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Physics Physics

physics Watch Physics

Wednesday 8 October

Optics

Multimode emission of fluorinated ethylene propylene clad large diameter liquid-core lasers

Anand Dewansingh, Abigail Deaton, Cortland Bergman et al. (+3 more)

A liquid-core (LiCo) dye laser was demonstrated using Rhodamine B (RhB) dissolved in glycerol as the gain medium and fluorinated ethylene propylene (FEP) tubing as the waveguide. Photoluminescence and amplified spontaneous emission (ASE) studies identified optimal RhB concentrations of 0.1 wt.% and 0.3 wt.% for low-threshold laser operation. Laser emission was achieved in LiCo rods with 1/16 inch and 1/32 inch inner diameter FEP tubing, with narrower tubing providing enhanced mode confinement and spectral narrowing. The addition of cavity mirrors improved emission coherence, revealing a distinct laser mode at low pump energies with mode spacing inconsistent with a simple Fabry-Pérot cavity, indicating complex mode coupling and internal reflections. Limitations include spectral broadening and scattering-induced parasitic feedback, which suggest avenues for further optimization in waveguide materials and output coupling.
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All authors Anand Dewansingh, Abigail Deaton, Cortland Bergman, Hengzhou Liu, Tracy Olin, Nathan J. Dawson

Fluid Dynamics

Viscoelastic flow of an Oldroyd-B fluid through a slowly varying contraction-expansion channel: pressure drop and elastic stress relaxation

Yali Kedem, Bimalendu Mahapatra and Evgeniy Boyko

Viscoelastic fluid flows in narrow non-uniform geometries are ubiquitous in various engineering applications and physiological flow systems. For such flows, one of the key interests is understanding how fluid viscoelasticity affects the flow rate-pressure drop relation, which remains not fully understood. We analyze the flow of the Oldroyd-B fluid in slowly varying contraction-expansion channels, commonly referred to as constrictions. Unlike most previous theoretical studies focusing on contracting channels, we consider a constriction geometry and present a theory for calculating the elastic stresses and flow rate-pressure drop relation at low and high Deborah ($De$) numbers. We apply lubrication theory and consider the ultra-dilute limit, in which the velocity approximates a parabolic and Newtonian profile. This results in a one-way coupling between the velocity and elastic stresses, allowing us to derive closed-form expressions for the elastic stresses and pressure drop for arbitrary values of $De$. We validate our theoretical predictions with numerical simulations, finding excellent agreement. We identify the physical mechanisms governing the pressure drop behavior and compare our results for the constriction with previous predictions for the contraction. At low $De$, the pressure drop in the constriction monotonically decreases with $De$, similar to the contraction. However, at high $De$, in contrast to a linear decrease for the contraction, the pressure drop across the constriction reaches a plateau due to the vanishing contribution of elastic normal stresses, leaving elastic shear stresses as the sole driver of the reduction. Furthermore, we elucidate the spatial relaxation of elastic stresses and pressure gradient in the exit channel following both constriction and contraction geometries, showing that the relaxation length is significantly shorter in the case of a constriction.
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Physics Education

Notes from the Physics Teaching Lab: Diode Laser Spectroscopy at 658 nm

Kenneth G. Libbrecht

We describe a teaching-lab experiment that applies basic optical spectroscopy to examine the physics of semiconductor diode lasers. By using a low-power visible laser, this experiment is suitable for use in an open lab environment, where students assemble the spectrometer optics themselves as part of the project. A small holographic grating disperses light onto a camera driving a large display monitor, providing a high-resolution, real-time look at the laser output spectrum. Observing this spectrum as a function of injection current, diode temperature, and optical feedback reveals several features of the laser internal optical resonator and semiconductor properties. While teaching about the intrinsically quantum phenomenon of laser physics, this experiment also provides a hands-on experience that gives students a practical familiarity working with lasers, optics, and imaging technologies.
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Plasma Physics

Chaotic Motion of Ions In Finite-amplitude Low-frequency Alfvén Waves

Jingyu Peng, Jiansen He and Rong Lin

Finite-amplitude low-frequency Alfvén waves (AWs) are ubiquitous in space plasmas, where they play a key role in the transport and dissipation of energy, particularly in the heating of ions in the solar corona and solar wind. In this study, we investigate the nonlinear interaction between ions and obliquely propagating AWs. When the wave amplitude and propagation angle lie within specific ranges, ion motion becomes chaotic. We quantify this behavior using the maximum Lyapunov exponent ($\lambda_{\mathrm{m}}$) and define a new parameter, the Chaos Ratio (CR), to describe the fraction of chaotic particles across different initial states. The global chaos threshold is determined as the contour CR = 0.01. Analysis of magnetic moment variations reveals that the physical origin of chaos is pitch-angle scattering induced by wave-driven field-line curvature (WFLC), which disrupts adiabatic invariance and leads to stochastic ion energization. The onset condition for chaos can be expressed by an effective relative curvature radius, $P_{eff.} < 25$. This analytical criterion delineates the boundary of the chaotic region in the ($k_x$, $k_z$, $B_w$) parameter space and agrees well with numerical results. The identified WFLC mechanism provides a new physical pathway for converting macroscale Alfvénic disturbances into microscopic ion heating. This analysis offers a simplified model that illustrates a plausible ion energization mechanism in Alfvénic turbulent plasmas, including those associated with solar wind switchbacks and coronal fluctuations. These results highlight a universal chaotic process that may underlie stochastic heating in heliospheric and astrophysical plasmas.
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Data Analysis and Statistics · Cross-listed from q-bio.NC

