Papers

The luminosity quoted for proposed particle colliders is usually derived from the analytic geometric formula, which assumes head-on collisions of identical, rigid Gaussian bunches. For the new generation of asymmetric machines—in particular lepton–hadron colliders such as the EIC, LHeC and FCC-eh—this assumption can misestimate the luminosity, because it ignores a few coupled effects that act in the interaction region such as hourglass effect, crossing angle, and mutual electromagnetic (beam–beam) deformation of the colliding bunches. We present AloHEP (A luminosity optimizer for High Energy Physics), an open-source, cross-platform Java application that simulates the interaction region of both symmetric and asymmetric colliders. AloHEP represents each bunch by an ensemble of macroparticles deposited on the grid with a cloud-in-cell scheme, and slices the crossing longitudinally. On each transverse slice it solves the two-dimensional electrostatic problem by a direct Green's-function convolution, then obtains the beam–beam forces by finite differencing and continuous bicubic interpolation. The hourglass effect, finite crossing angle and beam–beam pinch/disruption can be enabled independently, and the two colliding beams may consist of entirely different species (leptons, protons, heavy ions). A JSON-backed parameter database and an interactive graphical interface make AloHEP a flexible tool for the rapid prototyping of realistic luminosity estimates. We verify that the code reproduces the analytic geometric luminosity in the appropriate limit and present scaling, grid-convergence and asymmetric-collider benchmarks.

Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed. We construct a benchmark of 7,599 multicomponent configurations inspired by high-entropy design, elemental substitution and anion mixing. Eleven pretrained models are evaluated against density functional theory for energies, forces and stresses, with assessment extended to elastic, vibrational and adsorption-related properties. Force errors are analysed through training-reference coverage, local geometric heterogeneity, distance directionality and elemental response. Distances to training-reference environments reveal a qualitative association between coverage differences and increasing errors, while substantial variation remains at similar distances. Higher-error groups show greater local geometric heterogeneity, although OMat24 provides broad coverage of these environments. Relative to training-reference pair medians, errors remain low near the median, rise steeply on the compression side and increase more weakly on the extension side. After matching element pairs and absolute distance deviations, compression-side force errors are 1.81-1.95 times extension-side errors. Model-predicted pairwise interaction curves show greater curvature under compression. Fitting difficulty in independent elemental systems correlates with electronic band-energy responses to atomic displacements and Fermi-level shifts, and a similar pattern is observed in multicomponent systems. In parameter-matched comparisons, spherical-harmonic representations with maximum degrees of 2 and 4 lower test force errors for 38 and 40 of 43 elements, respectively, while differences in elemental difficulty remain. These findings inform force-field selection for experimental compositional design and identify targets for training-data sampling and model representations.

Third-order dispersion turns the soliton of the nonlinear Schrödinger equation, which underlies optical analogues of event horizons in fibers, into a weakly radiating solitary wave. We compute three of its properties without assuming a thermal emission law. First, the linearized (Bogoliubov-de Gennes) problem must be posed about the stationary state of the dispersive equation: linearizing about the integrable profile produces a complex quartet that is converged in grid and box, grows as the square root of the dispersion strength and is spurious. About the stationary state, on a periodic grid, the zero-mode sector consists of two Jordan blocks of size two and the spectrum is real at the operating point, including negative-energy box modes of the resonant branch. Second, we measure the steady Cherenkov emission of adiabatically prepared solitons from the flux of the radiation behind them, over eight decades. The radiation is phase-matched in the frame of the drifting soliton, and the first-order rate falls short of the measured one by factors of 29 to 45. Expressed through a scaled dispersion coefficient of the local soliton, the measured rate describes launched solitons of three amplitudes; with norm and momentum balance, and no further fitted parameter, it reproduces their recoil and the slow decay of their loss rate; at weak dispersion the loss of a launched soliton is the shedding of its launch mismatch. Third, a census of the asymptotic channels shows that long-wavelength upstream emission requires the chemical potential of the soliton, lowered by the comoving-frame Doppler shift of a drifting soliton, to lie inside the analysis band, which bounds the soliton amplitude. In a kinematic model of the flow the surface gravity has a closed form; the thermal relation and the entanglement built on it are imposed inputs of that model. The construction applies to Raman-free Kerr media.

