 arteries with a single network, costing 5-10 minutes of computational time. This significant performance boost compared to traditional iterative inverse methods holds promise towards applications of personalized cardiac output monitoring and hemodynamic assessment via noninvasive approaches like wearable devices.
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All authors Sokratis J. Anagnostopoulos, Georgios Rovas, Lydia Aslanidou, Vasiliki Bikia, Nikolaos Stergiopulos
Chemical Physics · Cross-listed from astro-ph.EP
CO and N2 Produced from H2O, CO2, and NH3 Cometary Ice Analogs
Alexandra McKinnon, Alexia Simon, Michelle R. Brann et al. (+3 more)
Hypervolatile species such as carbon monoxide (CO) and molecular nitrogen (N2) have been detected in comets, and could be used to constrain comet formation temperature conditions if their presence is due to freeze-out and/or entrapment. Here we instead explore another plausible origin of cometary hypervolatiles: photodissociation of less volatile species. We characterize CO and N2 formation following ultraviolet (UV) irradiation and electron bombardment of carbon dioxide (CO2), ammonia (NH3), H2O:CO2, H2O:NH3, and H2O:CO2:NH3 cometary ice analogs. We find that CO and N2 form in all photoprocessed ices at temperatures between 10 K and 100 K, resulting in 0.4-0.9 % CO and 0.03-0.7 % N2 relative to water, and CO/CO2 and N2/NH3 mixing ratios of 2.5-62 % and 0.7-9 %, respectively, across the experiments. Because our initial ices are reasonably well-matched to interstellar ices and we use UV exposure similar to a dark cloud, we can compare the resulting ratios directly to cometary abundances. Such a comparison shows that while only a few of CO observations in comets are readily explained by photodissociation, almost all observed cometary N2 can be accounted for by photodissociation of NH3 embedded in water ice. The latter result is also consistent with observed similarly elevated isotopic ratios of N2 and NH3 in 67P. Taken together, our results suggest that N2/H2O ratios less than 1 % should be used cautiously when inferring a comet's formation location, while the more substantial CO abundances seen in many comets do likely imply entrapment at low ice temperatures.
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All authors Alexandra McKinnon, Alexia Simon, Michelle R. Brann, Elettra L. Piacentino, Karin I. Oberg, Mahesh Rajappan
Computational Physics · Cross-listed from cs.LG
Real-Time Surrogate Modeling for Personalized Blood Flow Prediction and Hemodynamic Analysis
Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia et al. (+3 more)
Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large in silico cohorts remains challenging. Certain hemodynamic parameters like the terminal resistance/compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated
datasets to be discarded. In this work, we present a systematic framework for training machine learning (ML) models, capable of instantaneous hemodynamic prediction and parameter estimation. We initially start with generating a parametric virtual cohort of patients which is based on the multivariate correlations observed in the large Asklepios clinical
dataset, ensuring that physiological parameter distributions are respected. We then train a deep neural surrogate model, able to predict patient-specific arterial pressure and cardiac output (CO), enabling rapid a~priori screening of input parameters. This allows for immediate rejection of non-physiological combinations and drastically reduces the cost of targeted synthetic
dataset generation (e.g. hypertensive groups). The model also provides a principled means of sampling the terminal resistance to minimize the uncertainties of unmeasurable parameters. Moreover, by assessing the model's predictive performance we determine the theoretical information which suffices for solving the inverse problem of estimating the CO. Finally, we apply the surrogate on a clinical
dataset for the estimation of central aortic hemodynamics i.e. the CO and aortic systolic blood pressure (cSBP).
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All authors Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia, Theodore G. Papaioannou, Athanase D. Protogerou, Nikolaos Stergiopulos
Fluid Dynamics
Bifurcations in Stokes Flow Sedimentation
Elias Huseby, Pierre Mathier, Meera Das et al. (+5 more)
Particles whose shapes couple translation to rotation display a rich array of behaviors as they sediment at low Reynolds number. We introduce a unifying perspective in which the possible dynamical regimes and bifurcations between them can be understood. We use experimental measurements of helical ribbons, with controlled center of mass offsets, to identify the key bifurcation from complex dynamics to a single attracting state as the magnitude of the offset increases. The sedimentation dynamics are very sensitive to small center of mass offsets, with the bifurcation occurring for offsets less than one percent of the particle length. Using mobility tensors obtained from immersed boundary method simulations, we simulate helical particle sedimentation and identify an
alignment bifurcation surface, defined in the three dimensional space of center of mass offsets, that separates simple from complex sedimentation dynamics. Inside this surface we find limit cycles which emerge through
Hopf and homoclinic bifurcations. Cocentered particles with coincident centers of force and mobility provide a reference case at the center of the bifurcation surface. We show how the
geometric and dynamical
symmetries of sedimenting cocentered particles are broken as the center of force offset moves away from the cocentered case. Three parity time-reversal (PT)
symmetries exist for all cocentered particles under reflections normal to the eigenvectors of its translation-rotation coupling tensor. When a center of force offset preserves at least one of these PT
symmetries, then there are closed orbits for particles inside the
alignment bifurcation surface.