Quantifying spike train synchrony and directionality: Measures and Applications

Thomas Kreuz

By introducing the twin concepts of reliability and precision along with the corresponding measures, Mainen and Sejnowski's seminal 1995 paper "Reliability of spike timing in neocortical neurons" (Mainen and Sejnowski, 1995) paved the way for a new kind of quantitative spike train analysis. In subsequent years a host of new methods was introduced that measured both the synchrony among neuronal spike trains and the directional component, e.g. how activity propogates between neurons. This development culminated with a new class of measures that are both time scale independent and time resolved. These include the two spike train distances ISI- and SPIKE-Distance as well as the coincidence detector SPIKE-Synchronization and its directional companion SPIKE-Order. This article will not only review all of these measures but also include two recently proposed algorithms for latency correction which build on SPIKE-order and aim to optimize the spike time alignment of sparse spike trains with well-defined global spiking events. For the sake of clarity, all these methods will be illustrated on artificially generated data but in each case exemplary applications to real neuronal data will be described as well.
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Optics

Multi-NA Metalens Array for Compact High-NA Microscopy

Alireza Khalilian, Jie Fan, Mehdi Sh. Yeganeh et al. (+2 more)

We demonstrate a CMOS-compatible silicon-rich nitride metalens array for visible microscopy at 660 nm. Three co-planar elements provide numerical apertures of 0.54, 0.92, and 0.97, enabling within-sample NA benchmarking without objective swaps. Imaging of annular cell monolayers labeled in the AF647 ZO-1 channel shows progressive sharpening of junctional edges and reduced blur with increasing NA, consistent with diffraction-limited scaling. The compact, planar platform supports direct integration with image sensors, offering a practical route to high-NA fluorescence bioimaging in space-constrained, low-cost systems.
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All authors Alireza Khalilian, Jie Fan, Mehdi Sh. Yeganeh, Joe Lo, Yasha Yi

Fluid Dynamics

Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning

Xuemin Liu, Tom Hickling and Jonathan F. MacArt

We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and large eddy simulation are employed to model compressible, unconfined flow over two- and three-dimensional semi-infinite NACA 0012 airfoils at angles of attack $\alpha = 5^\circ$, $10^\circ$, and $15^\circ$. Control actions, implemented through a blowing/suction jet at a fixed location and geometry on the upper surface, are adaptively determined by a neural network that maps local pressure measurements to optimal jet total pressure, enabling a sensor-informed control policy that responds spatially and temporally to unsteady flow conditions. The sensitivities of the flow to the neural network parameters are computed using the adjoint Navier-Stokes equations, which we construct using automatic differentiation applied to the flow solver. The trained flow controllers significantly improve the lift-to-drag ratios and reduce flow separation for both two- and three-dimensional airfoil flows, especially at $\alpha = 5^\circ$ and $10^\circ$. The 2D-trained models remain effective when applied out-of-sample to 3D flows, which demonstrates the robustness of the adjoint-trained control approach. The 3D-trained models capture the flow dynamics even more effectively, which leads to better energy efficiency and comparable performance for both adaptive (neural network) and offline (simplified, constant-pressure) controllers. These results underscore the effectiveness of this learning-based approach in improving aerodynamic performance.
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Physics and Society

Data as Commodity: a Game-Theoretic Principle for Information Pricing

Pasquale Casaburi, Giovanni Piccioli and Pierpaolo Vivo

Data is the central commodity of the digital economy. Unlike physical goods, data exhibits properties that defy the standard theory of supply and demand: it is non-rival (the same dataset can be sold to multiple buyers without degradation), it is replicable at near-zero cost, and it is traded under heterogeneous licensing rules that restrict lawful use. Determining a new pricing principle to attach a fair price tag to datasets is therefore a difficult but central problem. We propose a game-theoretic framework in which the value of a data string emerges from strategic competition among $N$ players betting on a stochastic process with asymmetric information about past outcomes. A better-informed player may either exploit her advantage or sell part of her dataset to less informed competitors. By analytically deriving the Nash equilibrium, we identify the price range for a mutually beneficial trade. The model reveals market dynamics that depart from textbook intuition: informed players may compete or jointly exploit the least informed; data can be shared even at zero price without reducing the seller`s utility; rivalry among well-informed players can benefit uninformed ones; and trades infeasible in small markets can be viable in larger ones. These findings establish a theoretical foundation for the pricing of intangible goods in interacting digital markets, which are in need of robust valuation principles.
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Optics

