Atmospheric and Oceanic Physics
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Ankur Mahesh, William D. Collins, Travis A. O'Brien et al. (+10 more)
The response of the
climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typically activated in response to changes in ocean temperatures on decadal timescales and manifest as changes in climatic state with no recent historical analogue. However, fast feedbacks are activated in response to rapid atmospheric physical processes on weekly timescales, and they are already operative in the present-day
climate. This distinction implies that the physics of fast radiative feedbacks is present in the historical meteorological reanalyses used to train many recent successful machine-learning-based (ML) emulators of weather and
climate. In addition, these feedbacks are functional under the historical boundary conditions pertaining to the top-of-atmosphere radiative balance and sea-surface temperatures. Together, these factors imply that we can use historically trained ML weather emulators to study the response of radiative-convective
equilibrium (RCE), and hence the global hydrological cycle, to perturbations in carbon dioxide and other well-mixed greenhouse gases. Without retraining on prospective Earth system conditions, we use ML weather emulators to quantify the fast precipitation response to reduced and elevated carbon dioxed concentrations with no recent historical precedent. We show that the responses from historically trained emulators agree with those produced by full-physics Earth System Models (ESMs). In conclusion, we discuss the prospects for and advantages from using ESMs and ML emulators to study fast processes in global
climate.
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All authors Ankur Mahesh, William D. Collins, Travis A. O'Brien, Paul B. Goddard, Sinclaire Zebaze, Shashank Subramanian, James P.C. Duncan, Oliver Watt-Meyer, Boris Bonev, Thorsten Kurth, Karthik Kashinath, Michael S. Pritchard, Da Yang
Biological Physics · Cross-listed from cond-mat.soft
Evaporation-Induced Pattern Formation and Wetting in Active Microtubule-Kinesin Droplets
Vahid Nasirimarekani, Mehrana R. Nejad, Olinka Ramírez-Soto et al. (+4 more)
Active networks composed of biopolymers and motor
proteins provide versatile biomimetic systems that have advanced active matter physics and deepened our understanding of cytoskeletal dynamics and self-organization under diverse stimuli. In these systems, activity arises in aqueous solutions where motor
proteins cross-link biopolymers and generate active stress driving the emergent network behavior. Here, we establish the active network in the form of a sessile, multi-component droplet on a substrate and investigate how evaporation influences its dynamics. We focus on how mass loss and compositional changes in the droplet reshape the behavior of the active suspension. We show that capillary and Marangoni flows drive the self-organization of microtubules into a distinctive radial arrangement within the droplet. The cross-linking ability of motor
proteins gives rise to a striking non-monotonic wetting behavior, where the extensile stresses generated by the motor
proteins strongly affect the characteristic timescale of the contact-line retracting and subsequent expansion. Using a combined experimental and theoretical approach, we demonstrate the crucial role of crosslinking in evaporating microtubule networks, and explain how active stresses together with evaporation-induced flows govern the dynamics of reconstituted microtubule systems and their wetting behavior. Evaporating droplets have recently attracted significant
attention in the scientific community, and the findings of the setup presented in this study can have broad implications, ranging from self-organization and mechanical pattern formation in biological systems to questions about the origin of life.
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All authors Vahid Nasirimarekani, Mehrana R. Nejad, Olinka Ramírez-Soto, Susan Ali, Stefan Karpitschka, L. Mahadevan, Isabella Guido
Fluid Dynamics · Cross-listed from cond-mat.soft
Non-local physics-informed neural networks for forward and inverse solutions of granular flows
Saghar Zolfaghari and Safa Jamali
Dense granular flows exhibit nonlocal effects due to stress transmission in microplastic events, especially in quasi-static or slowly sheared regions. Hence, traditional local rheological models fail to capture spatial cooperativity effects that are prominent in many granular systems. The nonlocal granular fluidity (NGF) model addresses this limitation by introducing a diffusive-like partial differential equation for a fluidity field, governed by a key material-dependent parameter: the nonlocal amplitude A. However, determining A from experiments or simulations is known to be difficult and typically requires extensive calibration across multiple geometries. In this work, we present a data-driven platform based on Physics-Informed Neural Networks (PINNs) embedded with the NGF model, capable of solving granular flows in a forward or inverse manner. We show that once trained on transient flow fields, these non-local PINNs can readily infer the material parameters, as well as the pressure and stress fields. These data-driven frameworks allow for accurate recovery of small variations in the nonlocal amplitude, A, which lead to sharp bifurcation-like transitions in the flow field. This approach demonstrates the feasibility of data-driven parameter inference in complex nonlocal models and opens up new possibilities for characterizing granular materials from sparse experimental observations.
