Optics
Normal form computation of nonlinear dispersion relationship for locally resonant metamaterial
Tao Wang, Cyril Touzé, Haiqin Li et al. (+1 more)
This article is devoted to the application of the parametrisation method for invariant
manifold with a complex normal form style (CNF), for the derivation of high-order approximations of underdamped nonlinear dispersion relationships for periodic structures, more specifically by considering the case of a locally resonant metamaterial chain incorporating damping and various nonlinear stiffnesses. Two different strategies are proposed to solve the problem. In the first one, Bloch's assumption is first applied to the equations of motion, and then the nonlinear change of coordinates provided by the complex normal form style in the parametrisation method is applied. This direct procedure, which applies first the wave dependency to the original physical coordinates of the problem, is referred to as CNF-BP (for CNF applied with Bloch's assumption on physical coordinates). In the second strategy, the nonlinear change of coordinates provided by the parametrisation method, which relates the physical coordinates to the so-called normal coordinates, is first applied. Then the periodic assumption is used, thus imposing a Bloch wave ansatz on the normal coordinates. This method will be referred to as CNF-PN (for CNF with a periodic assumption on normal coordinates). In the conservative case, the CNF-PN strategy exhibits superior capability in capturing complex wave propagation phenomena, whereas the CNF-BP strategy encounters limitations in handling non-fundamental harmonics and the nonlinear interactions between host oscillators. For underdamped systems, the CNF-PN is rigorously validated and systematically compared against numerical techniques, a classical analytical perturbation technique (the method of multiple scales), and direct numerical time integration of annular chain structures.
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All authors Tao Wang, Cyril Touzé, Haiqin Li, Qian Ding
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.SR
Solar neutron and muon detection on November 11, 2025: First simultaneous recovery of energy spectra
A.Chilingarian, B.Sargsyan, L.Kozliner et al. (+1 more)
Ground Level Enhancement (GLE) events provide rare opportunities to study high-energy
solar particle acceleration through direct detection of secondary radiation at ground level. On November 11, 2025, the Aragats
Solar Neutron Telescope (ASNT) recorded a statistically significant increase in high-energy
neutron and
muon fluxes associated with an X5.1
flare and the subsequent
Solar Energetic
Proton (SEP) event. The event displayed a unique dual-peak profile: an initial hard component at 10 28 UT, followed by a softer yet still energetic peak at 10 45 UT. For the first time, we report simultaneous energy spectra of atmospheric
neutrons and
muons measured in the 10 600 MeV range at Aragats. Broken-power-law fits reveal a clear temporal
evolution of acceleration conditions, evidenced by spectral indices declining with energy. These findings highlight the unique capabilities of the ASNT as an instrument for studying extreme
solar particle acceleration.
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All authors A.Chilingarian, B.Sargsyan, L.Kozliner, T.Karapetyan
Instrumentation and Detectors
Quenching factors for Na recoils as a function of Tl dopant concentrations in NaI(Tl) crystals
G. Angloher, M. R. Bharadwaj, A. Böhmer et al. (+51 more)
Thallium-doped sodium iodide (NaI(Tl)) scintillation detectors play an important role in the field of direct dark matter (DM) searches. The DAMA/LIBRA experiment stands out for its reported observation of an annually modulating DM-like signal, which is in direct contrast with other results. To accurately calibrate the energies of
nuclear recoil signals with electron
recoils, precise measurements of the quenching factor of the NaI(Tl)
crystals are essential, as the two processes have different scintillation light yield. In this article, we present results of a systematic study carried out by the COSINUS collaboration and Duke University to measure the quenching factor of sodium (Na)
recoils as a function of
nuclear recoil energy and for differing Thallium (Tl) dopant concentrations in the bulk
crystal. Five ultrapure NaI(Tl)
crystals, manufactured by the Shanghai Institute for Ceramics, were irradiated with a quasi-monoenergetic
neutron beam at the Triangle Universities
Nuclear Laboratory, North Carolina, USA. The quenching factor for low
nuclear recoil energies of 5-26keV$_{nr}$ was extracted for all 5
crystals. A Tl-dependence could be deduced with a proportional response calibration schema using a $^{241}$Am source. However, this effect was not observed when using a low-energy calibration line from $^{133}$Ba.