Engineering Rogue Waves via Multimode Interactions in Integrated Waveguides

Gülsüm Yaren Durdu, Azka Maula Iskandar Muda and Uğur Teğin

We explore rogue wave formation in multimode silicon nitride (Si$_3$N$_4$) waveguides with multimode nonlinear Schrödinger equation-based simulations. Pure fundamental-mode excitation produces smooth propagation without extreme events, whereas higher-order modes or multimode superpositions yield heavy-tailed statistics with bursts exceeding the $8\sigma$ threshold. These results reveal that rogue wave generation in integrated waveguides is controlled not only by material properties such as nonlinearity and dispersion but also by modal excitation and intermodal nonlinear interactions. Our results identify modal control as a new degree of freedom for engineering extreme spatiotemporal events on photonic chips, with implications for on-chip supercontinuum generation, frequency combs, and nonlinear wave management.
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Optics

Dispersion and the transport of exciton-polaritons in an optical conveyor belt

Xingran Xu, Chunyu Jia and Xin-Xin Yang

The growing interest in exciton-polaritons has driven the need to manipulate their motion and engineer their band structures to the forefront of contemporary research. This study explores the band structures that emerge from a spatially modulated potential, ingeniously realized through the use of an optical conveyor belt. By leveraging Bloch theory and conducting a meticulous analysis of the time evolution of polariton intensity in Fourier space, we have derived the energy dispersion relations both analytically and numerically within the context of a static lattice model. For time-dependent potentials, we employ the Lagrange variational method to elucidate the dynamics of polariton motion. Our results reveal that polaritons exhibit linear dispersion and follow linear trajectories with minor oscillations superimposed. This investigation not only deepens our fundamental understanding of exciton-polaritons but also provides a robust tool for advancing photonic devices and exerting precise control over current transport in quantum computing. Our findings pave the way for future innovations in high-speed and high-performance technologies.
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Instrumentation and Detectors

Experimental Results from Early Non-Planar NI-HTS Magnet Prototypes for the Columbia Stellarator eXperiment (CSX)

D. Schmeling, M. Russo, B. T. Gebreamlak et al. (+12 more)

The Columbia Stellarator eXperiment (CSX) is an upgrade of the Columbia Non-neutral Torus (CNT) that aims to demonstrate a university-scale, quasi-axisymmetric stellarator using high-temperature superconducting (HTS) technology at an on-axis magnetic field target of 0.5 T. Due to the strain sensitivity of ReBCO (Rare-earth Barium Copper Oxides), adapting it to non-planar stellarator geometries requires new winding, structural, and cooling strategies. We report on the results of a staged prototype program (P1, P2, P3) employing 3D-printed, sectional aluminum coil frames with winding channels, gimballed constant-tension winding mechanics, and solder potting for radial current redistribution and passive quench mitigation. The first prototype, P1 (planar elliptical, double-pancake) tested additive manufacture, sectional joining and baseline winding, achieving predicted fields at 77 K. P2 (non-planar, higher strain) was wound to 42 turns, energized at 30-40 K to produce expected magnetic fields, and studied thermal gradients and resistance at up to 4.5 kAt. Design evolution in P3 introduces concave geometry with dual double-pancakes, 200 turns, and approaches the 70 kAt target at 20 K. In parallel, sub-microhm lap joints have been developed. Together, these results de-risk manufacturing, cooling interfaces, quench management, and diagnostics, paving the way for full-size non-planar HTS stellarator coils for CSX.
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All authors D. Schmeling, M. Russo, B. T. Gebreamlak, T. J. Kiker, A. R. Skrypek, A. R. Hightower, J. Xue, S. Chen, S. Sohaib, C. Martinez, K. F. Richardson, L. Filor, S. Komatsu, L. Liu, C. Paz-Soldan

Biological Physics

Space-filling discrete helices

Jayanth R. Banavar, Achille Giacometti, Trinh X. Hoang et al. (+2 more)

Proteins are linear chain molecules that play a central role in life and health. Protein native state folds are modular assemblies of space-filling building blocks of {\alpha}-helices, \{beta}-sheets and tight turns. Here we deduce the structures of a countable set of space-filling helical forms of a uniform discrete thick string from first principles with no additional input or adjustable parameters. These forms occur in correspondence with the natural numbers, loosely analogous to the energy levels in a Bohr atom. We find the remarkable result that one of these helical forms is an excellent candidate for an {\alpha}-helix through seemingly improbable quantum chemistry coincidences that fit the geometrical requirements. Our work suggests that geometry and chemistry are complementary ways of looking at proteins and suggests a route for developing a unified framework for understanding proteins.
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All authors Jayanth R. Banavar, Achille Giacometti, Trinh X. Hoang, Amos Maritan, Tatjana Škrbić

Fluid Dynamics

Machine Learning enhanced parametric Reynolds-averaged Navier-Stokes equations at the full- and reduced-order levels

Davide Oberto, Maria Strazzullo and Stefano Berrone

In this contribution, we focus on the Reynolds-averaged Navier-Stokes (RANS) models and their exploitation to build reliable reduced-order models to further accelerate predictions for real-time applications and many-query scenarios. Specifically, we investigate how machine learning can be employed to enhance the predictive capabilities of the model, both at the full-order model (FOM) and reduced-order model (ROM) levels. We explore a novel integration of these two areas. We generate the FOM snapshots, essential for ROM construction, using a data-driven RANS model: the $\nu_t$-Vector Basis Neural Network. This is the first time that this machine learning procedure covers a large parametric variation, and we propose tailored training strategies to increase the accuracy of the FOM model. At the ROM level, we compare the results obtained by standard proper orthogonal decomposition (POD) in an intrusive Galerkin setting (PODG) and POD neural network approach (PODNN). The numerical validation is conducted on a classic turbulent square-duct flow benchmark, with the bulk Reynolds number as the sole varying parameter. Our investigation reveals that the PODG method proves to be unstable and inaccurate for turbulent flow prediction, while PODNN demonstrates superior performance in terms of accuracy and computational efficiency.
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Optics