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Optics
Chem-SIM: Super-resolution Chemical Imaging via Photothermal Modulation of Structured-Illumination Fluorescence
Dashan Dong, Danchen Jia, Xinyan Teng et al. (+6 more)
Structured illumination microscopy (SIM) has attained high spatiotemporal delineation of subcellular architecture, yet offers limited insight into chemical composition. We develop Chem-SIM, a structured-illumination fluorescence detected mid-infrared photothermal microscopy, for super-resolved chemical imaging of microorganisms and mammalian
cells. Poisson maximum-likelihood demodulation and spectral normalization across wavenumber recover the weak IR-induced fluorescence intensity change under low
photon budgets and convert the fluorescence intensity modulation to chemical fingerprints. Photothermal gating further rejects water backgrounds in aqueous samples, while the IR pump maintains cellular activity at near-physiological temperature. Chem-SIM preserves full vibrational fingerprints, achieves SIM-grade lateral resolution in a high-throughput camera-based format. Here, we show that this platform distinguishes stationary- from log-phase bacteria through chemical content mapping, reports deuterated fatty-acid incorporation in ovarian cancer
cells, and resolves lipid-droplet dynamics in live
cells, establishing a high-throughput route to super-resolved imaging of organelle chemistry, metabolism, and dynamics.
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All authors Dashan Dong, Danchen Jia, Xinyan Teng, Jianpeng Ao, George Abu-Aqil, Biwen Gao, Meng Zhang, Qing Xia, Ji-Xin Cheng
Plasma Physics · Cross-listed from cond-mat.soft
Nonreciprocal interactions drive intermittent melting of Coulomb clusters
Zhicheng Shu, Wei-Chih Li, Wentao Yu et al. (+1 more)
Complex systems out of
equilibrium often experience intermittent oscillations between quiescent and highly dynamic states. While intermittency is usually driven by
stochastic noise or external forcing, energy can also be sourced from field-mediated interactions between particles, which are often nonreciprocal and effectively violate Newton's 3rd law. Here we demonstrate how nonreciprocal interactions produce intermittency in clusters of charged micron-sized particles confined in a
plasma sheath. Using three-dimensional particle tracking, we observe that vertical oscillations, induced by a noisy
plasma environment, are parametrically coupled to the horizontal vibrational modes. Experiments and simulations show that nonreciprocal interactions strongly amplify this coupling, injecting energy into the system. This mechanism triggers explosive melting transitions from an ordered cluster to an ergodic gas-like state, and leads to intermittent switching between states over long time. Overall, our work identifies nonreciprocal interactions as a key mechanism through which strongly-coupled finite systems transform interaction-mediated activity into dynamical nonequilibrium states.
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All authors Zhicheng Shu, Wei-Chih Li, Wentao Yu, Justin C. Burton
Fluid Dynamics
Stochastic Lorenz dynamics and wind reversals in Rayleigh-Bénard Convection
Yanni Bills and J. S. Wettlaufer
The Lorenz equations [1] are a severe Galerkin-truncation of the Oberbeck-Boussinesq (OB) equations describing Rayleigh-Bénard convection (RBC). Here we examine the mathematical connections between the chaotic lobe-switching behavior of a
stochastic form of the Lorenz equations, that model the interaction between the thermal boundary layers and the core circulation, and the mean wind reversals in the experiments of Sreenivasan et al. [2]. Long-time numerical simulations of these
stochastic equations, not easily accessible with the OB equations, yield a probability distribution for lobe inter-switch timings that exhibits non-Gaussian, multifractal behavior. In the Gaussian frequency range the simulations mirror the laboratory measurements and the classical Hurst exponent and quadratic variation show Brownian second-moment statistics. Further scrutiny reveals a non-linear cumulant generating function, or moment-exponent function, and thus multifractality. A simple generalized two-scale Cantor-cascade analysis reproduces these properties, showing that multiplicative intermittency, characteristic of
turbulence, strongly influences the statistics. This demonstrates that this
stochastic Lorenz system is a faithful, low-dimensional surrogate for mean-wind reversals in RBC.
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Computational Physics
Finding Molecules with Specific Properties: Simulated Annealing vs. Evolution
Dominic Mashak and S.A. Alexander
We compare the ability of a simulated annealing program and an evolutionary
algorithm to find molecules with large molecular average hyperpolarizabilities. This property is an important component of nonlinear optical materials. Both optimization programs represent molecules as SMILES strings, a method that is widely used by chemists to describe molecular structure using short ASCII strings. Our results suggest that both approaches are comparable and can be used to solve a variety of more realistic problems of interest to chemists and material scientists.
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Computational Physics
Multi-Objective Evolutionary Design of Molecules with Enhanced Nonlinear Optical Properties
Dominic Mashak, Jacob Schrum and S.A. Alexander
Nonlinear optical (NLO) materials are essential for many photonic, telecommunication, and
laser technologies, yet discovering better NLO molecules is computationally challenging due to the vast chemical space and competing objectives. We compare evolutionary
algorithms for molecular design, targeting four objectives: maximizing the ratio of first-to-second hyperpolarizability $(\beta/\gamma)$, optimizing HOMO-LUMO gap and linear polarizability to target ranges, and minimizing energy per atom. We encode molecules as SMILES strings and evaluate their properties using
quantum-chemical calculations. We compare NSGA-II, MAP-Elites, MOME, a single-objective $(\mu+\lambda)$ evolutionary
algorithm, and simulated annealing. Quality diversity methods maintain archives across a measure space defined by atom and bond count, enabling the discovery of structurally diverse molecules. Our results demonstrate that NSGA-II consistently earns high scores in every objective, leading to high-quality molecules, but MOME does a better job exploring a wide range of possibilities, resulting in higher global hypervolume and MOQD scores. However, each method has strengths and weaknesses, and produced many promising molecules.