Cooperative behavior can improve environmental quality, whereas selfish defection can degrade shared resources. In a recent agent-based study of the Aksum civilization (Vasellini et al., 2025), a population of agents moving over a reconstructed landscape and playing a local Public Goods Game grew faster and peaked higher when the game was enabled, but settled, in the long run, at the same population level as when it was not. Here we show that this behavior does not depend on the many rules of that model. We develop a theoretical ecological model of three coupled discrete-time equations, in which two sub-populations grow logistically and share a carrying capacity that rises when cooperators are sufficiently numerous and decays otherwise. Without fitting any parameter to the simulation, the equations reproduce its qualitative behavior: an initial boom, damped oscillations, and stabilization; a higher peak when the cooperative mechanism is active; and a final equilibrium that does not depend on it. Cooperation can be self-sustained for a transient period, but in the absence of measures that mitigate free-riding it collapses, and the environmental gains it produced are lost with it.

Accurate prediction of the electronic properties of extended $\pi$-conjugated systems with radical character remains a major theoretical challenge. To overcome this challenge, thermally-assisted-occupation density functional theory (TAO-DFT) has been developed in recent years. In this study, we employ TAO-DFT to systematically explore the electronic properties of ladder-type phenylenes ($n$-LPs), containing $n$ linearly fused four-membered carbon rings. Our calculations reveal that all investigated $n$-LPs (with $n$ = 1 ~ 40) possess singlet ground states. With increasing $n$-LP length, we observe a transition from the nonradical character of shorter $n$-LPs to the polyradical character of longer $n$-LPs. Notably, we identify a distinct damped periodic oscillation in the radical character of $n$-LPs, featuring highly correlated hotspots at specific lengths ($n = 3k - 1$, where $k$ are positive integers). The real-space representation of active orbitals confirms this global electronic behavior is governed by the intrinsic structural periodicity and completely delocalized nature of the active orbitals along the central ladder framework. These findings provide fundamental insights into the size-dependent electronic properties of $n$-LPs, guiding their rational design for future nanomaterial applications.

Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.

The increasing diversity of high performance computing systems makes separate, architecture specific implementations of lattice gauge theory algorithms costly to maintain. We present kwqft, a performance portable Kokkos implementation of Wilson pure gauge Monte Carlo simulation for $\mathrm{SU}(N)$ Yang-Mills theory in an arbitrary number of space-time dimensions. The gauge group order $N$ and the dimension $D$ are compile time parameters. A single source targets the Serial, OpenMP, CUDA, HIP, and SYCL execution spaces, with MPI halo exchange overlapped with interior updates. The implementation reproduces the exact two-dimensional plaquette and published three and four dimensional values for gauge groups up to $\mathrm{SU}(17)$. On an NVIDIA A100 the Kokkos CUDA backend is competitive with a native CUDA code, SIMD acceleration improves the OpenMP path on Armv9 processors, and a large scale speedup is demonstrated for an $\mathrm{SU}(4)$ lattice on the LineShine supercomputer, currently ranked first on the TOP500 list.

Multimode entangled states of light are key resources for high-dimensional photonic quantum technologies, particularly for continuous-variable quantum computing and quantum information processing. Exploiting their full capabilities requires detection strategies that can efficiently and simultaneously access a large number of modes and their quantum correlations. Multimode optical parametric amplification (MOPA) provides a promising approach by amplifying multiple quadratures prior to detection, rendering the measurement robust against subsequent optical losses while enabling broadband operation. Here, we demonstrate the spectral multimode capability of MOPA by simultaneously characterizing a broadband 60-node dual-rail continuous-variable cluster state. We reconstruct the cluster-state nullifiers across the spectral modes and observe nearly uniform squeezing ranging from -7.2 to -6.5 dB. Furthermore, by evaluating the van Loock-Furusawa criterion across all relevant bipartitions, we verify genuine multipartite entanglement throughout the 60-node state. These results establish MOPA as a promising platform for scalable high-dimensional quantum information processing in the spectral domain.

Quantum emitters in hexagonal boron nitride (hBN) are promising building blocks for solid-state quantum photonics because of their bright room-temperature emission. These emitters strongly interact with lattice vibrations, which partially underpins their excitation efficiency. Here, using complementary cathodoluminescence (CL) and photoluminescence (PL) spectroscopy, we demonstrate cavity-mediated interactions between hBN quantum emitters coupled to the same plasmonic Au void cavity at the room temperature. Cathodoluminescence measurements reveal that a vibronic transition of the 443 nm emitter hybridizes with the cavity mode, giving rise to a spectrally resolved splitting, while the neighboring 537 nm emitter remains in the weak-coupling regime despite spectral overlap. The energy split of the CL peak is at the order of 36.2 meV, whereas the PL-resolved peak is approximately 24.1 meV, which is due to the collective excitations of multiple emitters per electron excitation and their mutual interactions. Maxwell-Bloch simulations reproduce the experimental spectra and identify the observed splitting as the result of cavity-induced hybridization. Excitation-dependent PL further shows that the hybridized state can be accessed through phonon-assisted excitations below the zero-phonon transition. These results demonstrate that vibronic transitions provide an effective pathway for controlling interactions between multiple quantum emitters in hybrid plasmonic nanophotonic systems.