All-optical polarization control in time-varying low-index films via plasma symmetry breaking

Wallace Jaffray, Sven Stengel, Alexandra Boltasseva et al. (+6 more)

Controlling the polarization state of light with sub-picosecond speed and subwavelength precision remains a key challenge for next-generation nanophotonic devices. Conventional methods such as birefringent crystals, liquid crystals, or electro-optic Pockels cells are limited in speed, compactness, and energy efficiency. While structured materials and two-dimensional heterostructures offer potential for on-chip ultrafast performance, achieving all-optical control remains an open problem. Here we introduce an all-optical scheme that employs femtosecond pumping of low-index, subwavelength isotropic films to achieve ultrafast control over birefringence, dichroism, and optical activity within a single material platform. When the material is probed near its crossover wavelength, linearly polarized pumping induces a transient phase retardation up to 0.1{\pi} per micrometer, accompanied by a dichroic absorption ratio of approximately twenty. Under circularly polarized excitation, the probe experiences nonreciprocal optical activity, leading to polarization rotation of about 1.1 degrees per micrometer. A universal hydrodynamic model quantitatively reproduces these effects and attributes them to pump-induced symmetry breaking in the photoexcited carrier plasma. This symmetry breaking enables coupling between orthogonal probe polarization components, mediated by a modified time-dependent damping term, which connects to the inverse Faraday effect induced by a circularly polarized pump. Our combined experimental and theoretical study establishes a reconfigurable, deep-subwavelength polarization-control mechanism operating on sub-picosecond timescales, suitable for compact ultrafast modulators, dynamic metasurfaces, and tunable nonreciprocal photonic devices, with implications for quantum optics, ultrafast logic, and time-resolved sensing.
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All authors Wallace Jaffray, Sven Stengel, Alexandra Boltasseva, Vladimir M. Shalaev, Maria Antonietta Vincenti, Domenico De Ceglia, Michael Scalora, Carlo Rizza, Marcello Ferrera

Optics · Cross-listed from nlin.PS

Transverse instability of hybrid solitons in the strong light-matter coupling regime

Alexey V. Yulin and Dmitry A. Zezyulin

We investigate the transverse instability of two-component solitons forming in a planar waveguide operating in the regime of strong light-matter coupling. The instability emerges as a result of the coupling between transverse diffraction of the photonic component and nonlinearity of the material excitations. Solutions of three different forms are addressed which include bright, gray-dark, and gray-gray solitons. In the limit of long-wavelength transverse perturbations, the instability is described with an asymptotic expansion whose predictions agree with the results of numerical simulations. The dynamic development of instability of initially perturbed bright solitons leads to the formation of high-intensity spots in the photonic component. For gray-dark and dark-dark solitons, the transverse instability leads to the spontaneous nucleation of vortex-antivortex pairs which emerge in both fields as transient patterns.
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Applied Physics · Cross-listed from quant-ph

Realistic Threat Models for Fiber and Free-Space Continuous-Variable Quantum Key Distribution

Zhiyue Zuo, Masoud Ghalaii and Stefano Pirandola

Future global quantum communication networks, or quantum Internet, will realize high-rate secure communication and entanglement distribution for large-scale users over long distances. Continuous variable (CV) quantum key distribution (QKD) provides a powerful setting for secure quantum communications, thanks to the use of room-temperature off-the-shelf optical devices and the potential to reach high rates. However, the achievable performance of CV-QKD protocols is fundamentally limited by the fact that they appear to be fragile to both loss and noise. In this study, we provide a general framework for analyzing the composable finite-size security of CV-QKD with Gaussian-modulated coherent-state protocol (GMCS) under various levels of trust for the loss and noise experienced by the users of the protocol. Our work is comprehensive of several practical scenarios, encompassing both active and passive eavesdropping configurations, with both wired (i.e., fiber-based) and wireless (i.e., free-space and satellite-based) quantum communication channels. Our numerical results evaluate the robustness of the GMCS protocol under varying levels of trust and demonstrate that it is difficult for a practical protocol to remain robust against untrusted loss at the transmitter. In the wireless case, we analyze a scenario with a sun-synchronous satellite, showing that its key distribution rate, even with the worst level of trust, can outperform a ground chain of ideal quantum repeaters. Our results indicate that, when it comes to engineering and optimizing quantum-safe networks, it is essential to mitigate the shortcomings caused by critical trade-offs between rate performance, trust level, system noise, and communication distance.
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Data Analysis and Statistics · Cross-listed from cs.LG