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Computational Physics · Cross-listed from quant-ph
Dynamic Synaptic Modulation of LMG Qubits populations in a Bio-Inspired Quantum Brain
J. J. Torres and E. Romera
We present a biologically inspired
quantum neural network that encodes neuronal populations as fully connected
qubits governed by the Lipkin-Meshkov-Glick (LMG)
quantum Hamiltonian and modulated by a synaptic-efficacy feedback implementing activity-dependent changes in the collective time scale. The framework links collective
quantum many-body modes and collective-state structure to population homeostasis and rhythmogenesis, outlining scalable computational primitives long-lived operating regimes, activity-dependent oscillation periods, and size-dependent robustness that position LMG-based architectures as promising blueprints for bio-inspired
quantum brains on future
quantum hardware.
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Computational Physics
Zhong-Can Ou-Yang and Tao Xu
Biomembranes, primarily composed of lipid
bilayers, are not merely passive barriers but dynamic and complex materials whose shapes are governed by the principles of soft matter physics. This review explores the shape problem in biomembranes through the lens of material science and
liquid crystal theory. Beginning with classical analogies to
crystals and soap bubbles, it details the application of the Helfrich elastic model to explain the biconcave shape of red blood
cells. The discussion extends to multi-layer systems, drawing parallels between the focal conic structures of smectic
liquid crystals, the geometries of fullerenes and carbon nanotubes, and the reversible transitions in peptide assemblies. Furthermore, it examines icosahedral self-assemblies and shape formation in two-dimensional lipid monolayers at air/water interfaces. At the end of the paper, we find that the shapes such as cylinders, spheres,
tori, biconcave discoids and Delaunay surfaces form a group. This result is merely an intrinsic
geometric feature of these shapes and is independent of the biomembrane equation. When the pressure on the membrane, surface tension, and bending modules meet certain conditions, the biomembrane will take on these shapes. The review concludes by highlighting the unifying power of continuum elastic theories in describing a vast array of membrane morphologies across biological and synthetic systems.
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Medical Physics
Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods
Tanxin Zhu, Emran Hossen, Chen Zhao et al. (+4 more)
Purpose of Review Imaging derived fractional flow reserve (FFR) is rapidly evolving beyond conventional computational fluid dynamics (CFD) based pipelines toward machine learning (ML), deep learning (DL), and physics informed approaches that enable fast, wire free, and scalable functional assessment of coronary artery stenosis. This review synthesizes recent advances in computed
tomography (CT)- and angiography-based FFR measurement, with particular emphasis on emerging physics-informed neural networks and neural operators (PINNs and PINOs), as well as key considerations for their clinical translation. Recent Findings ML/DL approaches have markedly improved automation and computational speed, enabling prediction of pressure and FFR from anatomical descriptors or angiographic contrast dynamics. However, their real-world performance and generalizability can remain variable and sensitive to domain shift, due to multi-center heterogeneity, interpretability challenges, and differences in acquisition protocols and image quality. Physics informed learning introduces conservation structure and boundary condition consistency into model training, improving generalizability and reducing dependence on dense supervision while maintaining rapid inference. Recent evaluation trends increasingly highlight deployment oriented metrics, including calibration, uncertainty quantification, and quality control gatekeeping, as essential for safe clinical use. Summary The field is converging toward imaging derived FFR methods that are faster, more automated, and more reliable. While ML/DL offers substantial efficiency gains, physics informed frameworks such as PINNs and PINOs may provide a more robust balance between speed and physical consistency. Prospective multi center validation and standardized evaluation will be critical to support broad and safe clinical adoption.
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All authors Tanxin Zhu, Emran Hossen, Chen Zhao, Jingfeng Jiang, Michele Esposito, Jiguang Sun, Weihua Zhou
Atomic Physics
Tensor Polarizability of the Nucleus and Angular Mixing in Muonic Deuterium
G. S. Adkins and U. D. Jentschura
We investigate the effects of the tensor polarizability of a
nucleus on the bound-state energy levels, and obtain a general formula for the contribution of the tensor polarizability to the energy levels in two-body bound systems. In particular, it is demonstrated that the tensor polarizability leads to mixing between states with different orbital angular momenta. The effect of tensor polarizability is evaluated for the hyperfine-structure components of P states and for the mixing of S and D states in muonic deuterium.