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All authors G. Angloher, M. R. Bharadwaj, A. Böhmer, M. Cababie, I. Colantoni, I. Dafinei, N. Di Marco, C. Dittmar, L. Einfalt, F. Ferrella, F. Ferroni, S. Fichtinger, A. Filipponi, T. Frank, M. Friedl, M. Gapp, L. Gai, Z. Ge, M. Heikinheimo, M. N. Hughes, K. Huitu, M. Kellermann, R. Maji, M. Mancuso, L. Pagnanini, F. Petricca, S. Pirro, F. Pröbst, G. Profeta, A. Puiu, F. Reindl, K. Schäffner, J. Schieck, P. Schreiner, C. Schwertner, K. Shera, M. Stahlberg, A. Stendahl, M. Stukel, C. Tresca, F. Wagner, S. Yue, V. Zema, Y. Zhu, P.S. Barbeau, S.C. Hedges, C. Awe, J. Runge, T. Johnson, D.M. Markoff, P. An, C. G. Prior, A. Bracho, S. Alawabdeh
Chemical Physics
Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction
Mikhail Tsitsvero, Atsuyuki Nakao and Hisaki Ikebata
Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing data in patents and literature is heterogeneous and difficult to use. We introduce a universal directed-tree process-graph representation that unifies unstructured text, molecular structures, and numeric measurements into a single machine-readable format. To learn from this structured data, we developed a multi-modal graph neural network with a property-conditioned
attention mechanism. Trained on approximately 700,000 process graphs from nearly 9,000 diverse documents, our model learns semantically rich
embeddings that generalize across domains. When fine-tuned on compact, domain-specific
datasets, the pretrained model achieves strong performance, demonstrating that universal process representations learned at scale transfer effectively to specialized prediction tasks with minimal additional data.
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General Physics
From Poincare Invariance to Gauge Theories: Yang-Mills and General Relativity
Branislav Sazdovic
This article is founded on two fundamental principles: the principle field equations introduced in Refs. \cite{S, S1, S2} and the Fock-Ivanenko covariant derivatives \cite{FI, F}. The former yields the equations of motion for free fields of arbitrary
spin and helicity. In the massless case, it also dictates that Lorentz transformations for tensor fields acquire an additional term, which takes the form of a gauge transformation \cite{W, S1}. The latter principle, the Fock-Ivanenko derivative, introduces interactions based on the intrinsic and Poincare groups. This framework allows us to recover a complete Yang-Mills theory, as well as general relativity in the connection-based formulations of Palatini and Ashtekar, both of which are theories with local gauge
symmetries. While the standard approach begins with the
symmetries of a matter action, we will instead derive dynamics directly from Poincare invariance. This perspective reveals that for free fields, Lorentz invariance induces the gauge
symmetry of massless tensors. A proper definition of these gauge transformations, in turn, requires the covariant derivatives provided by the Fock-Ivanenko approach. Considering matter fields, we derive the interacting Dirac equation in the presence of Yang-Mills and gravitational fields from its free counterpart.
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General Physics
Massive Spin-2: Field-equations, Propagators, Massless-limit, and Perihelion Precessions
Th.A. Rijken and J.W. Wagenaar
This paper presents the quantization of massive and massless
spin-2 particles, using the auxiliary field method. The issue, the so-called vDVZ-discontinuity, whether the perihelion precessions for a massive graviton are in agreement with the data, is studied in the context of this
spin-2 theory in tree-approximation. In the context of this setting, it is found, that a massive gravitation model with an imaginary scalar ghost, for a small graviton mass is compatible with the perihelion-precession of Mercury, etc..
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Data Analysis and Statistics
An Algebra Bundle for Non-Composable Coincidence Probabilities in Gamma-ray Spectroscopy
Liam L. Schmidt
Understanding
nuclear level schemes from the detection of emitted gamma-rays is, at its core, a combinatorial problem. The transition
matrix formalism that treats
nuclear decay as a Markov process is a well established treatment for the prediction of emitted and detected gamma-rays. However, with the path
algebra being the underlying
algebra of such
matrix methods, the formalism is restricted to calculating probabilities only for composable paths through the decay. This paper presents a new
algebra that allows for the calculations of non-composable coincidence probabilities offering a more complete formalism for the prediction of gamma-ray spectra. The aim of this coincidence
algebra is to provide a general treatment for predicting all full energy summing peaks for composable and non-composable transitions. Non-composable summing peaks often corrupt peaks of interest by creating doublets in the energy spectra. The coincidence
algebra serves as a tool for investigating any possible doublets produced from full energy coincidence summing. This formalism is tested for the prediction of gamma-ray peaks in the electron capture of $^{133}$Ba and compared to the results a Geant4 simulation of the decay within the GRIFFIN spectrometer. It is also shown how the path
algebra formalism may be useful for understanding uncertainty propagation in
nuclear level schemes.
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Plasma Physics
Shear Alfvén Waves in Chaotic Magnetic Fields
Matthew Thomas, Zhisong Qu and Matthew Hole
The shear Alfvén spectrum is computed in the presence of
symmetry breaking perturbations that introduce chaotic
magnetic field trajectories. Quadratic flux minimised surfaces allow the creation of pseudo straight field line coordinates in the chaotic region. With these coordinates, the reduced ideal MHD equations are cast into an
eigenvalue problem and solved numerically. The spectrum is computed with varying perturbation strength, showing how shear Alfvén waves change as increasing number of flux surfaces are destroyed. Solutions on specific flux surfaces are shown to remain relatively unchanged while the flux surface remains intact, and retain some original features at large perturbations where the flux surface is destroyed.
