Optics
Optical Implementation of Equilibrium Propagation Using Spatial Photonic Ising Machines
Dimitri Vanden Abeele, Daniele Veraldi, Davide Pierangeli et al. (+2 more)
Equilibrium Propagation offers a compelling alternative to traditional machine learning for training energy-based networks. Here we demonstrate a hybrid optical-digital implementation of EP using a Spatial Photonic Ising Machine (SPIM). The SPIM exploits the gauge transformation method to optically encode both continuous
neuron states and rank-1 binary trainable patterns as phase modulations via a spatial light modulator, with inference realized using a finite difference scheme. The experimental system is evaluated on the Wine classification
dataset. The potential of this approach, including the use of continuous couplings and structured coupling
matrices, is evaluated numerically on the more complex MNIST
dataset. Our work provides a concrete pathway toward energy-efficient physical implementations of
Equilibrium Propagation.
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All authors Dimitri Vanden Abeele, Daniele Veraldi, Davide Pierangeli, Claudio Conti, Serge Massar
Atomic Physics · Cross-listed from quant-ph
Quantum optical photoelectron interferometry
Jonathan Dubois, Viviane Cotte, Richard Taïeb et al. (+8 more)
We present a general theoretical framework for multiphoton processes driven by
quantum light fields, establishing a direct link between
photon statistics and photoelectron observables. Our results show that the autocorrelation and cross-correlation functions, which quantify the underlying
photon statistics, are directly mapped onto the resulting photoelectron spectra. Although our framework is broadly applicable, we demonstrate specifically in the example of reconstruction of attosecond beating by interference of two-
photon transitions (RABBIT) the influence of the light statistical properties. In this approach, the amplitude, contrast and phase of the oscillations of the sideband signal as a function of pump-probe delay reveal the
quantum nature of light. We analyze these observables across several
quantum configurations, including correlated infrared and harmonic modes, as well as the uncorrelated case with non-classical harmonic statistics, thereby establishing a general framework for
quantum-light RABBIT spectroscopy. We compare the analytical theory with numerical simulations for the case of classical harmonics and an infrared field in a squeezed coherent state, obtaining excellent agreement. Our results reveal how the interplay between classical and
quantum correlations dictates the coherence of the photoemission process, providing a new window into the
quantum-optical foundations of attosecond science.
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All authors Jonathan Dubois, Viviane Cotte, Richard Taïeb, Camille Lévêque, Jérémie Caillat, Pranshu Dave, Pascal Salières, David Bresteau, Charles Bourassin-Bouchet, Anne L'Huillier, David Busto
Optics
Polarization-controlled transport of light in a two-dimensional waveguide
J.V.S. Ferreira, A.M.G de Melo, N.A. Moreira et al. (+3 more)
Light propagation in disordered media is particularly complex due to the interference effects and polarization-mixing terms. Two-dimensional systems are particularly interesting because they feature two decoupled scattering channels, with distinct spectral statistics due to the presence of polarization-coupling terms in only one of them. We here show that in one channel, Anderson localization bounds the light transport both longitudinally and transversely. In the second channel, in contrast, diffusive transport takes place independently of scatterer density. Our results open new possibilities for engineering of light transport in two-dimensional waveguides.
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All authors J.V.S. Ferreira, A.M.G de Melo, N.A. Moreira, S.E. Skipetrov, R. Kaiser, R. Bachelard
Physics and Society
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
Mitja Devetak, Jasper Verschuur and Peter Klimek
Maritime chokepoints concentrate shipping traffic. Disruptions to this traffic can have a widespread impact on the global economy. However, the way in which these impacts are shaped by the shipping sector's adaptive behavior is not well understood. Here, we introduce an empirically calibrated full-scale
agent-based model of the global commercial shipping fleet, representing 35,954 active ships moving among 1,651 ports. We use the model to quantify how rerouting changes arrival losses under chokepoint closures. Static route exposure alone does not predict realized losses. In the adaptive model, rerouting reduces losses at some directly exposed ports, while delayed vessel cycles create losses at later port calls and in dependent regions. Cumulative net shipping-day losses therefore continue to rise with closure duration because longer routes keep ships delayed after the initial adjustment. Each additional closure day reduces global shipping arrivals by 3.0% for Suez and 7.7% for simultaneous Suez, Panama, and Malacca closures. These losses are unevenly distributed in exposed regions and ports. Disruptions with known duration show different loss profiles from unexpected shocks with unknown duration, revealing that end-date information can reduce avoidable short-run losses. The results show that chokepoint risk is a dynamic problem of routing, timing, and regional exposure and not a static property of maritime-network
topology.