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Geophysics
An Interpretable Physics Informed Multi-Stream Deep Learning Architecture for the Discrimination between Earthquake, Quarry Blast and Noise
Nishtha Srivastava, Johannes Faber and Dhruv Aditya Srivastava
The reliable discrimination of tectonic earthquakes from anthropogenic quarry blasts and transient noise remains a critical challenge in single station seismic monitoring. In this study, we introduce a novel Physics Informed Convolutional Recurrent Neural Network (PI CRNN) that embeds seismological domain knowledge directly into the feature extraction and learning process. We adapt a multistream architecture with three parallel encoders: (i) Time Domain: SincNet Encoder, (ii) MultiResolution Spectrogram branch, and, (iii) Physics Branch, followed by a fusion and a bidirectionalLSTM module. Evaluated on the Curated Pacific Northwest AI ready Seismic
Dataset, the PI CRNN achieves an overall classification accuracy of 97.56 percent on an independent test set. It outperforms a standard CRNN baseline, a classical P to S amplitude ratio method, and a Physics Informed Neural Network (PINN) that enforces physical constraints via the loss function. Furthermore, the model demonstrates perfect precision in noise rejection (100 percent Recall). Interpretability analysis using saliency maps confirms that the architecture successfully learns distinct physical signatures, identifying bimodal P- and S-wave arrivals for earthquakes versus singular impulsive onsets for blasts. This work establishes a scalable, transparent framework for AI-driven seismology, proving that architectural inductive bias provides an alternative reliable approach compared to purely data-driven approaches.
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Instrumentation and Detectors
Properties of a compact neutron supermirror transmission polarizer with an electromagnetic system
V.G. Syromyatnikov, S. Yu. Semenikhin and M.V. Lasitsa
The paper will present SVAROG, a compact
neutron supermirror transmission multichannel polarizer with an electromagnetic system. The basic properties of this polarizer will be considered. Variants for using this polarizer in experimenrtla facilities of the PIK research reactor (Petersburg
Nuclear Physics Institute of National Research Centre {\guillemotleft}Kurchatov Institute{\guillemotright} (NRC {\guillemotleft}Kurchatov Institute{\guillemotright} - PNPI)) will be discussed and a comparison of the considered polarizer with known
neutron transmission polarizers will be carried out.
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Instrumentation and Detectors
Memristive tabular variational autoencoder for compression of analog data in high energy physics
Rajat Gupta, Yuvaraj Elangovan, Tae Min Hong et al. (+5 more)
We present an implementation of edge AI to compress data on an in-
memory analog content-addressable
memory (ACAM) device. A variational autoencoder is trained on a simulated sample of energy measurements from incident high-energy electrons on a generic three-layer scintillator-based calorimeter. The encoding part is distilled into tabular format by regressing the latent space variables using decision trees, which is then programmed on a memristor-based ACAM. In real-time, the ACAM compresses 48 continuously valued incoming energies measured by the calorimeter sensors into the latent space, achieving a compression factor of 12x, which is transmitted off-detector for decompression. The performance result of the ACAM, obtained using the Structural Simulation Toolkit, the SST open source framework, gives a latency value of 24 ns and a throughput of 330M compressions per second, i.e., 3 ns between successive inputs, and an average energy consumption of 4.1 nJ per compression.
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All authors Rajat Gupta, Yuvaraj Elangovan, Tae Min Hong, James Ignowski, John Moon, Aishwarya Natarajan, Stephen Roche, Luca Buonanno
Instrumentation and Detectors
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes et al. (+31 more)
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large
Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated
circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.
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All authors Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes, Shiqi Kuang, Ana Sofía Calle Muñoz, Danush Shekar, Jennet Dickinson, Giuseppe Di Guglielmo, Lindsey Gray, Mia Liu, Corrinne Mills, Mark S. Neubauer, Daniel Abadjiev, Doug Berry, Karri DiPetrillo, Farah Fahim, Abhijith Gandrakota, Harshul Gupta, James Hirschauer, Eliza Howard, Ron Lipton, Petar Maksimovic, Nick Manganelli, Benjamin Parpillon, Jannicke Pearkes, Ricardo Silvestre, Morris Swartz, Chinar Syal, Nhan Tran, Amit Trivedi, Keith Ulmer, Mohammad Abrar Wadud, Benjamin Weiss, Eric You
Plasma Physics
Particle-in-Cell Methods for Simulations of Sheared, Expanding, or Escaping Astrophysical Plasma
Fabio Bacchini, Evgeny A. Gorbunov, Maximilien Péters de Bonhome et al. (+4 more)
Particle-in-
Cell (PIC) methods have achieved widespread recognition as simple and flexible approaches to model collisionless
plasma physics in fully kinetic simulations of astrophysical environments. However, in many situations the standard PIC
algorithm must be extended to include macroscopic effects in microscale simulations. For
plasmas subjected to shearing or expansion, shearing-box and expanding-box methods can be incorporated into PIC to account for these global effects. For
plasmas subjected to local acceleration in confined regions of space, a leaky-box method can allow closed-box PIC simulations to account for particle escape from the accelerator region. In this work, we review and improve methods to include shearing, expansion, and escape in PIC simulations. We provide the numerical details of how Maxwell's equations and the particle equations of motion are solved in each case, and introduce generalized Boris-like particle pushers to solve the momentum equation in the presence of extra forces. This work is intended to serve as a comprehensive reference for the implementation of shearing-box, expanding-box, and leaky-box
algorithms in PIC.