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All authors Elias Huseby, Pierre Mathier, Meera Das, Arjun Menezes, Theo Witkamp, Ziqi Wang, Bernhard Mehlig, Greg A. Voth
Applied Physics · Cross-listed from cs.NE
Biologically Realistic Dynamics for Nonlinear Classification in CMOS+X Neurons
Steven Louis, Hannah Bradley, Artem Litvinenko et al. (+3 more)
Spiking neural networks encode information in spike timing and offer a pathway toward energy efficient artificial intelligence. However, a key challenge in spiking neural networks is realizing nonlinear and expressive computation in compact, energy-efficient hardware without relying on additional
circuit complexity. In this work, we examine nonlinear computation in a CMOS+X spiking
neuron implemented with a
magnetic tunnel junction connected in series with an NMOS transistor.
Circuit simulations of a multilayer network solving the XOR classification problem show that three intrinsic neuronal properties enable nonlinear behavior: threshold activation, response latency, and absolute refraction. Threshold activation determines which
neurons participate in computation, response latency shifts spike timing, and absolute refraction suppresses subsequent spikes. These results show that magnetization dynamics of MTJ devices can support nonlinear computation in compact neuromorphic hardware.
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All authors Steven Louis, Hannah Bradley, Artem Litvinenko, Cody Trevillian, Darrin Hanna, Vasyl Tyberkevych
Atomic Physics · Cross-listed from cond-mat.quant-gas
Determination of the ground state polarizability of $^{162}$Dy near 530 nm
Alexandre Journeaux, Maxime Lecomte, Julie Veschambre et al. (+3 more)
Open-shell lanthanide atoms, and dysprosium in particular, combine a large ground-state angular momentum with dense electronic spectra, making their dynamical polarizability strongly dependent on wavelength and internal state, and therefore challenging to calculate and characterize experimentally. This issue has become especially relevant with the recent development of single-atom trapping of dysprosium in optical-tweezer arrays, where precise knowledge of the polarizability is needed to design optimized trapping architectures. Here, we exploit the strong
spin-dependent light shift near the $J'=J-1$ intercombination line at 530.306 nm to determine the background scalar and vector polarizabilities of $^{162}$Dy in its ground state near this wavelength. Our measurements quantitatively agree with atomic-structure calculations and provide new insight into the contributions of nearby transitions in a spectral region relevant to emerging dysprosium tweezer platforms.
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All authors Alexandre Journeaux, Maxime Lecomte, Julie Veschambre, Maxence Lepers, Jean Dalibard, Raphael Lopes
Optics
Wideband integrated high-speed graphene-silicon slot-waveguide electro-absorption modulator at 2 {\mu}m and 1.5 {\mu}m wavebands
Chao Luan, Deming Kong, Yunhong Ding et al. (+1 more)
The 2-{\mu}m waveband, emerging as a highly promising candidate for optical communication, offers an extended wavelength window for high-speed optical transmission. Despite its potential, the development of integrated electro-optic (E/O) modulators operating at this wavelength range has been limited. Such E/O modulators are crucial for high-speed optical communication systems at the 2-{\mu}m waveband. In this work, we propose and experimentally demonstrate high-performance E/O absorption modulators based on a
graphene-silicon slot waveguide. Our approach enables wideband, high-speed, efficient, robust and compact modulators at both 2-{\mu}m and 1.5-{\mu}m wavebands. This work represents a significant advancement towards the realization of high-speed integrated E/O modulators for optical communication systems operating at the 2-{\mu}m wavelength range.
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All authors Chao Luan, Deming Kong, Yunhong Ding, Hao Hu
Optics · Cross-listed from math-ph
Scattering of TE and TM waves by inhomogeneities of a 2D material, low-frequency behavior of the scattering amplitude, and low-frequency invisibility
Farhang Loran and Ali Mostafazadeh
The propagation of the transverse electric (TE) and transverse
magnetic (TM) waves in an effectively two-dimensional (2D) isotropic medium is described by Bergmann's equation of acoustics. We develop a dynamical formulation of the stationary scattering of these waves and explore its application in the study of the low-frequency behavior of the scattering data. Specifically, we introduce a suitable notion of fundamental transfer
matrix for TE and TM waves in 2D. This is an integral operator $\widehat{\mathbf{M}}$ that carries the information about the scattering properties of the medium and admits a Dyson series expansion involving a non-Hermitian
Hamiltonian operator. For situations where the inhomogeneities of the medium are confined to a layer of thickness $\ell$, we use the Dyson series for $\widehat{\mathbf{M}}$ to construct the series expansion of the scattering amplitude in powers of $k\ell$, where $k$ is the incident wavenumber. We derive analytic expressions for the leading- and next-to-leading-order terms of this series, verify the effectiveness of their application to a class of exactly solvable models, and use them to study low-frequency invisibility. In particular, we develop a low-frequency cloaking scheme which is applicable for both TE and TM waves. Our results have immediate applications in the study of low-frequency scattering of acoustic waves in a 2D fluid as these waves are also described by Bergmann's equation.