Fisher Information, Training and Bias in Fourier Regression Models

Lorenzo Pastori, Veronika Eyring and Mierk Schwabe

Motivated by the growing interest in quantum machine learning, in particular quantum neural networks (QNNs), we study how recently introduced evaluation metrics based on the Fisher information matrix (FIM) are effective for predicting their training and prediction performance. We exploit the equivalence between a broad class of QNNs and Fourier models, and study the interplay between the effective dimension and the bias of a model towards a given task, investigating how these affect the model's training and performance. We show that for a model that is completely agnostic, or unbiased, towards the function to be learned, a higher effective dimension likely results in a better trainability and performance. On the other hand, for models that are biased towards the function to be learned a lower effective dimension is likely beneficial during training. To obtain these results, we derive an analytical expression of the FIM for Fourier models and identify the features controlling a model's effective dimension. This allows us to construct models with tunable effective dimension and bias, and to compare their training. We furthermore introduce a tensor network representation of the considered Fourier models, which could be a tool of independent interest for the analysis of QNN models. Overall, these findings provide an explicit example of the interplay between geometrical properties, model-task alignment and training, which are relevant for the broader machine learning community.
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Applied Physics

Lamb wave-based MVDR imaging and CNN classification of defects in pipelines

Shuangshuang Li and Kai Zhao

Significant progress has been made in ultrasonic guided wave (UGW) technology for pipe signal processing and defect imaging recently. However, developing a defect localization and imaging algorithm that requires fewer parameters, offers a wide imaging range, and achieves high positioning accuracy remains a considerable challenge. Traditional direction-of-arrival (DOA) estimation methods primarily focus on the single-angle estimation with low resolution, failing to satisfy the spatial localization requirements for pipeline defects. Therefore, a high-resolution spatial spectrum estimation algorithm is introduced to realize the two-dimensional DOA estimation. By distributing multiple sensors in a specific geometric configuration to form an array, this method employs the array signal processing technology to accurately obtain the DOA of spatial signals. A uniform circular array (UCA) is employed in the present study to receive signals from pipe defects, and high-precision localization and imaging of defects are achieved based on the two-dimensional minimum variance distortionless response (MVDR) beamforming algorithm, with a relative positioning error of less than 1%. An image classification method based on the convolutional neural network (CNN) is further developed to distinguish the defect types. By constructing a novel CNN model to extract defect features and perform classification, this model achieves a prediction accuracy of 97.50%, which effectively distinguishes between defect types.
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Fluid Dynamics

Regulation of droplet size and flow regime by geometrical confinement in a microfluidic flow-focusing device

Somasekhara Goud Sontti and Arnab Atta

We have developed a coupled level set and volume of fluid-based computational fluid dynamics model to analyze the droplet formation mechanism in a square flow-focusing microchannel. We demonstrate a flexible manipulation of droplet formation and flow regime based on the modified flow-focusing microchannel with a constricted orifice. Furthermore, we have systematically studied the influence of geometrical confinement, flow rate, and interfacial tension on the droplet formation regime, length, volume, velocity, and shape. Three different flow regimes, namely squeezing, dripping, and jetting, are observed, and the flow regime maps are formulated based on the Reynolds and capillary numbers. After an extensive numerical investigation, we described the boundaries between the different regimes. Droplet shape is also quantified based on the deformation index value. Plug-shaped droplets are observed in the squeezing regime, and near spherical droplets are found in the dripping and jetting regimes. Our study provides insights into the transition of a regime under various geometrical confinement and fluid properties. The results reveal that the modified flow-focusing microchannel can substantially enhance dripping while decreasing the squeezing regime, which is of paramount importance from the standpoint of producing high throughput stable and monodisperse microdroplets. Eventually, this work emphasizes the importance of geometrical confinement, fluid properties, and flow conditions on the droplet formation process in a flow-focusing microchannel that can effectively provide helpful guidelines on the design and operations of such droplet-based microfluidic systems.
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Medical Physics · Cross-listed from nucl-ex

Measurement of the $^{35}Cl(n, p)^{35}S$ cross-section at the CERN n\_TOF facility from subthermal energy to 120 keV

Marco Antonio Martínez-Cañadas, Pablo Torres-Sánchez, Javier Praena et al. (+130 more)