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Space Physics
On the Relation between Source Region and In-situ Variability of Low $\delta B$ Solar Wind Streams
Kai Jaffarove, Tamar Ervin and Stuart D. Bale
Parker
Solar Probe (PSP) observed a high speed stream near the Sun ($\sim 9.8 R_\odot$) in March 2025. As this stream was observed near the Sun, it allowed for an unparalleled opportunity to observe pristine, coronal hole wind with little
evolution (expansion, stream interaction, etc) effects impacting its properties. Through an ensemble of
magnetic connectivity analysis utilizing Potential Field Source Surface (PFSS) modeling and ballistic propagation, we make estimates of the footpoints, and corresponding source region parameters, associated with the stream. We then look at how the low variability regions (small gradient in the in-situ $B_r$) are related to the variability in the field strength at their source ($B_0$). We find that about half of these periods are associated with low source region variability. Lastly, we examine the low $\delta B / B$ periods between switchback patches and similarly find that they show statistically less variability in their associated $B_0$ value than their switchback patch counterparts. We believe that these results point to a
solar source of these “low-variability" periods, that warrants further investigation with composition diagnostics and more complex modeling techniques.
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Accelerator Physics · Cross-listed from cs.SE
Beyond Like-for-Like: A User-centered Approach to Modernizing Legacy Applications
M. Polzin and M.Guzman
When modernizing a legacy application, it is easy to fall back on a like-for-like replica with new tools and updated design stylings, but this is an opportunity to explore making a more intuitive application that supports user tasks and efficiency. Rather than having a blank canvas-unburdened by legacy tech debt-to create a new application, you are working with an existing application that is integral to accelerator operations and one that expert users are already familiar with. Due to this, you might assume people will prefer the like-for-like, but you could be carrying forward the pain points, processes that are inefficient, and ultimately wind up with an application that no one wants to use because it doesn't solve existing problems. Getting users involved can make all the difference in your approach to modernizing a legacy application that caters to both newer and expert users. It also can bridge the gap between like-for-like and introducing new GUI design. Having a legacy application doesn't have to make the modernized one difficult to develop, as the existing application is a tool in how you move forward with the new application. It provides insight into areas that an application with a clean slate doesn't give you.
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Optics
Photonic Generation and Free-Space Distribution of Millimeter Waves for Portable Optical Clocks
Dylan Meyer, Alexander Lind, William Groman et al. (+7 more)
Robust and portable optical clocks promise to bring sub-picosecond timing instability to smaller form factors, offering possible performance improvements and new scenarios for positioning and
navigation, radar technologies, and experiments probing fundamental physics. However, there are currently limited methods suitable for broadly disseminating the sub-picosecond timing signals or performing frequency comparison of these clocks–particularly over open-air paths. Established microwave time transfer techniques only offer nanosecond level time synchronization, whereas optical techniques have challenging pointing requirements and lack the capability of all-weather operation. In this paper, we explore optically derived millimeter-wave carriers as a time-frequency link for full utilization of the next generation of portable optical clocks. We introduce an architecture that synthesizes 90 GHz millimeter waves with a one second residual instability of 2x10^-15, averaging into the 10^-17 range. In addition, we demonstrate a first-of-its-kind 110 m phase-stabilized free-space frequency comparison link over a millimeter-wave band with a one second instability in the 10^-14 region. Technical and systematic uncertainties are investigated and characterized, providing a foundation for future time and frequency transfer experiments among distributed portable optical clocks.
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All authors Dylan Meyer, Alexander Lind, William Groman, Hero Trent, Carter Mashburn, Matthew Heyrich, Jeffrey Sherman, Franklyn Quinlan, Gabriel Santamaria-Botello, Scott A. Diddams
Optics · Cross-listed from quant-ph
A scalable advantage in multi-photon quantum machine learning
Yong Wang, Zhenghao Yin, Tobias Haug et al. (+6 more)
Photons are promising candidates for
quantum information technology due to their high robustness and long coherence time at room temperature. Inspired by the prosperous development of photonic computing techniques, recent research has turned
attention to performing
quantum machine learning on photonic platforms. Although
photons possess a high-dimensional
quantum feature space suitable for computation, a general understanding of how to harness it for learning tasks remains blank. Here, we establish both theoretically and experimentally a scalable advantage in
quantum machine learning with multi-
photon states. Firstly, we prove that the learning capacity of linear optical
circuits scales polynomially with the
photon number, enabling generalization from smaller training
datasets and yielding lower test loss values. Moreover, we experimentally corroborate these findings through
unitary learning and metric learning tasks, by performing online training on a fully programmable photonic integrated platform. Our work highlights the potential of photonic
quantum machine learning and paves the way for achieving
quantum enhancement in practical machine learning applications.