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All authors Fabio Bacchini, Evgeny A. Gorbunov, Maximilien Péters de Bonhome, Paul Els, Konstantinos-Xanthos Argyropoulos, Minh Nhat Ly, Daniel Grošelj
Atomic Physics · Cross-listed from quant-ph
Robustness of Kardar-Parisi-Zhang-like transport in long-range interacting quantum spin chains
Sajant Anand, Jack Kemp, Julia Wei et al. (+3 more)
Isotropic integrable
spin chains such as the Heisenberg model feature superdiffusive
spin transport belonging to an as-yet-unidentified dynamical universality class closely related to that of Kardar, Parisi, and Zhang (KPZ). To determine whether these results extend to more generic one-dimensional models, particularly those realizable in
quantum simulators, we investigate
spin and energy transport in non-integrable, long-range Heisenberg models using state-of-the-art tensor network methods. Despite the lack of integrability and the asymptotic expectation of
diffusion, for power-law models (with exponent $2 < \alpha < \infty$) we observe long-lived $z=3/2$ superdiffusive
spin transport and two-point correlators consistent with KPZ scaling functions, up to times $t \sim 10^3/J$. We
conjecture that this KPZ-like transport is due to the proximity of such power-law-interacting models to the integrable family of Inozemtsev models, which we show to also exhibit KPZ-like
spin transport across all interaction ranges. Finally, we consider anisotropic
spin models naturally realized in Rydberg atom arrays and ultracold polar molecules, demonstrating that a wide range of long-lived, non-diffusive transport can be observed in experimental settings.
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All authors Sajant Anand, Jack Kemp, Julia Wei, Christopher David White, Michael P. Zaletel, Norman Y. Yao
Atmospheric and Oceanic Physics
Ensemble-size-dependence of deep-learning post-processing methods that minimize an (un)fair score: motivating examples and a proof-of-concept solution
Christopher David Roberts
Fair scores reward ensemble forecast members that behave like samples from the same distribution as the verifying observations. They are therefore an attractive choice as loss functions to train data-driven ensemble forecasts or post-processing methods when large training ensembles are either unavailable or computationally prohibitive. The adjusted continuous ranked probability score (aCRPS) is a fair and ensemble-size-unbiased scoring rule when applied to ensembles with forecast members that are interpretable as conditionally independent draws from an underlying predictive distribution. However, distribution-aware post-processing methods that introduce structural dependency between members can violate this assumption, such that aCRPS no longer functions as a fair scoring rule. We demonstrate this effect using two approaches designed to minimize the expected aCRPS of a finite ensemble: (1) a linear member-by-member calibration, which couples members through a common dependency on the sample ensemble mean, and (2) a deep-learning method, which couples members via
transformer self-
attention across the ensemble dimension. In both cases, the results are sensitive to ensemble size and apparent gains in aCRPS can correspond to systematic unreliability characterized by over-dispersion. We introduce trajectory
transformers as a proof-of-concept that ensemble-size independence can be achieved. This approach is an adaptation of the Post-processing Ensembles with
Transformers (PoET) framework and applies self-
attention over lead time while preserving the conditional independence required by aCRPS. When applied to weekly mean $T_{2m}$ forecasts from the ECMWF subseasonal
forecasting system, this approach successfully reduces systematic model biases whilst also improving or maintaining forecast reliability regardless of the ensemble size used in training or real-time forecasts.
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Optics
Tunable microwave frequency synthesis with optically-derived spectral purity
James Greenberg, Scott C. Egbert, William F. McGrew et al. (+2 more)
Microwave synthesizers are central to test and measurement systems across applications including wireless communications, radar, spectroscopy, and time and frequency metrology. State-of-the-art microwave sources, however, are fundamentally constrained by trade-offs between frequency tunability and spectral purity. Electro-optic frequency division (eOFD) is an emerging technique for dividing down the purity of optical sources to the microwave domain. Previously reported eOFD-based synthesizers generally have limited tunability due to feedback stabilization requirements. Here we demonstrate a feed-forward eOFD architecture in which the frequency tunability of a microwave source is preserved while optical spectral purity is divided through feed-forward cancellation, without any downstream electronic frequency synthesis. By canceling the phase noise of the microwave source without feedback, this eOFD approach removes loop bandwidth and source noise constraints observed in prior eOFD architectures. We achieve octave-spanning tunability, including the entire X-band, with phase noise below -140 dBc/Hz at kilohertz offsets and a high-frequency noise floor between -155 dBc/Hz and -145 dBc/Hz for carrier frequencies from 8 to 16 GHz. This performance corresponds to single-femtosecond integrated timing jitter, enabling, to our knowledge, the first demonstration of coherent, optically referenced microwave synthesis under wide tuning with this level of spectral purity.