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Fluid Dynamics · Cross-listed from quant-ph
Quantum Statistical Memory Advantage Reveals Predictive Structure in Chaotic Invariant Measures
Maida Wang, Xiao Xue, Minh Chung et al. (+1 more)
Long-horizon prediction of a chaotic system is governed by fidelity to its invariant measure, and which parts of that measure a learned predictor needs is a physical question not known in advance. We establish a
quantum statistical
memory advantage for interrogating it. A
quantum statistical prior (Q-Prior), trained once on classical data, stores an efficiently preparable $k$-point marginal in polynomially many
circuit parameters against exponentially many for explicit tabulation. Collective Bell measurements on two copies then estimate any post hoc Pauli-expectation magnitude at a copy cost independent of register size, with a worst-case exponential separation from single-copy protocols. A single Bell
dataset supports an entire family of candidate observables at a cost growing only logarithmically in the family size, so an exponentially large candidate space stays open for later analysis. We implement the protocol on IQM superconducting processors with two-copy registers of up to 54 physical-
qubit chips. We apply this to turbulent channel flow and ERA5
forecasting, resolving invariant structure by statistical sector and order. Turbulent phase correlations persist through eighth order, while most predictive gains arise from low-order constraints; in ERA5, planetary-wave phase coherence complements covariance regularisation, identifying distinct low-order sectors with predictive value.
Quantum statistical
memory is therefore both a near-term computational resource and an instrument for identifying which invariant structures matter for prediction.
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All authors Maida Wang, Xiao Xue, Minh Chung, Peter V. Coveney
Chemical Physics · Cross-listed from quant-ph
Characterizing the functional role of quantum coherence in energy transfer
Hallmann Óskar Gestsson and Alexandra Olaya-Castro
Quantum coherence has been suggested to play a role in excitation energy transfer in open
quantum systems, yet a quantitative approach to assessing its influence on the transfer process is still missing. Using Nakajima-Zwanzig projection operators, we derive a general
memory kernel identity that enables us to characterize and quantify the impact of coherence in the eigenenergy basis on a generalized rate of energy transfer. Applying our approach to the electronic dynamics of a dimer coupled to a structured
phonon bath, we demonstrate how
quantum coherence acts to modulate energy transfer.
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Optics
Ultrafast chiral sensing with an ultraviolet vector beam
Aude Rodriguez and Laura Rego
We present a robust, ultrafast and highly efficient setup for distinguishing molecular enantiomers by combining ultrafast techniques with vector beams, a type of topological light with azimuthally varying polarization. An infrared vector beam generates high-order harmonics in a sample of randomly oriented
chiral molecules, resulting in the emission of an ultraviolet vector beam whose intensity profile carries information about the handedness of the
chiral molecules. Our approach allows for spatial discrimination of molecular enantiomers, opening a new route for studying ultrafast chirality.
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Plasma Physics · Cross-listed from astro-ph.IM
A robust super-time-stepping scheme for Ohmic and ambipolar diffusion
Giancarlo Mattia, Mario Flock, David Melon Fuksman et al. (+3 more)
Context. Non-ideal magnetohydrodynamics (MHD) is a key tool for modeling
magnetic flux transport in astrophysical systems such as molecular clouds, protostellar cores, and protoplanetary disks. Conventional explicit methods for non-ideal MHD
diffusion are severely limited by timestep constraints, while substepping approaches can be unstable due to truncation errors near boundaries and strong
magnetic-field gradients. Aims. Our main goal is to address these limitations by developing robust super-time-stepping methods for Ohmic and ambipolar
diffusion. Methods. We present a super-time-stepping method based on the stability of the Gegenbauer
polynomials. The method is designed to enhance robustness in the presence of strongly anisotropic resistivity and to reduce sensitivity to truncation errors near boundaries. We implement the scheme in the PLUTO code and assess its performance through dedicated Ohmic and ambipolar
diffusion tests. We also compare this novel numerical scheme against two common astrophysical problems, namely
magnetic reconnection and the magnetorotational instability. Results. The novel Runge-Kutta-Gegenbauer scheme retains computational efficiency beyond purely explicit schemes while providing excellent stability compared with other traditional substepping methods. It remains stable in the presence of strongly anisotropic
diffusion, enabling accurate
magnetic-field
evolution in regimes characteristic of protoplanetary disks and collapsing dense cores.
Benchmark tests, including
magnetic reconnection and magnetorotational-instability setups, confirm the method's accuracy, efficiency, and suitability for large-scale non-ideal MHD simulations.
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All authors Giancarlo Mattia, Mario Flock, David Melon Fuksman, Anastasia Tzouvanou, Vittoria Berta, Daniele Crocco
Chemical Physics · Cross-listed from quant-ph
Tree-Structured Commutativity Packing in Adaptive Variational Quantum Simulation: Measurement Overhead and Representation Limits
Hermawan Kresno Dipojono
While
fermion-to-
qubit isomorphisms are mathematically invariant under exact
unitary statevector transformations, their physical implementation on noisy intermediate-scale
quantum (NISQ) devices introduces severe, practical measurement bottlenecks. In this work, we systematically investigate the measurement efficiency of adaptive derivative-assembled pseudo-trotter variational
quantum eigensolver (ADAPT-VQE)
algorithms across three critical molecular
benchmarks (LiH, BeH2, and H2O) at stretched bond lengths (2xRe) covering distinct point-group
symmetries. First, we establish exact representation invariance across all target multi-reference geometries, demonstrating that operator pool gradients and statevector electronic energies remain strictly isomorphic under exact mathematical execution (E(LiH) = -7.691469 Ha, E(BeH2) = -15.110110 Ha, and E(H2O) = -74.566874 Ha). Second, we quantify the hardware measurement overhead by partitioning operator pool observables into
qubit-wise commuting (QWC) tensor-product basis (TPB) cliques. We reveal that while both Jordan-Wigner (JW) and Bravyi-Kitaev (BK) encodings yield identical total Pauli term counts across the singlet excitation
manifold (128 terms for H2O and BeH2), the hierarchical binary-tree layout of the BK mapping dramatically compresses the required measurement
circuits—achieving up to a 45.31% reduction in total hardware execution steps compared to 12.50% under JW. Our findings demonstrate that tree-structured mappers serve as implicit, highly efficient measurement-packing engines for adaptive
quantum simulations without altering the underlying variational trajectory.