We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transient simulations from EC-Earth3 (1950-2166, SSP2-4.5) to predict the day ahead temperature field from the previous days temperature as well as annual mean temperature. Predictions are made auto-regressively with rollout periods of between a month and an extended season. Using only this variable of interest, we are able to reproduce the daily variability, spatial patterns, annual cycle and long-term trend from EC-Earth3 at a substantially lower computational cost than the physical model. We demonstrate that EC-EarthFlow is stable for long inference periods, and that it can learn the physical relationships as simulated in EC-Earth3.

The organization of interfacial water shapes the electric double layer behavior in aqueous electrolytes. Asymmetries upon charge reversal are commonly attributed to specific ion adsorption or surface chemistry. Yet it is unresolved whether interfacial water is net oriented at zero charge, and whether such orientation alone makes its response charge-asymmetric. Here, using electrochemical heterodyne-detected sum-frequency generation spectroscopy and constant-potential molecular dynamics simulations, we resolve the absolute orientation of water at a weakly interacting graphene electrode. We show that interfacial water retains a net H-down orientation, with hydrogens pointing toward the bulk, at zero net surface charge. Superimposed on this offset, the field-induced reorientation is linear and symmetric upon charge reversal, independent of how the charge is introduced or distributed. The zero-charge offset therefore shifts the potential of zero water orientation negatively relative to the potential of zero charge, making the interfacial water response intrinsically charge-asymmetric. These results establish an electrostatic baseline against which extrinsic contributions at electrified interfaces can be isolated.

Variational two-particle reduced-density-matrix methods impose hierarchies of semidefinite constraints. We ask whether a fixed level of such a hierarchy can recover even an elementary many-fermion spectrum, using the dispersion test, in which a value is accepted as an eigenvalue when the minimized dispersion vanishes. For an exactly solvable operator that counts fermions in one half of the spin orbitals, whose spectrum consists of integers, we determine the complete set of accepted values at every level of the full hierarchy of particle-hole semidefinite constraints. Besides the integers, this set contains an entire interval of noninteger values unless the hierarchy level grows linearly with the particle number, and we give the exact threshold. The proof constructs explicit pseudo reduced density matrices that are diagonal in the occupation basis, obtained by continuing the occupation probabilities of a fixed-particle-number state to a noninteger particle number. Classical semidefinite constraints on occupation correlations, supplemented by nonnegative occupation-pattern probabilities, suffice to establish fermionic positivity. For this counting model the two relaxations have the same dispersion zero set, not necessarily the same feasible states, and a converse argument shows that no other false eigenvalues occur. The limitation is specific to the hierarchy: two-body inequalities that encode the integer spectrum would remove every false value but are not generated at a fixed level. We also verify that the full semidefinite constraints and their complete projection onto two-particle reduced-density-matrix space are equivalent in finite dimensions, so that the result applies equally to the polar formulation.

Simulations of water at solid interfaces routinely disagree on basic properties, from contact angle to friction, depending on the model used, whether an empirical force field or a density functional theory (DFT) exchange-correlation (XC) approximation. Yet no accurate computational reference has been available to determine which, if any, is reliable. Here we assess XC approximations spanning the rungs of Jacob's Ladder against the random-phase approximation (RPA) at the graphene-water interface. Using machine-learned potentials (MLPs) trained on both DFT and RPA data, we achieve the extensive sampling required to characterize interfacial structure, wettability, friction, and vibrational sum-frequency generation spectra. We find that XC approximations disagree substantially with RPA and with each other. To make sense of this, we introduce a transferable, multi-observable scoring framework that distills each functional's agreement with RPA into a single measure. Six functionals emerge as reliable starting points for studies of graphene-water and other dispersion-dominated interfaces, namely B3LYP-D3(0), revPBE-D3(0), revPBE-D3(BJ), B97M-rV, r$^{2}$SCAN, and revPBE0-D3(0). Notably, this ranking follows no simple rule based on functional family or rung of Jacob's Ladder. What does hold across functionals is that dynamical and spectroscopic properties tend to be harder for DFT to reproduce than structural ones, with direct consequences for functional selection. Our framework also offers a general strategy for benchmarking any atomistic model, including the new generation of foundational MLPs, against high-accuracy references at solid–liquid interfaces. Beyond simulation, this work identifies which models can be trusted to connect measured contact angles, friction, and vibrational spectra to the molecular structure behind them.