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All authors James Greenberg, Scott C. Egbert, William F. McGrew, Brendan M. Heffernan, Antoine Rolland
Chemical Physics
Extending numerical simulations in SIMPSON: Electron paramagnetic resonance, dynamic nuclear polarisation, propagator splitting, pulse transients, and quadrupolar cross terms
David L. Goodwin, Jose P. Carvalho, Anders B. Nielsen et al. (+4 more)
Aimed at the simulation, design, and interpretation of advanced pulse experiments crossing the boundaries between
nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR), including the rapidly emerging, hybrid discipline of pulsed dynamic
nuclear polarisation (DNP), we present a host of novel features in the widely used SIMPSON software package addressing these aspects. Along with this come new features for advanced pulse sequence evaluation in terms of propagator splitting, high-order
spin operator cross terms, and pulse phase transients. These fundamental new tools are introduced in a C++-based next generation of the SIMPSON software, which improves calculations speed in some aspects, is better prepared for further developments, and facilitates easier community contributions to the open-source software package.
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All authors David L. Goodwin, Jose P. Carvalho, Anders B. Nielsen, Nino Wili, Thomas Vosegaard, Zdenek Tosner, Niels Chr. Nielsen
Data Analysis and Statistics · Cross-listed from hep-ex
Neural Scaling Laws for Boosted Jet Tagging
Matthias Vigl, Nicole Hartman, Michael Kagan et al. (+1 more)
The success of Large
Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and
dataset size, is the primary driver of performance in modern machine learning. While machine learning has long been an integral component of High Energy Physics (HEP) data analysis workflows, the compute used to train state-of-the-art HEP models remains orders of magnitude below that of industry foundation models. With scaling laws only beginning to be studied in the field, we investigate neural scaling laws for boosted jet classification using the public JetClass
dataset. We derive compute optimal scaling laws and identify an effective performance limit that can be consistently approached through increased compute. We study how data repetition, common in HEP where simulation is expensive, modifies the scaling yielding a quantifiable effective
dataset size gain. We then study how the scaling coefficients and asymptotic performance limits vary with the choice of input features and particle multiplicity, demonstrating that increased compute reliably drives performance toward an asymptotic limit, and that more expressive, lower-level features can raise the performance limit and improve results at fixed
dataset size.
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All authors Matthias Vigl, Nicole Hartman, Michael Kagan, Lukas Heinrich
Data Analysis and Statistics · Cross-listed from astro-ph.IM
Deep Learning for Point Spread Function Modeling in Cosmology
Dayana Andrea Henao Arbeláez, Pierre-François Léget and Andrés Alejandro Plazas Malagón
We present the development of a data-driven, AI-based model of the Point Spread Function (PSF) that achieves higher accuracy than the current state-of-the-art approach, "PSF in the Full Field-of-View” (PIFF). PIFF is widely used in leading weak-
lensing surveys, including the Dark Energy Survey (DES), the Hyper Suprime-Cam (HSC) Survey, and the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The PSF characterizes how a point source, such as a
star, is imaged after its light traverses the atmosphere and
telescope optics, effectively representing the "blurred fingerprint” of the entire imaging system. Accurate PSF modeling is essential for weak gravitational
lensing analyses, as biases in its estimation propagate directly into
cosmic shear measurements – one of the primary cosmological probes of the expansion history of the Universe and the growth of large-scale structure for dark energy studies. To address the limitations of PIFF, which constructs PSF models independently for each CCD and therefore loses spatial coherence across the focal plane, we introduce a deep-learning-based framework for PSF reconstruction. In this approach, an autoencoder is trained on
stellar images obtained with the Hyper Suprime-Cam (HSC) of the Subaru
Telescope and combined with a Gaussian process to interpolate the PSF across the
telescope's full field of view. This hybrid model captures systematic variations across the focal plane and achieves a reconstruction error of $3.4 \times 10^{-6}$ compared to PIFF's $3.7 \times 10^{-6}$, laying the foundation for integration into the LSST Science Pipelines.
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Accelerator Physics
New Challenges in Plasma Accelerators: Final Focusing for Wakefield Colliders
Keegan Downham, Spencer Gessner, Lewis Kennedy et al. (+2 more)
The focusing of particle beams for
collider experiments is crucial for maximizing the luminosity and thus the discovery potential of these machines. In recent years,
plasma wakefield acceleration has emerged as a leading candidate for achieving higher energy collisions with smaller facility footprints due to the large accelerating gradients in the
plasma. This higher beam energy poses significant challenges for the final focusing system of the
collider. Here, we discuss the various challenges of final focusing for TeV-scale
plasma accelerators and propose possible solutions. Finally, we present the first design of a final focusing system for a 10 TeV linear wakefield
collider, evaluate its performance, and discuss its shortcomings as well as improvements for future designs.