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Physics and Society
Mathematical Modeling of HDV RNA, HBV DNA, and HBsAg Dynamics during Lonafarnib-Based Therapy: Insights from the LOWR HDV-1 Study
Adquate Mhlanga, Louis Shekhtman, Rami Zakh et al. (+10 more)
Lonafarnib (LNF) is an investigational drug targeting hepatitis delta virus (HDV) but not hepatitis B virus (HBV), providing a unique opportunity to model HDV kinetics and how changes in HDV affect HBV. We performed a detailed kinetic analysis and developed a mathematical model to explain serum HBV DNA, HDV RNA and hepatitis B surface antigen (HBsAg) kinetics in 15 HBV/HDV coinfected patients receiving LNF-based treatment. After a delay of 0-2 days, patients experienced a rapid 1st-phase HDV-decline followed by either a viral plateau, 2nd slower-decline phase, or viral breakthrough (VB). LNF monotherapy led to a flat-partial-response (often followed by VB), while LNF combination therapy with ritonavir or pegylated interferon-$\alpha$ (PEG-IFN$\alpha$) was associated with a biphasic HDV decline (without VB). All treatments except LNF+PEG-IFN$\alpha$ had at least one patient experiencing an increase in HBV on-treatment. Our model successfully reproduced the observed HDV and HBV kinetics. We estimated an HDV RNA half-life of 1.26 days [95% confidence interval, CI: 1.05–1.47] in serum and treatment efficacy of 94% in inhibiting HDV RNA production across all treatments [95% CI: 89%–97%], as reflected by the 1st phase HDV decline. The 2nd phase of HDV decline was explained by a time-dependent increase in efficacy, reaching a maximum of 98.9%. The model explained the increase in serum HBV DNA by a median 4-fold [interquartile range, IQR: 1–28] increase in HBV DNA production rate when HDV declined below an inhibitory threshold. The stability of serum HBsAg was explained by a constant number of HBsAg-producing
cells.
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All authors Adquate Mhlanga, Louis Shekhtman, Rami Zakh, Sarah Duehren, Ashish Goyal, Alexander Churkin, Vladimir Reinharz, Danny Barash, Jeffrey Glenn, Ohad Etzion, Scott J. Cotler, Cihan Yurdaydin, Harel Dahari
Optics
Experimental Design Space Exploration of Ultra-Low Threshold Hybrid III-V/Si Quantum Dot Microring Lasers
Xucheng Yang, Preston Luong, Yatiraj Ramanujam et al. (+10 more)
In this work, we report on the design strategies and experimental validation of ultra-low threshold ($< 0.8\,\mathrm{mA}$) hybrid III–V/Si
quantum dot (InAs/GaAs) micro-ring
lasers with optical output powers $> 2\,\mathrm{mW}$ for $1.3\,\mu\mathrm{m}$ emission. The multi-dimensional design exploration allows for the demonstration of record wall-plug efficiencies ($\sim 10\%$) and threshold current densities ($109\,\mathrm{A/cm^2}$) for these compact sources on silicon. We also demonstrate the thermal performance of several designs with record characteristic temperature values of $T_0 = 212\,\mathrm{K}$, indicating minimal temperature dependence of the threshold current. In addition, the high differential gain allows for the demonstration of 3-dB bandwidths up to $5\,\mathrm{GHz}$.
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All authors Xucheng Yang, Preston Luong, Yatiraj Ramanujam, Antoine Descos, Yingtao Hu, Yuan Yuan, Bassem Tossoun, Geza Kurczveil, Eunso Shin, Jonathan Wierer, Ray Beausoleil, Di Liang, Stanley Cheung
Applied Physics
Bandedge-state-limited single-photon emission from volumetric quantum design of 2D colloidal quantum wells
Xiao Liang, Bo Wang, Yue Yu et al. (+6 more)
Present-day solution-processable single-
photon sources are dominated by three-dimensionally confined colloidal
quantum dot emitters, yet their particle-to-particle variation in single-
exciton properties limits reproducibility and scalability. Here, to avoid such heterogeneity, we demonstrate reliable room-temperature single-
photon emission from atomically flat two-dimensional (2D) colloidal
quantum wells (CQWs) with inherently uniform one-dimensional
quantum confinement, despite their long-standing limitations of efficient multiexciton emission and pronounced
exciton-surface susceptibility. We resolve these challenges through volumetric
quantum design (VQD) of CQWs, yielding a highly localized, single bandedge state. This design laterally confines the bandedge excitonic domain within the
exciton coherent area and vertically decouples it from surface states via a thick, strain-relieved
quantum-barrier shell that preserves strong confinement, overcoming the daunting thickness-confinement trade-off in 2D CQWs. Statistical single-particle spectroscopy reveals that VQD-CQWs deliver near-blinking-free (on-time >99.5%) and fluence-insensitive antibunching (g(2)(0): 0.041), protected by a bandedge-state-filling bottleneck, together with linear polarization of up to 73% under cavity-free conditions, originating from synergistic transition-dipole and electric-field anisotropies. These advances establish 2D CQWs as a viable, homogenous and scalable platform for
quantum technologies.