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All authors Yong Wang, Zhenghao Yin, Tobias Haug, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Philip Walther
Plasma Physics
Thermodynamically Consistent Vibrational-Electron Heating: Generalized Derivation for Excited State Populations
Bernard Parent and Felipe Martin Rodriguez Fuentes
Accurate prediction of electron temperature ($T_{\rm e}$) in non-
equilibrium plasma flows is critical for applications ranging from hypersonic flight to
plasma-assisted combustion. We recently proposed a thermodynamically consistent model for vibrational-electron (V-e) heating [Phys. Fluids 37, 096141 (2025)] which enforces convergence of $T_{\rm e}$ to the vibrational temperature ($T_{\rm v}$) at
equilibrium. While the original derivation assumed electron energy loss was dominated by collisions with ground-state molecules, this Letter presents a rigorous generalization of the model. We demonstrate that the heating-to-cooling ratio $\exp(\theta_{\rm v}/T_{\rm e}-\theta_{\rm v}/T_{\rm v})$ with $\theta_{\rm v}$ the characteristic vibrational temperature remains valid even when electron cooling interactions with vibrationally excited states are included. This derivation removes the previous constraint assuming ground-state dominance, thereby extending the model's validity to
plasma flows where vibrationally excited populations contribute significantly to electron cooling.
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Applied Physics
On the Generation of Phononic Frequency Combs Using Defect Modes of Phononic Crystals
Suhas Suresh Bharadwaj, Murtaza Rangwala and Adarsh Ganesan
This paper proposes a method for generating phononic frequency combs (PFCs) using defect-localized modes in a two-dimensional hexagonal phononic
crystal. Localized vibration modes from a singular point defect produce evenly spaced spectral lines corresponding to PFCs. Numerical modelling reveals robust energy transfer under a single-tone drive, generating spectral sidebands. These results demonstrate defect engineering in phononic
crystals as a tunable platform for PFC generation with significant applications in high-resolution sensing, timing, and
quantum-acoustic technologies.
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Plasma Physics
FPGA-Accelerated Real-Time Beam Emission Spectroscopy Diagnostics at DIII-D Using the SLAC Neural Network Library for ML Inference
Abhilasha Dave, James Russell, Mudit Mishra et al. (+9 more)
Achieving reliable real-time control of tokamak
plasmas is essential for sustaining high-performance operation in next-generation fusion reactors. A major challenge is the accurate and timely prediction of edge-localized modes (ELMs), especially in high-confinement regimes such as wide-pedestal quiescent H-mode. We present a hardware-accelerated machine learning (ML) inference system integrated into the RTSTAB processing node of the DIII-D real-time diagnostic and control infrastructure. The system uses an AMD/Xilinx KCU1500 FPGA to enable ultra low latency
plasma state classification and ELM
forecasting. Input features come from real-time Beam Emission Spectroscopy (BES), and the ML model is implemented as a dense neural network using the SLAC Neural Network Library (SNL). A key capability is SNL dynamic parameter loading, which allows on-the-fly updates of neural network weights and biases without hardware resynthesis. This enables multiple classification tasks on a single FPGA design and supports adaptive control strategies that respond to evolving
plasma conditions. By decoupling inference from fixed-weight configurations, the system supports continuous model refinement and seamless task switching during live operation. The SNL-based inference engine is fully integrated with the FPGA in the DIII-D RTSTAB
Plasma Control System (PCS), improving ELM avoidance, confinement, and operational stability. These results show the feasibility of
embedding dynamically reconfigurable FPGA-based ML inference into real-time fusion diagnostic pipelines, providing a scalable and resilient path toward intelligent and autonomous
plasma control in future
magnetic confinement fusion devices.
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All authors Abhilasha Dave, James Russell, Mudit Mishra, Larry Ruckman, Keith Erickson, SangKyeun Kim, Semin Joung, Jalal Butt, Ryan Herbst, Ryan Coffee, David Smith, Egemen Kolemen
Plasma Physics
GANDALF: A hardware-agnostic spectral solver for kinetic reduced MHD turbulence
Anjor Kanekar
We present GANDALF, a JAX-based spectral solver for Kinetic Reduced MHD (KRMHD)
turbulence designed to lower infrastructure barriers to
plasma turbulence research. Existing production codes require specialized HPC infrastructure and compilation expertise, limiting participation to well-resourced institutions. GANDALF addresses this barrier by leveraging JAX's hardware abstraction to run transparently on laptops, desktop GPUs, and Apple Silicon without modification, enabling single-command installation via pip. We employ
Fourier spectral methods for spatial discretization and Hermite spectral basis for velocity space, combined with an exponential integrating factor method that exactly propagates linear Alfvén waves, eliminating associated numerical stiffness. Verification demonstrates research-grade accuracy: linear Alfvén waves achieve machine precision (~10^{-15} relative error), the Orszag-Tang
vortex conserves energy to 10^{-6} over two Alfvén times, and driven
turbulence reproduces the expected k_perp^{-5/3} cascade spectrum. GANDALF enables rapid prototyping, parameter surveys, and educational applications on commodity hardware. The code complements rather than replaces established solvers like AstroGK and Viriato, prioritizing accessibility for researchers without HPC resources. By removing infrastructure barriers while maintaining spectral accuracy, GANDALF broadens participation in fundamental
plasma turbulence research, particularly benefiting students, small research groups, and institutions in developing regions.