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Optics
Localized Photon Absorption in a Single-Crystalline Material
Daniel Kazenwadel, Jacob Holder, Livio Ciorciaro et al. (+6 more)
The absorption of light is one of the most fundamental processes in condensed-matter physics and optics. Here we investigate under which conditions
laser light is absorbed by a crystalline material as an electromagnetic wave with delocalized properties or rather as
photons that cause discrete, localized, nanometer-sized consequences. We excite the first-order phase transition of vanadium dioxide with
laser pulses of sufficient frequency to overcome the band gap but with insufficient pulse energy to overcome the latent heat. According to Maxwell's equations and Bloch theory, no transition should occur, because nowhere in the material is enough energy. Nevertheless, we observe with ultrafast electron diffraction a disordered
crystal geometry with nanometer-sized spots of switched material that grow and diminish with time. The amount of localized spots matches approximately to the number of
photons in the absorbed
laser wave. Two optical experiments substantiate this phenomenon, and simulations reproduce all measurements results. We discuss whether
crystals defects, temperature, or a genuine wavefunction collapse can explain the discovered phenomenon. Practically, the reported absorption mechanism enables local consequences at substantially higher energy than average and provides insight into
symmetry breaks and non-thermal fluctuations within complex materials.
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All authors Daniel Kazenwadel, Jacob Holder, Livio Ciorciaro, Noel Neathery, Raphael Schwenzer, Leon Oleschko, Jannik Hertkorn, Margaretha Sandor, Peter Baum
Chemical Physics
Low-Scaling Many-Body Green's Function Calculations for Molecular Systems via Interacting-Bath Dynamical Embedding Theory
Christian Venturella, Jiachen Li and Tianyu Zhu
We present a molecular extension of our recently proposed Green's function
embedding method, interacting-bath dynamical
embedding theory (ibDET), for computing charged excitation energies at the $GW$ and EOM-CCSD levels. Starting from atom-centered
impurities, we construct bath representations that capture the frequency-dependent
entanglement between the
impurity and its environment and can be systematically improved via the construction of cluster-specific natural orbitals. Utilizing a $GW$ or coupled-cluster Green's function solver, the self-energy of the full system is assembled from all
embedding problems to obtain the interacting Green's function. We show that ibDET provides accurate spectral properties with much reduced cost for a broad range of systems, including conjugated molecules and nanoclusters. Compared with full-system results, the errors in the predicted ionization potentials and electron affinities are around 0.1 eV or smaller, while each
embedding problem includes only a small fraction of the total orbital space. This work provides an efficient and scalable framework for computing spectral properties of molecular systems.
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Physics and Society
Understanding the complexity of frequency and phase angle fluctuations in power grids
Alessandro Lonardi, Jacques M. Maritz, Leonardo Rydin Gorjão et al. (+1 more)
Power grids must modernize to meet
climate goals while maintaining reliable and stable operating conditions. Yet progress is hindered by a limited understanding of the
stochastic processes underlying grid frequency and phase-angle fluctuations, which are induced by the growing penetration of renewable generation, consumer demand fluctuations, and
market trading. This issue is particularly acute in Africa, where grids often face weak investment. Here, we present results from a newly collected, large-scale, high-resolution
dataset of grid frequency and phase angles for the United Kingdom and South Africa, comprising close to one billion data points. Using superstatistical modeling, we treat
market-driven power fluctuations as a slowly varying parameter driving grid dynamics and incorporate nonlinear frequency control. As a result, we derive an analytical model that reproduces
multimodal frequency distributions previously obtained from numerical simulations, as well as heavy-tailed fluctuations and double-exponential frequency autocorrelation decays, all in excellent agreement with experimental measurements. Beyond frequency, we also address the so far largely overlooked problem of characterizing spatial phase-angle fluctuations. By comparing our predictions with measurement data, we demonstrate that a low-dimensional effective grid model accurately fits South African data despite the grid's complexity. We also highlight significant differences between the grids of South Africa and the United Kingdom. Our results clarify how energy
markets and control policies shape grid dynamics across countries with contrasting infrastructure maturity.
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All authors Alessandro Lonardi, Jacques M. Maritz, Leonardo Rydin Gorjão, Christian Beck