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All authors Keegan Downham, Spencer Gessner, Lewis Kennedy, Rogelio Tomás, Andrei Seryi
Atmospheric and Oceanic Physics
Southern Ocean latent heat flux variability driven by oceanic meso- and submesoscale motions
Lucie Reymondet, Lia Siegelman and Luc Lenain
Latent heat flux is a primary pathway for ocean-atmosphere exchange of heat and moisture, yet the influence of sea surface temperature variability at fine scales ($\leq$ 100 km) on latent heat flux variability, particularly over the Southern Ocean, remains poorly understood. Here we quantify the scale-dependent drivers of latent heat flux (LHF) variability using a year-long, global, fully coupled ocean-atmosphere simulation with kilometer-scale resolution. Annual-mean LHF in eddy-rich regions reaches $\approx$ 215 W m$^{-2}$, approximately three times larger than in eddy-poor regions. Spectral analyses show that ocean mesoscale [$\mathcal{O}$(100 km)] and submesoscale [$\mathcal{O}$(1-10 km)] variability accounts for up to $\approx$ 80% of the total LHF variance in eddy-rich sectors, but as little as 10% in eddy-poor regions, and increases proportionally with eddy kinetic energy and sea surface temperature (SST) variance. We also find that strong submesoscale SST fronts ($\approx$ 5 $^\circ$C over 10 km) force a localized secondary circulation that extends well above the marine boundary layer into the mid-troposphere. Comparison with ERA5 shows that fine ocean scales, responsible for about 17% of the ocean-driven LHF variance in the simulation, are largely unresolved in the reanalysis, leading to a muted atmospheric response lacking any secondary circulation. Despite a strong heterogeneity in LHF variability, the atmospheric dynamics are mostly uniform across the domain, suggesting a non local atmospheric response to ocean forcing. These results highlight the potential for ocean meso- and submesoscales, commonly under-resolved in
climate models and reanalysis, to influence Southern Ocean air-sea coupling and atmosphere both locally and remotely.
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Optics
PRISM: Photonics-Informed Inverse Lithography for Manufacturable Inverse-Designed Photonic Integrated Circuits
Hongjian Zhou, Haoyu Yang, Nicholas Gangi et al. (+3 more)
Recent advances in photonic inverse design have demonstrated the ability to automatically synthesize compact, high-performance photonic components that surpass conventional, hand-designed structures, offering a promising path toward scalable and functionality-rich photonic hardware. However, the practical deployment of inverse-designed PICs is bottlenecked by manufacturability: their irregular, subwavelength geometries are highly sensitive to fabrication variations, leading to large performance degradation, low yield, and a persistent gap between simulated optimality and fabricated performance. Unlike electronics, photonics lacks a systematic, flexible mask optimization flow. Fabrication deviations in photonic components cause large optical response drift and compounding error in cascaded
circuits, while calibrating fabrication models remains costly and expertise-heavy, often requiring repeated fabrication cycles that are inaccessible to most designers. To bridge this gap, we introduce PRISM, a photonics-informed inverse lithography workflow that makes photonic mask optimization data-efficient, reliable, and optics-informed. PRISM (i) synthesizes compact, informative calibration patterns to minimize required fabrication data, (ii) trains a physics-grounded differentiable fabrication model, enabling gradient-based optimization, and (iii) performs photonics-informed inverse mask optimization that prioritizes performance-critical features beyond geometry matching. Across multiple inverse-designed components with both electron-beam lithography and deep ultra-violet photolithography processes, PRISM significantly boosts post-fabrication performance and yield while reducing calibration area and turnaround time, enabling and democratizing manufacturable and high-yield inverse-designed photonic hardware at scale.
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All authors Hongjian Zhou, Haoyu Yang, Nicholas Gangi, Tianle Xu, Rena Huang, Jiaqi Gu
Applied Physics · Cross-listed from cond-mat.mes-hall
Polarization-resolved measurement of forward volume spin waves by micro-focused Brillouin light scattering
Krzysztof Szulc, Mengying Guo, Ondřej Wojewoda et al. (+7 more)
We show how the micro-focused BLS signal of forward volume
spin waves is formed and why it remains observable despite
symmetry-based "suppression" expectations. A reciprocity-
theorem based model with vectorial diffraction-limited focusing identifies the nonnegligible longitudinal focal-field component as the key element responsible for BLS sensitivity in the forward volume geometry. We further demonstrate that full polarization analysis, implemented through polarizer-analyzer maps of coherently excited
spin waves, provides information beyond the conventional crossed polarizer-analyzer readout. In a BiYIG thin film, the measured maps exhibit Stokes/anti-Stokes polarization asymmetries and nontrivial patterns that stem from quadratic magneto-optical coupling terms. Fitting the data with a model including Voigt and Cotton-Mouton contributions yields an effective Cotton-Mouton constant and shows that the quadratic response is comparable to the linear Voigt contribution.