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Physics Education
Null Results, Real Learning: Geomagnetic Response to an X1.8 Solar Flare with Research-Grade and Smartphone Magnetometers in a Citizen-Science Classroom Activity
Roger M. Hart, Lauren E. Messina, Eric A. Schenck et al. (+9 more)
Introductory college Earth and space science courses offer rich opportunities for citizen science projects. One especially compelling context is Earth's geomagnetic field: a self-excited dynamo in the
liquid outer core generates a global field that couples Earth's interior to
solar forcing, providing a natural laboratory for space weather education. We tested the viability of smartphone magnetometers for quantitative monitoring during the 4 November 2025 X1.8
solar flare, linking planetary magnetism, space weather, and authentic undergraduate research. Co-located observations were obtained with a Geometrics G-857
proton-precession magnetometer and tri-axial smartphone sensors logging via Physics Toolbox in a course-based undergraduate research experience (CURE) emphasizing the Nature of Science (NOS). Fourteen one-minute paired averages spanning 17:27-17:40 UT revealed a systematic smartphone bias of about 630 nT (95% confidence interval 550-710 nT) relative to the G-857 and a weak negative correlation (r ~ -0.4). Smartphone magnetometers thus lack the precision and calibration stability needed for nanotesla-scale
flare signatures but remain valuable as pedagogical and engagement tools. We frame smartphones within a tiered instrumentation ladder linking research-grade observatories, intermediate-cost community magnetometers (for example, HamSCI Personal Space Weather Stations), and smartphones as high-engagement entry points to geomagnetic and space weather studies. This hierarchy aligns citizen science with open data protocols and NOS pedagogy, transforming low-cost sensing into epistemically grounded inquiry suitable for introductory college laboratories.
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All authors Roger M. Hart, Lauren E. Messina, Eric A. Schenck, Samantha R. Kaplan, Diego A. Canté, Izaiah Figueroa, Gabriella Sepe, Zavier Lopez, Ryan Ward, Sammy P. Morse, Melanie V. Ramirez, Brady J. Gaulin
Fluid Dynamics
Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach
Ludovico Nista, Christoph D. K. Schumann, Temistocle Grenga et al. (+3 more)
A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the subfilter-scale (SFS) stress tensor can be computed. A data-driven super-resolution generative
adversarial network (SR-GAN) is employed to upsample the grid-filtered velocity fields by a factor of two, enabling the evaluation of both scale-similarity and the dynamic Smagorinsky contributions. A priori analyses of forced homogeneous isotropic
turbulence (HIT) show that the SR-GAN accurately reconstructs flow features and generalizes well across different filter sizes and higher Reynolds numbers within the same flow configuration, even for unseen input fields. The DMSRM reproduces SFS stresses and energy dissipation more accurately than the traditional DMM. A posteriori LES calculations further confirm that DMSRM predicts the energy spectrum and intermittency more accurately than DMM, even for different LES grid-scale resolutions and higher Reynolds numbers than those used for training within the same forced HIT flow configuration. Unlike DMM, DMSRM yields realistic backscatter and physically consistent SFS energy dissipation. These improvements arise from the physically accurate super-resolved fields generated by the SR-GAN, from which SFS stresses are directly computed. The result is a closure that accurately reproduces stress magnitudes and dissipation while reducing reliance on additional dissipation from the dynamic term. The DMSRM formulation achieves a balance of physical fidelity, robustness, and computational efficiency, offering a promising alternative to traditional DMMs for
turbulence LES modeling.