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All authors Xiao Liang, Bo Wang, Yue Yu, Pedro Ludwig Hernandez-Martinez, Zengshan Xing, Lu Ding, Vijay Kumar Sharma, Tze Chien Sum, Hilmi Volkan Demir
Medical Physics · Cross-listed from cs.CV
Dual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis
Gabriel Steele, Alzahra Altalib and Alessandro Perelli
We present a Dual-Domain Equivariant Generative
Adversarial Network (DDE-GAN) for
multimodal CT-PET image synthesis. Traditional GAN-based approaches often operate solely in the spatial domain and ignore
geometric consistency, resulting in limited structural fidelity. DDE-GAN addresses these challenges by jointly learning from both spatial and frequency (
Fourier) domains, capturing complementary anatomical and spectral information. Furthermore, rotational equivariance embedded in the physics of the CT and PET measurements are integrated into the loss of both the generator and discriminator to ensure consistent responses under rotations, improving anatomical accuracy. A hierarchical dual-domain training strategy enforces intra- and inter-domain consistency through multi-stage loss functions. Evaluated on the HECKTOR 2022 CT-PET
dataset, DDE-GAN achieves superior synthesis quality over baseline models for CT-PET image synthesis. The results demonstrate that combining dual-domain learning with
geometric equivariance substantially enhances
multimodal image synthesis accuracy and robustness, enabling practical applications in PET completion and data augmentation.
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Fluid Dynamics
Data-Driven Equation Discovery for Nonlinear Liquid Film Flows
Sebastian T. Dooley, Albert P. Bartók, James E. Sprittles et al. (+1 more)
Over the past decade data-driven equation discovery emerged as a powerful alternative to first principles-based methodologies traditionally used in mathematical modelling cycles. The approach provides a promising path towards deep, physical insight into systems that have previously evaded rigorous mathematical derivation procedures, often due to intractable complexity. The strengths of such techniques have been successfully established for many problem classes described by systems of ordinary differential equations and continue to be extended, with their reach into partial differential equation systems gaining momentum, though comparatively nascent. Herein we tackle such a frontier: elucidating the dynamics of
liquid film flows, a problem space providing a rich backdrop in terms of asymptotic analytical building blocks. By leveraging expert knowledge and the ability to carefully curate data, we establish a best-case scenario for identifying the underlying governing equations. Even here, multi-collinearity, stemming from the choice of monomial basis functions in our multi-scale flow configuration, introduces complex identifiability issues. Early-time transients compound this further, as the most dynamically rich behaviour carries the largest residuals in training data. Pinpointing such vulnerabilities allows us to better define the boundaries of current discovery techniques and paves the way for the next generation of more resilient, numerically stable
algorithms.
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All authors Sebastian T. Dooley, Albert P. Bartók, James E. Sprittles, Radu Cimpeanu
Plasma Physics
Feasibility of a Flexible, Hybrid Tokamak-Stellarator Experiment using an Axisymmetric Dipole Coil Array
Jacob Halpern, Mohammed Haque, Elizabeth Paul et al. (+6 more)
We demonstrate the design of a flexible, university-scale hybrid tokamak-stellarator experiment based on an axisymmetric array of planar HTS dipole coils. Because the coil array has few
geometric degrees of freedom, we use single-stage optimization of the coil currents initialized from two-stage solutions to obtain mutually consistent
equilibria and coil sets within realistic engineering limits. We find that the field error and coil current thresholds set minimum and maximum coil-
plasma distances that confine the boundary to a roughly fixed axisymmetric envelope, within which rotational transform, volume, coil current, and quasi-
symmetry (QS) error trade off against one another. Tighter current limits delocalize the non-axisymmetric shaping and raise QS error at fixed transform. From this single coil array we obtain a broad range of
equilibria-quasi-axisymmetric
vacuum stellarators with $\iota$ up to 0.2, finite-$\beta$ hybrids with realistic profiles reaching on-axis $\iota$ $\approx$ 1 and
vacuum transform relevant for MHD stabilization, and strongly shaped tokamaks with elongation $\kappa$ = 1.7 and triangularity $\delta$ = $\pm$0.6, all at peak pointwise coil forces well below the HTS tolerance. We show the same array can additionally correct toroidal field (TF) coil ripple, reducing the number of TF coils required compared to the equivalent tokamak. These results establish the design as a promising platform for hybrid tokamak-stellarator research.