Real-time equilibrium reconstruction is essential for tokamak plasma control. Data-driven surrogates are usually trained on high-quality labeled experimental data, but producing a large amount of labeled data sufficient for surrogate learning requires many expensive discharge shots, and few exist at the start of a new campaign or the operation of a new device. To reduce this cost, we propose to pre-train on simulated equilibria, generated at low cost by physics-based simulation, then fine-tune on limited real data. We evaluate under three settings with increasing difficulties, i.e., in-distribution, out-of-distribution (shape), and cross-campaign (temporal) splits, which mirror how reconstruction is actually deployed. Experimental results validate that synthetic pre-training is highly effective when few real labels are available: fine-tuning on only 1% of the full available real dataset cuts the per-sample normalized root-mean-square error (nRMSE) of the reconstructed poloidal flux by roughly 58% under out-of-distribution shapes and 64% under cross-campaign extrapolation, compared to a model trained from scratch on the same amount of real data. These realistic evaluations show that synthetic pre-training improves equilibrium reconstruction accuracy in the data-scarce regime that real tokamak campaigns face.

The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal ones, are useful for enabling inferences, forecasts and decisions from sparse oceanic observations. This article focuses on the statistical treatment of two types of in situ observations in the Global Ocean Observing System: from profiling floats and surface drifters, focusing on the Argo and Global Drifter Programs. We describe the spatio-temporal models that have been developed in recent years for these data, to give a picture of the statistical challenges faced in modern physical oceanography. We will discuss in detail two types of reference frames when modeling such data: Eulerian and Lagrangian, the former of which is more appropriate for profiling floats and the latter for surface drifters.

Random selection is widely used in resource allocation and auditing, making reliable implementation essential for AI-agent systems. Behavioural tests across six reasoning models uncovered threshold and divisibility rules used in identifier-based choices. For threshold-following GPT-6 Sol and Gemini 3.8 Flash, single-agent measurements prospectively predicted correlated participation under shared identifiers and biased participation under distinct identifiers with common timestamp bits. Changing dates, formats and identifier labels revealed when these predictions held. Explicit instructions to randomize independently reduced but did not eliminate shared-input correlation. To test implications for oversight, we asked four models to select customer requests randomly for human review. GPT-6 Sol approached the target rate while selecting predictably from identifiers; the others rarely selected requests. All four closely followed supplied random draws. These findings expose collective and audit vulnerabilities that selection rates alone miss, making input-dependent bias, correlation and predictability central targets for agent evaluation.

In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured. It typically appears as coherent noise with linear or non-linear movement and varying amplitudes across different sail lines. SI is commonly observed and poses a challenge for seismic data processing. We present a case history of a previously proposed deep neural network (DNN)-based workflow applied for SI attenuation across a marine seismic block in the Camie Field of Angola. This field survey covers over 345 km2 and is marked by the challenge of multiple SI types. The employed DNN-based workflow performs SI attenuation in the common shot domain based on a supervised learning framework: a small subset of the SI-contaminated data was first processed by a conventional geophysical algorithm to obtain an estimate of the SI noise, which was then manually blended with the SI-free common shot gathers from the same survey to generate the training pairs. To ensure signal fidelity, several techniques were applied to improve the DNN's performance. A key highlight of this case history is its scale: this represents a real-world, large-scale processing project and we present a comprehensive comparison of the DNN-based workflow with the conventional geophysical algorithm across the entire survey block, focusing on both processing quality and processing time. The results demonstrate the outstanding performance of the employed DNN-based workflow, which achieved higher SI removal accuracy, with less signal leakage and more complete SI removal. The promising results of this application also open up possibilities for integrating deep learning into other seismic denoising tasks. In addition, we discuss the limitations of this case history, aiming to provide insights for future research and applications in the field.