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All authors Ludovico Nista, Christoph D. K. Schumann, Temistocle Grenga, Jonathan F. MacArt, Antonio Attili, Heinz Pitsch
Physics and Society · Cross-listed from cs.GT
The Evolution of Trust under Institutional Moral Hazard
Hiroaki Chiba-Okabe and Joshua B. Plotkin
We study the behavior of for-profit institutions that broadcast reputations to foster trust among
market participants. We develop a theoretical model in which buyers and sellers are matched on a platform to engage in transactions involving a moral hazard: sellers can either faithfully deliver goods after receiving payment, or not. Although the buyer does not know a seller's true type, the platform maintains a reputation system that probabilistically assigns binary reputation signals. Buyers make purchase decisions based on reputation signals, which influence the payoffs to sellers who then adapt their type over time. These
market dynamics ultimately shape the platform's profit from commissions on sales. Our analysis reveals that platforms inherently have an incentive for rating inflation, driven by the desire to increase commission. This introduces a second layer of moral hazard: the platform's incentive to distort reputations for its own profit. Such distortion is self-limited by the platform's need to maintain enough accuracy that trustworthy sellers remain in the
market, without which rational buyers would refrain from purchases altogether. Nonetheless, the optimal strategy for the platform can be to invest in order to reduce signal accuracy. When the platform can freely set commission fees, however, maximum profit may be achieved by costly investment in an accurate reputation system. These findings highlight the intricate tensions between platform incentives and resulting social utility for marketplace participants.
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Chemical Physics · Cross-listed from quant-ph
Accuracy and resource advantages of quantum eigenvalue estimation with non-Hermitian transcorrelated electronic Hamiltonians
Alexey Uvarov and Artur F. Izmaylov
In electronic structure calculations, the transcorrelated method consists in transforming the
Hamiltonian so as to remove the Coulomb cusp in its eigenfunctions. As a result, the wavefunction can be described more accurately without increasing the size of the basis set. However, the transcorrelated
Hamiltonian is non-Hermitian and non-normal, which makes many common
quantum algorithms inapplicable. Recently, a
quantum eigenvalue estimation
algorithm (QEVE) was proposed for non-Hermitian
Hamiltonians with real spectra [FOCS 65, 1051 (2024)]. Although the asymptotic scaling of this
algorithm with the desired accuracy is shown to be optimal, the constant factor in its complexity scaling has not been analyzed. Here we investigate the cost of QEVE applied to transcorrelated electronic
Hamiltonians of second-row atoms and compare it to the cost of applying standard qubitization to non-transcorrelated
Hamiltonians. We find that, with the xTC approximation, the T gate count of QEVE in the minimal STO-6G basis is between those of standard qubitization in the cc-pVTZ and cc-pVQZ bases. The accuracy of the transcorrelated energy differs between systems: for Li and Be, it is more accurate than the cc-pVQZ energy, while for larger atoms, the error gradually increases, exceeding the cc-pVDZ level for O, F, and Ne.
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Atmospheric and Oceanic Physics
A Comprehensive Review of Phase-Averaged and Phase-Resolving Wave Models for Coastal Modeling Applications
Md Meftahul Ferdaus, Nathan Alton Cooper, Austin B. Schmidt et al. (+4 more)
Predicting ocean wave behavior is challenging due to the difficulty in choosing suitable numerical models among many with varying capabilities. This review examines the development and performance of numerical wave models in coastal engineering and oceanography, focusing on the difference between phase-averaged spectral models and phase-resolving models. We evaluate the formulation, governing equations, and methods of widely used third-generation phase-averaged spectral models (SWAN, WAVEWATCH III, MIKE 21 SW, TOMAWAC, and WAM) alongside advanced phase-resolving models (FUNWAVE, SWASH, COULWAVE, and NHWAVE) that employ Boussinesq-type equations and non-hydrostatic formulations. The review begins with early parameterized models and progresses to contemporary third-generation models, which solve the wave action conservation equation with few spectral constraints. A comparison of the models' efficiency, accuracy in nearshore conditions, ability to resolve nonlinear wave-wave interaction, simulate wave breaking, diffraction, and wave-current interactions is provided. Applications in operational
forecasting, extreme event simulation, coastal structure design, and assessing
climate change impacts are discussed. The validation of these models and the statistical metrics and intercomparison studies used are addressed. A discussion of the limitations in computational scalability, physics parameterization, and model coupling is provided, along with emerging trends in high-resolution modeling and hybrid models. This review guides researchers in evaluating which models to use in coastal and oceanographic research.
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All authors Md Meftahul Ferdaus, Nathan Alton Cooper, Austin B. Schmidt, Pujan Pokhrel, Elias Ioup, Mahdi Abdelguerfi, Julian Simeonov
Fluid Dynamics
Predicting liquid properties and behavior via droplet pinch-off and machine learning
Jingtao Wang, Qiwei Chen, C Ricardo Constante-Amores et al. (+3 more)
Here we demonstrate that the time-evolving interface observed during droplet formation, and consequently the resulting morphology nearing pinch-off, encode sufficient physical information for machine-learning (ML) frameworks to accurately infer key fluid properties, including viscosity and surface tension. Snapshots of dripping drops at the moment of break-up, together with their
liquid properties and the flow rate, are used to form a data set for training ML
algorithms. Experiments consisted of visualizing, using high-speed imaging, the process of droplet formation and identifying the frame closest to break-up. Experiments were conducted using Newtonian fluids under controlled flow conditions. In terms of the Reynolds (Re) and Ohnesorge (Oh) numbers, our conditions cover the domains 0.001< Re< 200 and 0.01 < Oh < 20, by using silicon oils, aqueous solutions of ethanol and glycerin, and methanol. For each case, flow parameters were recorded, along with images capturing the final stages of droplet break-up. Supervised regression models were trained to predict fluid parameters from the extracted contours of the breaking droplets. Our data set contains 840 examples. Our results demonstrate that the droplet geometry at pinch-off contains sufficient information to infer fluid properties by machine learning approaches. Our methods can predict surface tension, viscosity, or the droplet shape at pinch-off. These approaches provide alternatives to conventional methods to measure
liquid properties while reducing measurement complexity and evaluation time and facilitating integration into automation. Unsupervised clustering is performed; the clusters represent regions in the Re-Oh and Bo-Oh planes, indicating that the latent representation may reveal physical properties and offering insight into droplet dynamics.