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All authors Jacob Halpern, Mohammed Haque, Elizabeth Paul, Carlos Paz-Soldan, Rithik Banerjee, Talia Angles, Frederick Sheehan, Ian Stewart, Alan Kaptanoglu
Optics
Learning light scattering from operator parameter spaces to Galerkin-consistent solution spaces
Lida Liu, Jingwei Wang, Wei Cao et al. (+2 more)
Efficient and generalizable full-wave simulation is essential for nanophotonic analysis and inverse design, yet existing methods face a tradeoff between the high computational cost of numerical solvers and the limited generalizability of neural operator models for complex optical scattering. Here, we introduce FEMONet, a finite-element-constrained operator-learning framework that learns light scattering from an operator parameter space to a Galerkin-consistent solution space. The operator parameter space encodes the physical entities defining a wave-equation problem, while the variational weak form links this space to the coordinate and physical solution spaces. Integrated with operator-learning networks, FEMONet extends classical solvers from isolated problem instances to parameterized scattering operators. To our knowledge, FEMONet represents the first Galerkin-consistent operator-learning framework for complex-valued optical scattering, grounded in the variational weak form of the governing vector wave equations. Finite-element discretization absorbs spatial derivatives into assembled stiffness
matrices and load vectors, removing coordinate-based derivatives of the neural-network output from the physics loss and improving training efficiency. By predicting finite-element expansion coefficients rather than unconstrained field values, the Galerkin-consistent formulation preserves compatible trial and test spaces, achieving high accuracy, stable training, and generalization across dielectric, metallic, arrayed, plasmonic, and three-dimensional nanophotonic structures.
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All authors Lida Liu, Jingwei Wang, Wei Cao, Yang Zhang, Yuntian Chen
Instrumentation and Detectors
Demonstration of length control for a filter cavity with coherent control sidebands
Naoki Aritomi, Yuhang Zhao, Eleonora Capocasa et al. (+22 more)
For broadband
quantum noise reduction of gravitational-wave detectors, a frequency-dependent squeezed
vacuum field realized using a filter cavity is the most promising technique and will be implemented in Advanced LIGO and Advanced Virgo in the fourth observing run. To obtain the benefit of frequency-dependent squeezing, the length and
alignment of the filter cavity with respect to the squeezed
vacuum field must be accurately controlled. To this purpose, a new length and
alignment control scheme for a filter cavity, using coherent control sidebands, was suggested [Phys. Rev. D 102, 042003 (2020)]. The coherent control sidebands are already used to control the squeezing angle in squeezed
vacuum sources for gravitational-wave detectors. As both the squeezed
vacuum field and coherent control sidebands have the same mode-matching conditions and almost the same frequency, the length and
alignment of the filter cavity with respect to the squeezed
vacuum field can be accurately controlled with this scheme. In this paper, we experimentally demonstrate the new control scheme for a filter cavity with coherent control sidebands. In addition to the conventional filter cavity control with the green field, we succeed in controlling the length of a 300-m filter cavity with coherent control sidebands and reduce the filter cavity length noise (rms) from 6.8 to 2.1 pm.
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All authors Naoki Aritomi, Yuhang Zhao, Eleonora Capocasa, Matteo Leonardi, Marc Eisenmann, Michael Page, Yuefan Guo, Eleonora Polini, Akihiro Tomura, Koji Arai, Yoichi Aso, Martin van Beuzekom, Yao-Chin Huang, Ray-Kuang Lee, Harald Luck, Osamu Miyakawa, Pierre Prat, Ayaka Shoda, Matteo Tacca, Ryutaro Takahashi, Henning Vahlbruch, Marco Vardaro, Chien-Ming Wu, Matteo Barsuglia, Raffaele Flaminio
Atomic Physics · Cross-listed from hep-ph
Exploring Exotic Spin-Dependent Interactions Beyond the Standard Model: Theoretical Foundations and Experimental Investigations
L. Y. Wu and H. Yan
New interactions mediated by novel particles propose solutions to several important questions in modern physics.
Axions serve as examples of such particles; they are lightweight and interact weakly with ordinary matter. This category of particles, including those similar to
axions-termed
Axion-Like Particles (ALPs)-arises from diverse theoretical frameworks, such as the Peccei-Quinn mechanism addressing the strong CP problem, string theory, and spontaneous supersymmetry breaking. Given their light mass and weak coupling, ALPs are also possible candidates for cold dark matter. Introducing these new interactions mediated by novel particles not only tackles several challenges in modern physics but also raises a crucial question: Are there undiscovered interactions beyond the Standard Model? Many of the interactions predicted by these theories are
spin-dependent, which is the primary focus of this review. In this review, we first outline the theoretical foundations for investigating exotic
spin-dependent interactions, highlighting their importance in various models beyond the Standard Model. We examine the potential roles of new lightweight particles in mediating these interactions, which may enhance our understanding of dark matter. Relevant formulas derived from theoretical models are included to support experimental investigations. Following this theoretical framework, we conduct a detailed review of recent experimental efforts to detect these exotic interactions. A systematic review of current constraints on these interactions is presented, along with an assessment of various detection approaches.
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Classical Physics
Formulation of stress-gradient models describing three-dimensional non-local medium
Sla{\dj}an Jelić and Dušan Zorica
Based on one-dimensional Eringen stress-gradient non-local model, by considering non-locality vector and nabla operator instead of non-locality scalar parameter and second order derivative, eight three-dimensional Eringen non-local models are formulated and classified into two groups: three scalar- and five tensor-type non-local models, according to the type of used non-locality operator which is obtained via various vector products of non-locality vector and nabla operator. The compatibility conditions ensuring symmetricity of Cauchy stress tensor in the case of the tensor-type model are derived. Furthermore, using the
Fourier integral transform with respect to spatial coordinates, non-locality kernels (Green's functions), reflecting non-locality character of the material, are derived for each of the proposed models. Except for the one scalar-type model, all other models account for both local and non-local contributions to Cauchy stress tensor. Additionally, the isotropy of proposed models, as well as their non-local isotropy properties, both depending on non-locality kernel, are examined. All scalar-type models are isotropic, such that one of them is non-locally isotropic and two of them correspond to a non-locally anisotropic body, while all tensor-type models are anisotropic, such that there are two models that do not prefer direction of non-locality, thus corresponding to a non-locally isotropic body, whereas three models correspond to a body exhibiting non-locality along a specific direction(s), thus corresponding to a non-locally anisotropic body.