Background: The $^{35}Cl(n, p)^{35}S$ reaction is of special interest in three different applications. First, in Boron Neutron Capture Therapy due to the presence of $^{35}Cl$ in brain and skin tissue. Second, it is involved in the creation of $^{36}S$, whose astrophysical origin remains unresolved. Third, in the designing of fast nuclear reactors of new generation based on molten salts. Purpose: To measure the $^{35}Cl(n, p)^{35}S$ cross-section from thermal energy to 120 keV, determine the resonance parameters in this range and Maxwellian Averaged Cross-Section (MACS). Method: We made use of the Time-of-Flight technique with microMEGAS detectors at Experimental Area 2 (EAR-2) of n\_TOF facility at CERN. The $^{10}B(n, \alpha)^{7}Li$ and $^{235}U(n, f)$ reactions were used as references. Rutherford Back-scattering Spectrometry technique was performed at Centro Nacional de Aceleradores (CNA) in Sevilla, in order to accurately determine the masses of the irradiated samples. Results: We obtain a thermal cross-section of $0.470 \pm 0.009$ barns. The $1/v$ energy dependence of the cross-section is observed up to the first resonance at 0.398 keV, the resonances up to 120 keV are analyzed and MACS calculated for $k_{B} T$ from 1 to 100 keV. Conclusions: The $^{35}Cl(n, p)^{35}S$ cross-section has been obtained over a wide energy range for the first time, with high accuracy across the aforementioned range. The thermal cross-section and first two resonances are in agreement with latest evaluation in ENDF/B-VIII.1, while lower resonance strength was found for high energy resonances. These data are used to calculate the MACS for different $k_{B} T$.
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All authors Marco Antonio Martínez-Cañadas, Pablo Torres-Sánchez, Javier Praena, Ignacio Porras, Marta Sabaté-Gilarte, Oliver Aberle, Victor Alcayne, Simone Amaducci, Józef Andrzejewski, Laurent Audouin, Vicente Bécares, Victor Babiano-Suarez, Michael Bacak, Massimo Barbagallo, František Bečvář, Giorgio Bellia, Eric Berthoumieux, Jon Billowes, Damir Bosnar, Adam Brown, Maurizio Busso, Manuel Caamaño, Luis Caballero, Francisco Calviño, Marco Calviani, Daniel Cano-Ott, Adria Casanovas, Francesco Cerutti, Yonghao Chen, Enrico Chiaveri, Nicola Colonna, Guillem Cortés, Miguel Cortés-Giraldo, Luigi Cosentino, Sergio Cristallo, Lucia-Anna Damone, Maria Diakaki, Mirco Dietz, César Domingo-Pardo, Rugard Dressler, Emmeric Dupont, Ignacio Durán, Zinovia Eleme, Beatriz Fernández-Domínguez, Alfredo Ferrari, Francisco Javier Ferrer, Paolo Finocchiaro, Valter Furman, Kathrin Göbel, Ruchi Garg, Aleksandra Gawlik-Ramięga, Benoit Geslot, Simone Gilardoni, Tudor Glodariu, Isabel Gonçalves, Enrique González-Romero, Carlos Guerrero, Frank Gunsing, Hideo Harada, Stephan Heinitz, Jan Heyse, David Jenkins, Erwin Jericha, Franz Käppeler, Yacine Kadi, Atsushi Kimura, Niko Kivel, Michael Kokkoris, Yury Kopatch, Milan Krtička, Deniz Kurtulgil, Ion Ladarescu, Claudia Lederer-Woods, Helmut Leeb, Jorge Lerendegui-Marco, Sergio Lo Meo, Sarah-Jane Lonsdale, Daniela Macina, Alice Manna, Trinitario Martínez, Alessandro Masi, Cristian Massimi, Pierfrancesco Mastinu, Mario Mastromarco, Francesca Matteucci, Emilio-Andrea Maugeri, Annamaria Mazzone, Emilio Mendoza, Alberto Mengoni, Veatriki Michalopoulou, Paolo Maria Milazzo, Federica Mingrone, Agatino Musumarra, Alexandru Negret, Ralf Nolte, Francisco Ogállar, Andreea Oprea, Nikolas Patronis, Andreas Pavlik, Jarosław Perkowski, Luciano Persanti, José-Manuel Quesada, Désirée Radeck, Diego Ramos-Doval, Thomas Rauscher, René Reifarth, Dimitri Rochman, Carlo Rubbia, Alok Saxena, Peter Schillebeeckx, Dorothea Schumann, Gavin Smith, Nikolay Sosnin, Athanasios Stamatopoulos, Giuseppe Tagliente, José Tain, Zeynep Talip, Ariel Tarifeño-Saldivia, Laurent Tassan-Got, Andrea Tsinganis, Jiri Ulrich, Sebastian Urlass, Stanislav Valenta, Gianni Vannini, Vincenzo Variale, Pedro Vaz, Alberto Ventura, Vasilis Vlachoudis, Rosa Vlastou, Anton Wallner, Philip John Woods, Tobias Wright, Petar Žugec

Geophysics

Accelerating wave simulations with neural dispersion correctors

Felipe Rincón, Andreas Fichtner, Mattia Aleardi et al. (+2 more)

We present a Fourier neural operator network, designed to correct dispersion errors in numerical wave simulations. The neural dispersion corrector enables the replacement of a computationally expensive high-accuracy simulation by a less expensive low-accuracy simulation. In contrast to neural network surrogates that fully replace a wave equation, the neural dispersion corrector has only a weak dependence on the distribution of model parameters, such as wave speeds. Consequently, the network can be trained with a significantly smaller dataset, while still generalising to unseen input parameters. Following a description of the network architecture and training, we provide examples for the 3-D elastic wave equation. After training with merely 1$\,$000 examples on one GPU, the neural corrector achieves a speed-up of 16$\times$ compared to a reference spectral-element simulation and a generalisation to a broad range of strongly heterogeneous wave speed distributions.
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All authors Felipe Rincón, Andreas Fichtner, Mattia Aleardi, Andrea Tognarelli, Eusebio Stucchi

Optics

Versatile 3D reconstruction framework for hard X-ray grazing incidence imaging of nanostructures

Luke Besley, P. S. Jørgensen, A. Diaz et al. (+6 more)