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All authors Jingtao Wang, Qiwei Chen, C Ricardo Constante-Amores, Denise Gorse, Alfonso Arturo Castrejon-Pita, Jose Rafael Castrejon-Pita
Computational Physics
A multi-language auto-differentiation module and its application to a parallel particle-in-cell code on distributed computers
Ji Qianga, Yue Hao, Allen Qiang et al. (+1 more)
The auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to various input parameters. It provides an efficient method to study sensitivity of the simulation results with respect to the input parameters. Furthermore, it can be used in gradient based optimization methods for rapidly optimizing design parameters. In this paper, we present the development of a fast and transparent auto-differentiation module/class designed for easy integration into numerous simulation codes. As an application, this auto-differentiation module is integrated into a parallel particle-in-
cell code with message passing interface (MPI) on distributed
memory computers.
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All authors Ji Qianga, Yue Hao, Allen Qiang, Jinyu Wan
Medical Physics
Effective Hyper-clutter Artifacts Suppression for Ultrafast Ultrasound Doppler Imaging
Lijie Huang, Jingyi Yin, Jingke Zhang et al. (+13 more)
Objective: Hyper-clutter artifacts (HCA), arising from strong tissue reflections or physiological motion, present persistent challenges in ultrafast ultrasound Doppler imaging, often obscuring surrounding small vessel flow signals, especially in fascial regions such as the renal capsule. This study proposes U-profile-based decluttering (UPBD), a robust and computationally efficient method that exploits singular value decomposition (SVD)-derived spatial singular vectors to suppress HCA in ultrafast Doppler imaging. Methods: UPBD analyzes intensity profile of each pixel along the singular-order dimension of the SVD-derived left singular vectors U. A pixel-wise clutter-energy ratio is computed to derive a spatially adaptive declutter weighting map, which is applied to the SVD-filtered flow signals. Results: UPBD was evaluated on multiple in vivo
datasets. Quantitative assessments based on contrast-to-noise ratio (CNR) and contrast-to-tissue ratio (CTR) demonstrated significant improvements over conventional SVD filtering. For example, UPBD enhanced CTR from 7.3 dB to 21.7 dB in contrast-free pig kidney, 17.8 dB to 42.1 dB in contrast-enhanced pig kidney, 8.2 dB to 32.8 dB in human kidney, and -4.9 dB to 3.7 dB in 3D human liver. Conclusion: The proposed UPBD method effectively suppresses HCA while preserving blood flow signals with minimal extra computational cost and no need for extensive parameter tuning. Significance: UPBD serves as a lightweight, easily integrated post-processing method that enhances HCA suppression, enabling broader application of SVD-based ultrafast Doppler imaging.
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All authors Lijie Huang, Jingyi Yin, Jingke Zhang, U-Wai Lok, Ryan M. DeRuiter, Kaipeng Ji, Yanzhe Zhao, Tao Wu, James D. Krier, Xiang-yang Zhu, Andrew J. Bentall, Andrew D. Rule, Thomas D. Atwell, Lilach O. Lerman, Shigao Chen, Chengwu Huang
Atmospheric and Oceanic Physics
Climatological benchmarking of AI-generated tropical cyclones
Yanmo Weng and Avantika Gori
This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu-Weather and Aurora) to reproduce observed TC track density, climatology of storm characteristics, and physical consistency with TC theory. By comparing AI-simulated TCs with ERA5 reanalysis, we
benchmark the distributions of intensity, size, forward speed, and evaluate the model's ability to credibly simulate extratropical transition. Results show that both Pangu and Aurora perform well in reproducing storm track density, forward speed distribution, and outer size distribution. Aurora shows an improved performance in simulating storm intensity compared to Pangu, with less bias in the distribution of minimum central pressure and maximum wind speed. However, both models overestimate the distribution of storm inner size (radius of maximum winds), especially for extreme events. AI models capture the relative frequency and temporal
evolution of extratropical transition patterns with reasonable accuracy. The AI-simulated TCs are also less likely to conform to gradient wind balance compared to ERA5, indicating that the AI TCs may not be physically realistic in many cases. This benchmarking identifies systematic biases that can guide future corrections and support extended applications of AI models for TC hazard and risk assessment. Our work establishes a foundation for future studies using AI weather models in the context of TC climatological and hazard research.