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All authors Krzysztof Szulc, Mengying Guo, Ondřej Wojewoda, Hongyu Wang, Dominik Pavelka, Jan Klíma, Jakub Krčma, Xiufeng Han, Qi Wang, Michal Urbánek
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Three-Dimensional Optical-Electrical Simulation of Cs2AgBiBr6 Double Perovskite Solar Cells
Md Shanian Moed, Adnan Amin Siddiquee and Md Tashfiq Bin Kashem
Despite significant advances in lead-free perovskite photovoltaics, achieving a balance among environmental safety and high optoelectronic performance remains challenging. The inorganic double perovskite Cs2AgBiBr6 has emerged as a promising candidate owing to its robust three-dimensional
crystal structure and suitable visible-range bandgap. However, best power conversion efficiencies (PCEs) for Cs2AgBiBr6
solar cells reported so far - 6.37% experimentally and 27.78% in numerical studies - remain below the theoretical performance potential, largely due to suboptimal charge transport layers, and interface-related recombination losses. Here, we address this gap using a 3D finite-element method (FEM) implemented in COMSOL Multiphysics, which couples optical simulations with
semiconductor drift-
diffusion transport. To our knowledge, this work represents the first comprehensive 3D FEM-based study of a double halide perovskite
solar cell. Screening of 25 electron transport layer (ETL)-hole transport layer (HTL) combinations identifies CeO2 and P3HT as the optimal ETL and HTL respectively. Device performance is further analyzed through systematic variation of layer thicknesses, doping concentrations and defect densities within the FTO/CeO2/Cs2AgBiBr6/P3HT/Au architecture. Under optimized parameters, the simulated device achieves a PCE of 31.76%, representing the theoretical upper bound predicted by the model. Overall, this work demonstrates 3D physics-based device engineering as a decisive pathway for overcoming efficiency bottlenecks in lead-free double perovskite photovoltaics.
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Chemical Physics
How to Train a Shallow Ensemble
Moritz Schäfer, Matthias Kellner, Johannes Kästner et al. (+1 more)
Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different ensemble members share a large part of the model weights. In this work, we systematically investigate training strategies for shallow ensembles to balance calibration performance with computational cost. We first demonstrate that explicit optimization of a negative log-likelihood (NLL) loss improves calibration with respect to approaches based on ensembles of randomly initialized models, or on a last-layer Laplace approximation. However, models trained solely on energy objectives yield miscalibrated force estimates. We show that explicitly modeling force uncertainties via an NLL objective is essential for reliable calibration, though it typically incurs a significant computational overhead. To address this, we validate an efficient protocol: full-model fine-tuning of a shallow ensemble originally trained with a probabilistic energy loss, or one sampled from the Laplace posterior. This approach results in negligible reduction in calibration quality compared to training from scratch, while reducing training time by up to 96%. We evaluate this protocol across a diverse range of materials, including amorphous carbon, ionic
liquids (BMIM),
liquid water (H$_2$O), barium titanate (BaTiO$_3$), and a model tetrapeptide (Ac-Ala3-NHMe), establishing practical guidelines for reliable uncertainty quantification in atomistic machine learning.
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All authors Moritz Schäfer, Matthias Kellner, Johannes Kästner, Michele Ceriotti
Fluid Dynamics
Effect of flexibility on the pitch-heave flutter instability of a flexible foil elastically supported on its leading edge
Ramon Fernandez-Feria
An analytical tool is presented to compute the parametric regions of flutter instabilities of a two-dimensional flexible foil elastically mounted. It is based on a new analytical formulation of the unsteady fluid-estructure interaction valid for small-amplitude oscillations and deformations of the foil immersed in an inviscid fluid. The formulation extends a previous analysis by including the effects of
gravity and a second flexural mode, increasing its validity range to much smaller
rigidities. The analytical results are validated with available numerical results, capturing the first two natural flexural modes down to values of the stiffness parameter $S$ of order $10^{-1}$. When only passive heave, or only passive pitch, is allowed, the rigid foil is stable, existing an upper stiffness bound for the flexural instabilities, wich become coupled with the spring instability mode for small spring constant increasing the growth rate. These coupled spring (linear or torsional) and flexural instability modes occur below a threshold value of $S$ and above a threshold value of $R$, both depending on the corresponding spring constant. Coupled pitch-heave flutter instabilities of a rigid foil occur in a region below a curve of the parametric plane of the two springs constants that depends on $R$, which shrinks to zero as $R$ decreases. For a flexible foil, the flexural unstable modes become coupled with the springs unstable mode as $S$ decreases from infinity, enlarging the mass ratio range for flutter instability and increasing its growth rate, the more so the smaller the springs constants. The parametric regions for flutter instabilities are easily characterized with the present analytical tool, providing the corresponding frequency and critical flutter velocity. The present results can be useful as a guide in the design of future turbines based on flexible oscillating foils.
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