Coherent imaging techniques such as ptychography offer powerful capabilities for 3D resolution of nanoscale structures. By application in grazing incidence, such techniques may achieve exceptional surface sensitivity as demonstrated by grazing incidence small angle scattering. This requires however an extension of the conventional analysis based on the Distorted Wave Born Approximation which is typically limited to stratified-layer models and statistical descriptions of in-plane structures. The prevailing implementations of reconstruction algorithms for ptychography based on the projection approximation fails to capture the significant multiple scattering that occurs in grazing incidence. We present a ptychographic reconstruction framework that replaces the single-scattering model with a multislice wave-propagation formalism tailored to grazing incidence. The framework supports simultaneous phase retrieval and reconstruction, and can incorporate multiple incidence angles, multiple rotation angles, and flexible experimental geometries into a single inversion. Reconstructions can be initialized from a random guess without strong structural priors, enabling the recovery of complex surface and near-surface nanostructures. This reconstruction framework is applied to both experimental and simulated datasets, demonstrating its versatility.
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All authors Luke Besley, P. S. Jørgensen, A. Diaz, C. Detlefs, S. De Angelis, M. Carlsen, B. Chang, C. Silvestre, J. W. Andreasen

Computational Physics

GPU-MetaD: Full-Life-Cycle GPU Accelerated Metadynamics with Machine Learning Potentials

Haoting Zhang, Qiuhan Jia, Zhennan Zhang et al. (+6 more)

Large-scale molecular dynamics simulations with high accuracy have been increasingly popular for their capability to bridge the gap between atomistic modeling and mesoscale phenomena. Both machine learning potentials and enhanced sampling approaches offer substantial improvements in high-accuracy simulation efficiency, which can be further boosted through GPU acceleration. However, an efficient framework combining these advances for extending simulations to large systems and long timescales remains elusive. In this work, we proposed a full-life-cycle GPU accelerated metadynamics simulations package GPU-MetaD. Benchmarking across molecular, interface, and bulk systems demonstrates that GPU-MetaD efficiently handles diverse atomic systems and delivers an order-of-magnitude performance improvement. Building on this demonstrated capability, it enables ab-initio-level rare-event sampling for systems comprising millions of atoms on a typical single GPU. This capability allows us to reveal a previously unknown size-dependent two-step nucleation mechanism in gallium nitride (GaN), highlighting the potential of GPU-MetaD for uncovering complex rare events in realistic large-scale materials systems.
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All authors Haoting Zhang, Qiuhan Jia, Zhennan Zhang, Yijie Zhu, Zhongwei Zhang, Junjie Wang, Jiuyang Shi, Zheyong Fan, Jian Sun

Computational Physics · Cross-listed from quant-ph

A Quantum Linear Systems Pathway for Solving Differential Equations

Abhishek Setty

We present a systematic pathway for solving differential equations within the quantum linear systems framework by combining block encoding with Quantum Singular Value Transformation (QSVT). The approach is demonstrated on a complex tridiagonal linear system and extended to problems in computational fluid dynamics: the heat equation with mixed boundary conditions and Carleman-linearized nonlinear Burgers' equation. Our scaling analysis of the heat equation identifies regimes where classical computation remains feasible and estimates circuit depths required to achieve potential quantum advantage. We further evaluate post-selection success probabilities for the presented examples and provide hardware resource estimates for block encoding and QSVT circuits in terms of two-qubit gate depth, evaluated on IBM superconducting processors with heavy-hex and square lattice topologies. These results highlight both the practical limitations of current hardware and key directions for depth reduction and scalable quantum linear solvers.
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Applied Physics

Low and high frequency noise in LEDs

Danylo Bohomolov, Vita Ivanova and Ulrich T. Schwarz

LED degradation is usually associated with defects in the active region. Whereby the noise analysis can be a strong instrument to reveal them. The results of optical noise measurements for commercially available blue LED samples in a wide frequency range from kHz to MHz are reported. Noise spectra were decomposed into components according to the presented theoretical model which includes 1/f-type noise, generation-recombination noise, and white noise. The 1/f$^\gamma$-type noise was modeled as a superposition of generation-recombination noise components at defects with a continuous wide distribution of relaxation lifetimes. The coincidence of the experimental results with 1/f$^\gamma$ model for the low frequency range is proved and a fitting is made. We identified three noise components that are highly current-dependent. The corresponding model of temperature dependence for the low-frequency range was developed. At low currents, the model partially matched the experimental results in the temperature range from 100 K to 300 K at low frequencies. However, high-frequency measurements showed deviations from the expected Lorentzian behavior.
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Applied Physics · Cross-listed from cs.LG

Early wind turbine alarm prediction based on machine learning: AlarmForecasting

Syed Shazaib Shah and Daoliang Tan

Alarm data is pivotal in curbing fault behavior in Wind Turbines (WTs) and forms the backbone for advancedpredictive monitoring systems. Traditionally, research cohorts have been confined to utilizing alarm data solelyas a diagnostic tool, merely indicative of unhealthy status. However, this study aims to offer a transformativeleap towards preempting alarms, preventing alarms from triggering altogether, and consequently avertingimpending failures. Our proposed Alarm Forecasting and Classification (AFC) framework is designed on twosuccessive modules: first, the regression module based on long short-term memory (LSTM) for time-series alarmforecasting, and thereafter, the classification module to implement alarm tagging on the forecasted alarm. Thisway, the entire alarm taxonomy can be forecasted reliably rather than a few specific alarms. 14 Senvion MM82turbines with an operational period of 5 years are used as a case study; the results demonstrated 82%, 52%,and 41% accurate forecasts for 10, 20, and 30 min alarm forecasts, respectively. The results substantiateanticipating and averting alarms, which is significant in curbing alarm frequency and enhancing operationalefficiency through proactive intervention.
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Biological Physics · Cross-listed from cond-mat.soft