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Physics and Society
NetworkGames: Simulating Cooperation in Network Games with Personality-driven LLM Agents
Xuan Qiu
While Large
Language Models (LLMs) have been extensively tested in dyadic game-theoretic scenarios, their collective behavior within complex network games remains surprisingly unexplored. To bridge this gap, we present NetworkGames, a framework connecting Generative
Agents and
Geometric Deep Learning. By formalizing social simulation as a message-passing process governed by LLM policies, we investigate how node heterogeneity (MBTI personalities) and network
topology co-determine collective welfare. We instantiate a population of LLM
agents, each endowed with a distinct personality from the MBTI taxonomy, and situate them in various network structures (e.g., small-world and scale-free). Through extensive simulations of the Iterated Prisoner's Dilemma, we first establish a baseline dyadic interaction
matrix, revealing nuanced cooperative preferences between all 16 personality pairs. We then demonstrate that macro-level cooperative outcomes are not predictable from dyadic interactions alone; they are co-determined by the network's connectivity and the spatial distribution of personalities. For instance, we find that small-world networks are detrimental to cooperation, while strategically placing pro-social personalities in hub positions within scale-free networks can significantly promote cooperative behavior. We validate the robustness of these findings through extensive stress tests across multiple LLM architectures, scaled network sizes, varying random seeds, and comprehensive ablation studies. Our findings offer significant implications for designing healthier online social environments and
forecasting collective behavior. We open-source our framework to facilitate research into the social physics of AI societies.
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Physics and Society
CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior
Fong Yew Leong, Jaeyoung Kwak, Zhengwei Ge et al. (+7 more)
The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and human behavior. Combining computational fluid dynamics (CFD), machine learning (ML), and
agent-based modeling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare center, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across
agents, with more than two-fold change in risk of infection between the individuals with the lowest and highest risks in more than 90% of cases, despite all individuals being in the same overall environment. We found that infection risk distributions can exhibit not only a striking bimodal pattern in certain activities but also exponential decay and fat-tailed behavior in others. Specifically, we identify low-risk modes arising from source containment, as well as high-risk tails from prolonged close contact. Our approach enables near-real-time scenario analysis and provides policy-relevant quantitative insights into how ventilation design, spatial layout, and social distancing policies can mitigate transmission risk. These findings challenge simple distance-based heuristics and support the design of targeted, evidence-based interventions in high-occupancy indoor settings.
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All authors Fong Yew Leong, Jaeyoung Kwak, Zhengwei Ge, Chin Chun Ooi, Siew-Wai Fong, Matthew Zirui Tay, Hua Qian, Chang Wei Kang, Wentong Cai, Hongying Li
Computational Physics · Cross-listed from cond-mat.supr-con
Edge-Dependent Superconductivity in Twisted Bismuth Bilayers
Isaías Rodríguez, Renela M. Valladares, Alexander Valladares et al. (+3 more)
Twisted
bilayers offer a compelling and, at times, confounding platform for the engineering of new twistronic materials. Whereas standard studies almost exclusively focus on the explicit enigma that is presented by twist-angles, perhaps better epitomized by the related phenomena that have been observed in twisted
bilayer graphene, functional devices necessarily face a fundamental concern: boundary heterogeneity in their structures. In this study, we address this concern by strictly investigating the electronic properties of twisted bismuth
bilayers at the flake's edges and the vibrational properties of the flake. Twisted flakes exhibit continuous variations of these properties, away from the bulk, as we herein report using ab initio density functional theory, by systematically mapping the drastic
evolution of band
topology, electronic density of states, and possible superconductivity. Our work reveals a dramatic, non-fortuitous consequence of the structural disorder at the edges of the flakes: an enhanced electronic density of states at the
Fermi level. This enhancement reaches a maximum of 10 times that of perfect-crystalline bismuth. Given that the superconducting critical temperature, Tc, is exponentially dependent on the electronic density of states at the
Fermi level, this substantial structural variation immediately suggests a powerful mechanism for vastly increasing Tc. We also identify the twist-angle as a new critical parameter in designing novel engineering devices with topologically enhanced properties. Our results provide a necessary theoretical framework for interpreting new data for the upcoming generation of twistronic heterogeneous materials, and pave the way to search for atomic disordered metastable structures that could lead to enhanced superconducting transition temperatures.
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All authors Isaías Rodríguez, Renela M. Valladares, Alexander Valladares, David Hinojosa-Romero, Flor B. Quiroga, Ariel A. Valladares