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Plasma Physics
Archê, an orbital-free molecular dynamics code for fast production of equations of state
William Weens, Augustin Blanchet and Philippe Arnault
We present Archê, an orbital-free molecular dynamics (OFMD) code designed to produce equations of state (EOS) in the
plasma state. Unlike in other OFMD codes, Archê uses a self-consistent field (SCF) approach to compute the electronic density. This allows us to implement two
algorithms that accelerate SCF convergence by a factor of up to six. First, the density is initialized using the results from the previous MD timestep to define a one-center profile, which is then applied to the new
nuclei positions. Second, the initial and final densities are mixed at each SCF iteration in a proportion that minimizes an approximate free energy. We validate the code by comparing the calculated aluminum EOS to results obtained with the Kohn-Sham density functional theory software Abinit. Achieving agreement in the internal energy requires adding a correction related to the norm-conserving pseudopotential derived from an average-atom model. Performance is compared across CPU and GPU architectures, demonstrating an order-of-magnitude speedup for a single GPU compared to 256 CPUs. Archê exhibits an overall linear computational complexity with respect to the number of atoms, as well as the number of real and reciprocal grid points. Execution time is weakly dependent on density; however, interestingly, it decreases as temperature increases – in contrast to simulations based on Kohn-Sham orbitals.
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Geophysics
Towards imaging Earth's large-scale structures by directional geoneutrino detection with Ocean Bottom Detector
Zhihao Xu, Takumi Araki, Simran Chauhan et al. (+17 more)
Geoneutrinos, electron antineutrinos produced by radioactive decays of heat-producing elements (HPEs) within the Earth, provide unique insights into Earth's interior and heat budget since their first detection in 2005 by KamLAND. Conventional geoneutrino detectors currently provide integrated global information and lack the capability to spatially resolve structures deep within the Earth. Here, we evaluate the ability of angular-sensitive geoneutrino detectors to distinguish between homogeneous and heterogeneous mantle models, focusing on Large Low Shear Velocity Provinces (LLSVPs). Our results show that LLSVPs enriched in Th and U yield a distinct flux of geoneutrinos with distinctive angular patterns. An oceanic site above the Pacific LLSVP is considered a particularly favorable detector location. The Ocean Bottom Detector (OBD) project aims to leverage this spatial resolving advantage by deploying a kiloton-scale
liquid scintillator detector directly on the ocean floor, enabling unprecedented sensitivity for mantle geoneutrino detection. These findings demonstrate the critical role of combining geophysical and geochemical data to guide detector site selection, ultimately improving constraints on Earth's internal heat and the HPE distribution.
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All authors Zhihao Xu, Takumi Araki, Simran Chauhan, Brian C. Crow, Max A. A. Dornfest, Stephen T. Dye, John Graham, Misaki Hosoya, Kunio Inoue, John G. Learned, Viacheslav A. Li, William F. McDonough, Takeru Ohno, Takanobu Ono, Taichi Sakai, Jackson Seligman, Nathan Sibert, David Vartanyan, Hiroko Watanabe, Jeffrey Yepez
Medical Physics
ffdas: Volumetric ultrasound reconstruction at warp speed
Luuk Verhoef and Pieter Kruizinga
Volumetric ultrafast ultrasound imaging demands reconstruction of images with millions of voxels thousands of times per second, creating computational challenges that limit both real-time feedback and easy offline analysis. Graphics processing units (GPUs) are well suited to this workload, yet we show that standard delay-and-sum implementations underutilize GPU resources through fragmented
memory access patterns, even when sufficient computational capacity is available. Three optimization strategies address this: aligning
memory access with GPU transfer granularity, halving
memory traffic through mixed-precision storage, and exploiting spatial locality to utilize tensor core arithmetic. Together, these achieve kilohertz frame rates for $128^3$-voxel grids with 1024-element arrays, substantially outperforming existing implementations while maintaining image quality. This enables real-time volumetric imaging at scales previously restricted to offline processing, supporting applications such as intraoperative
brain imaging and
brain-computer interfaces where immediate feedback is essential. We release our implementation as part of the open-source ffdas library.