The Hidden Wheel-Within

Falko Ziebert and Igor M. Kulić

There is this old, eternal question: Why don't animals have wheels? In this perspective we show that they actually do. And they do so in a physically extraordinary way – by combining incompatible elasticity, differential geometry and dissipative self-organization. Nature's wheel – the “wheel-within” – has been mysteriously concealed in plain sight, yet it spins in virtually every slender-body organism: in falling cats, crocodilians spinning to subdue their prey, rolling fruit-fly larvae, circumnutating plants and even in some of our own body movements. Flying somehow under the radar of our cognition, in recent years the wheel-within also tacitly entered the field of soft robotics, finally opening our eyes for its ubiquitous role in Nature. We here identify its underlying physical ingredients, namely the existence of a neutrally-stable, shape-invariant and actively driven elastic mode. We then reflect on various man-made realizations of the wheel-within and outline where it could be spinning from here.
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Optics

Can a vector beam be critically coupled leading to perfect absorption?

Sauvik Roy, Nirmalya Ghosh, Ayan Banerjee et al. (+1 more)

Critical coupling has emerged as a prominent area of research in recent years. However, most theoretical models are based on scalar theories (and occasionally coupled mode theories), which inadequately account for the polarization states of the incident light. To bridge this gap, we revisit the concept of critical coupling in planar multilayer structures using a full vectorial theory, where conventional plane wave illumination is replaced by well-defined vector beams with and without orbital angular momentum (OAM). Our investigation explores the possibility of complete absorption of monochromatic beams without and with intrinsic OAM (such as Gaussian and Laguerre-Gaussian (LG)), incident on the multilayer structure at normal or oblique incidence. A two-component metal-dielectric composite film is chosen as the absorbing layer in the system. Our results demonstrate a significant reduction in the intensities of the reflected and transmitted beams at normal incidence, with reduced efficiency for oblique incidence due to the lack of spatial overlap of multiply reflected components. Interestingly, we also observe super-scattering from the same structures when conditions for constructive interference of the various reflected components are satisfied. This work highlights the need to incorporate the vector nature of beams by retaining the complete polarization information of off-axis spatial harmonics in future studies.
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All authors Sauvik Roy, Nirmalya Ghosh, Ayan Banerjee, Subhasish Dutta Gupta

Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

Thermodynamics of data

Borko Stosic

The recently introduced concept of generalized thermodynamics is explored here in the context of 1d, 2d and 3d data analysis, performed on samples drawn from a 3d X-ray soil sample image. Different threshold levels are used to binarize the 3d sample, wherefrom relative frequencies of binary patterns are found and then used to address finite size scaling behavior of the response functions as a function of the disorder parameter (equivalent of temperature in thermodynamics). It is found that for different threshold levels response functions for increasing sample sizes approach the thermodynamic limit from different directions, with a crossover reminiscent of a transition from open to periodic boundaries of the Ising model, implying existence of a characteristic correlation scale. It is argued here that this characteristic scale corresponds to the "natural" properties of the data, where correlations within finite size samples are neither underestimated nor overestimated. In the current context of soil this scale may be related to the so-called Representative elementary volume (REV), while in other situations this characteristic scale should be interpreted in the context of the phenomenon under study.
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Atomic Physics

Precision measurement of the $^{176}\mathrm{Lu}^+$ $^3D_1$ microwave clock transitions

M. D. K. Lee, Qi Zhao, Qin Qichen et al. (+4 more)

We report precision measurement of the unperturbed ${^{3}}D_1$ microwave transition frequencies in $^{176}\mathrm{Lu}^+$ to a fractional uncertainty of $4\times10^{-14}$. We find the $|F,m_F\rangle=|8,0\rangle$ to $|7,0\rangle$ hyperfine transition frequency to be $10\,491\,519\,945.228\,82(38)\,$Hz and the $|7,0\rangle$ to $|6,0\rangle$ transition frequency to be $11\,290\,004\,289.881\,61(36)\,$ Hz. At this precision we are able to observe the hyperfine-mediated effects in the ratio of the quadrupole shifts, from which we can directly infer the residual quadrupole moment after $^3D_1$ hyperfine averaging. We find a residual quadrupole moment of ${-2.48(23)\times10^{-4}}\,e a_0^2$, consistent with a previous assessment using a different and less direct method. With the unperturbed microwave frequencies accurately known, the residual quadrupole shift for a $^{176}\mathrm{Lu}^+$ ($^3D_1$) optical frequency standard can henceforth be readily evaluated to $<10^{-20}$ uncertainty by routine ${^{3}}{D}_1$ microwave spectroscopy.
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All authors M. D. K. Lee, Qi Zhao, Qin Qichen, Zhao Zhang, N. Jayjong, K. J. Arnold, M. D. Barrett