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Computational Physics
REMAL: Residual Equilibrium Manifold Active Learning for Surrogate-Based Multidisciplinary Design Analysis
Kail Yuan and Ashwin Renganathan
Multidisciplinary design analysis of coupled engineering systems requires the computation of
equilibrium states in which all disciplinary coupling variables are mutually consistent. Conventional fixed-point iteration resolves this consistency problem separately at each design point, which can become expensive when disciplinary evaluations are costly and many analyses are required in outer-loop tasks such as multidisciplinary design optimization, uncertainty quantification, or digital twin updating. This paper introduces REMAL, a residual
manifold surrogate modeling framework for coupled systems. Instead of approximating each discipline independently or directly learning converged coupling variables, the proposed method learns a surrogate model of the joint residual
manifold via multitask Gaussian process models. An
entropy-based active learning strategy selects additional residual evaluations near uncertain zero-contour regions, and
equilibrium states for new design inputs are recovered by solving a nonlinear least squares optimization problem using only the trained surrogate. The method is evaluated on four engineering coupled system
benchmarks: a satellite model, an aerostructural model, a finite-element gas-turbine heat-transfer and economics model, and a modified turbine model with added feedback coupling. Across these cases, REMAL consistently demonstrates the cost effectiveness when repeated evaluations of the fixed point across the design space are necessary. Theoretically, we show that, under mild assumptions, REMAL's predictive fixed point error is bounded.
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Chemical Physics
Excited-state Properties Beyond the Excitation Energy from Orbital-Optimized Density Functional Calculations II: Absorption Spectra
Lorenzo Restaino, Diego Llorena Prieto, Jukka John et al. (+3 more)
Previous
benchmarks of orbital-optimized density functional calculations of transition intensities in molecules have primarily been restricted to the lowest electronic excitation. Here, the performance of this variational approach is assessed across several valence and Rydberg excited states of small molecules, calculating the absorption spectra up to 10~eV. A plane-wave basis set is employed to accurately describe diffuse Rydberg excitations, while the transition dipole moment is evaluated from nonorthogonal Kohn–Sham determinants within the projector augmented wave formalism. Comparison with higher-level coupled-cluster results shows that orbital-optimized calculations provide a good description of oscillator strengths for states with predominantly single-configurational character, even with the generalized gradient approximation functional PBE. The inclusion of exact exchange and self-interaction correction further improves the results, with the latter yielding the smallest errors (mean absolute relative error of $\sim$25%). In contrast, large errors for all functionals are found for multi-configurational states, due to an inability of the approach to describe the multi-configurational character. Nonorthogonality between independently optimized states has previously been raised as a possible source of error in transition properties. In the present
benchmark, no correlation of the nonorthogonality between the ground and excited states with the error on the transition intensity is found. These results establish the performance of orbital-optimized density functional calculations for absorption spectra of small molecules and highlight the need for extensions that combine state-specific orbital relaxation with a multi-configurational treatment.
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All authors Lorenzo Restaino, Diego Llorena Prieto, Jukka John, Yorick Leonard Adrian Schmerwitz, Elvar Örn Jónsson, Gianluca Levi
Geophysics
Historical Seismic Monitoring of EGS and Conventional Geothermal Fields: LBNL Efforts and Lessons Learned
Nori Nakata, Ernest Majer, Chet Hopp et al. (+4 more)
Enhanced Geothermal Systems (EGS) deployment across diverse geological settings requires a better understanding and management of induced seismicity. Lawrence Berkeley National Laboratory (LBNL) has established seismic monitoring systems at numerous geothermal sites, including the Geysers, Desert Peak, Brady Hot Springs, Raft River, Newberry, Patua, Don A. Campbell, Jersey Valley, Utah FORGE, and Cape Modern. These multi-site deployments provide valuable observations of induced seismicity and reservoir
evolution, supporting studies of thermo-hydrological-mechanical-chemical (THMC) processes, risk assessment, and reservoir management. In this presentation, we review the status of LBNL-operated seismic networks, summarize lessons learned from sensor deployment and long-term monitoring, and discuss challenges related to noise mitigation, sensor coupling, data transmission, real-time processing, and data quality. We also highlight ongoing efforts to make these
datasets publicly accessible to support future geothermal and induced seismicity research.
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All authors Nori Nakata, Ernest Majer, Chet Hopp, Steve Jarpe, Tim Elnitiarta, Hongrui Qiu, Michelle Robertson
Fluid Dynamics
Duty-cycle modulation of the self-sustaining process by spanwise wall oscillation
Lionel Agostini
Direct Numerical Simulation of turbulent channel flow at friction Reynolds number around 200 is performed with spanwise wall actuation to achieve drag reduction. A quasi-square-wave waveform, featuring impulsive transitions and constant-velocity plateaus, separates the actuation cycle into distinct Reversal and Displacement Phases, thereby permitting direct observation of the underlying physics. Phase-resolved analysis reveals that the actuation modulates the self-sustaining process (SSP): during the Reversal Phase, the Stokes strain passes through zero, the SSP resumes, and streaks regenerate; during the Displacement Phase, sustained Stokes strain diverts wall-normal vorticity spanwise via
vortex tilting, depleting SSP precursors and suppressing streaks. A
stochastic enstrophy-budget analysis confirms this mechanism at the governing-equation level: competition between mean-shear production of wall-normal enstrophy and Stokes-driven spanwise diversion, drawing from a shared reservoir, reflects directed, phase-opposed switching. The quasi-square wave improves the gross drag-reduction margin by 2.5 percentage points over the optimal sinusoidal baseline, solely via temporal Stokes-strain redistribution, and the waveform renders duty-cycle switching directly observable, thus elucidating the causal chain of drag reduction.
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Optics
Robustness against disorder in topological fibre lasers with explicitly broken PT symmetry
Brook Salter, Nathan Roberts, Habib Rostami et al. (+2 more)
Fibre
lasers realise a large gain medium in a compactly coiled fibre. Disorder due to fabrication can negatively impact the stability of their lasing modes, especially in multi-core fibres. Recently, topological fibres (without gain) have been experimentally demonstrated to be robust against fabrication disorder, but topological fibre
lasers have not yet been designed or modelled. Here, we use a combination of mode-coupling theory and finite-element simulations to design and model a topological
laser based on a non-Hermitian Su-Schrieffer-Heeger (SSH) chain embedded in a photonic
crystal fibre. Our design is based on a winding-number invariant in combination with a PT-symmetric SSH bulk. We show that the topological boundary mode is selectively amplified when extra gain is added at the topological interface. Even with nonlinearity added through saturable gain, the lasing supermode retains its robustness against disorder. We present a realistic design for a topologically robust fibre
laser using readily available stack-and-draw methods with doped cores. This work establishes a new approach for imbuing non-Hermitian photonic systems with topological protection, with technological implications towards generating robust
quantum and classical signals.
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All authors Brook Salter, Nathan Roberts, Habib Rostami, Peter J. Mosley, Anton Souslov
Plasma Physics
Ultrashort Pulse Train Generation on a 100TW Laser Beamline Using a Delay Mask After the Final Focusing Optics
David Gregocki, Federica Baffigi, Lorenzo Fulgentini et al. (+2 more)
Experimental results aimed at demonstrating the feasibility of a two-section delay mask for the generation of ultrashort pulse trains are reported. Based on the initial simulation results, a 500 um thick circular fused silica plate featuring a central aperture was designed to enable two distinct transverse portions of the incident
laser pulse to be focused, ideally, with equal intensity. This fulfills one of the requirements of the resonant multipulse ionization injection (ReMPI) scheme for
laser wakefield acceleration. The experiment was carried out at the CNR-INO Intense
Laser Irradiation Laboratory using a 240 TW
laser system operated at 120 TW, as part of the ongoing preparation for the first experimental demonstration of ReMPI.
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All authors David Gregocki, Federica Baffigi, Lorenzo Fulgentini, Luca Labate, Leonida Antonio Gizzi
Optics
Can non-orthogonal bases form stable skyrmionic beams?
Zan Zhang, Zhujun Ye, Zhenyu Guo et al. (+7 more)
Skyrmions, topologically stable
spin textures, have recently garnered significant
attention in optics promising robust high-density information transition and nontrivial light-matter interaction. It was believed that the optical skyrmionic beams should be constructed by superposition of two orthogonal spatial modes with orthogonal polarizations to obtain topologically stable propagation. Here, we surprisingly find that propagation-stable skyrmionic beams can still be formed by superpositions of neither orthogonal spatial modes nor orthogonal polarizations. We theoretically present the mechanism to control the stable skyrmionics beams through the hybrid superposition of modes from the Hermite-Gaussian and Laguerre-Gaussian families and experimentally control the longitudinal on-demand dynamics of the skyrmions. This work redefines the topological stability of optical skyrmions, breaks limits and reduces the requirement for manipulating topologically structured light for practical multidimensional implementation of topologically robust information technologies.
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All authors Zan Zhang, Zhujun Ye, Zhenyu Guo, Zihan Liu, Zhuoyue Sun, Zijing Zhang, Jiantao Ma, Katsuya Inoue, Yijie Shen, Dongmei Deng
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Decoding Crystallographic Surface Chirality from Real Space and Momentum Space Images by Machine Learning
Chetana Badala Viswanatha, Ka Man Yu, Benito Arnoldi et al. (+6 more)
Intrinsically
chiral metal surfaces, where handedness arises from the asymmetric step-kink-terrace
topology of high-Miller index planes, are model systems for enantiospecific catalysis, sensing, and spintronics. Yet no consistent method exists to classify their handedness directly from experimental observables, without prior knowledge of crystallographic indices. Here, we report a dual-domain machine learning framework that recognizes handedness in
chiral metal surfaces from two independent image representations: atomic structure models in real space and simulated momentum-resolved photoemission maps of
Fermi surface projections in reciprocal space. A ResNet18 model pretrained on general image data is fine-tuned for chirality classification independently on both image representations and achieves meaningful classification accuracy for both real space and reciprocal space images. However, the
Fermi surface classifier significantly outperforms the real space classifier. Critically, the reciprocal space classifier, trained only on synthetic images, correctly identifies synchrotron-acquired experimental Angle-Resolved Photoemission Spectroscopy (ARPES) maps of Cu(643)$^R$ and Cu(643)$^S$. The stronger performance in momentum space, as well as the transfer to experimental data, shows that handedness is encoded more globally and robustly in the electronic structure than in the spatially localized kink-site geometry. These results establish ARPES as a quantitative readout of crystallographic surface chirality and suggest a scalable route to identify
chiral metal surfaces relevant to
spin-selective phenomena.
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All authors Chetana Badala Viswanatha, Ka Man Yu, Benito Arnoldi, Anagha Aravind, Aaruni Kaushik, Jannis Lessmeister, Martin Aeschlimann, Benjamin Stadtmüller, S. Harshini Tekur