Bayesian inference was combined with Unified Fit to resolve the effects of the mixing history on polymer nanocomposites whose ultra-small-angle X-ray scattering profiles are nearly indistinguishable by conventional analysis. Bayesian Unified Fit was applied to comparing the aggregate size, internal mass-fractal structure, and surface roughness for three carbon black–filled ethylene propylene diene copolymer rubber samples prepared by different mixing processes. Whereas conventional Unified Fit reported essentially identical carbon-black dispersions, the radius of gyration, mass fractal dimension, and surface fractal dimension for the three samples all followed the same ordering, which coincides with the reported ordering of the tensile strength and elongation at break and suggests that the inferred aggregate hierarchy is physically meaningful for the macroscopic mechanical response. These results identify the aggregate size as a candidate structural descriptor of rubber reinforcement and provide an uncertainty-aware basis for examining processing–structure–property relationships in polymer nanocomposites.

Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar energy conversion, photocatalysis and photo switching. Accurately describing these processes requires a representation that respects electronic symmetry and consistently relates energies to interstate couplings. Here we introduce PEACE, which combines a parity-equivariant latent Hamiltonian with a learned electronic connection. Controlled ablations reveal the complementary roles of symmetry-allowed state mixing and electronic-frame variation in reproducing crossing structures and relaxation dynamics. PEACE closely reproduces excited-state population dynamics from first-principles simulations, while its extension to spin-orbit coupling enables simulations of intersystem crossing. These results demonstrate that a more complete incorporation of the underlying physics into learned electronic representations leads to more accurate predictions of nonadiabatic dynamics.

From cells to cities, ecosystems to economies, complex networks exhibit peak intensities that define their empirical limit. Estimating these limits traditionally requires domain-specific knowledge. We demonstrate that a wide range of complex networks share a capacity frontier that constrains these maxima. We formalize this regularity by proposing Structural Capacity Theory for Network Substrate Structured Systems. These systems couple a fixed network substrate with an observable dynamical process. The frontier is learned from a vector representation of the network substrate and modulated log-additively by a vector representation of the dynamical process. Frontiers trained on one set of domains reliably bound empirical maxima of entirely different ones without observing their peak intensities or simulating their dynamics. Independent learning paradigms converge on consistent upper bounds. Frontier performance is stable under perturbation, yet degrades under deliberate falsification, with no performance gain under ablation. These findings support the capacity frontier as a generalizable constraint that describes how network structure bounds dynamical processes across otherwise unrelated systems.

A complete characterization of earthquake induced ground motions require three translational and three rotational components, together with the six components of the strain tensor. Despite the importance of rotational ground motions, their characteristics are not yet completely recognized. Limited observational database and challenges of simulating them in realistic geological media has primarily constrained the research in this domain. In the present study, we develop an extension of the spectral-element package SPECFEM3D that directly evaluates rotational ground motion from spatial gradients of displacements at the Gauss-Lobatto-Legendre integration points during the numerical solution. The implementation is validated against analytical solutions for a homogeneous elastic medium, showing good agreement in arrival times, amplitudes, waveform characteristics, and spatial propagation. For analyzing the propagation patterns of rotational ground motions, we introduce Peak Ground Rotation Rate (PGRR) maps, analogous to Peak Ground Velocity (PGV) maps. Comparison of PGRR and PGV maps revealed that the critical locations of translations and rotations do not coincide. Rotational ground motions are subsequently simulated in a layered medium for two earthquake source mechanisms, demonstrating the variation in PGRR maps for different source characteristics. Finally, a three-dimensional (3D) model of the Indo-Gangetic Basin is developed to show that low-velocity sedimentary deposits substantially amplify and redistribute rotational ground motions. The spatial distribution of PGRRs in the basin changes significantly relative to the corresponding generic layered medium. The developed framework provides a basis for investigating rotational ground motions in complex geological environments and assessing its potential relevance to sensor deployment strategies, seismic hazard and earthquake engineering.

This study considers rapidly modifying the wake shedding frequency of the flow around an airfoil using sparse sensor information, subject to constraints on the lift coefficient fluctuations. This is achieved by combining phase-amplitude reduction with nonlinear machine-learning-based sparse sensor reconstruction. We derive time-varying phase and amplitude sensitivity fields that identify the optimal spatial locations and timing for actuation from merely three sensors. Through the sensitivity fields, we analytically obtain the optimal waveform for fast synchronization of wake shedding frequency while minimizing amplitude deviation of aerodynamic responses. The proposed approach is evaluated using flows over various NACA airfoils at several post-stall angles of attack, all of which exhibit unsteady periodic vortex shedding. With the identified optimal forcing, the wake frequency is altered much faster than with a standard sinusoidal actuation. Furthermore, the amplitude-penalized forcing achieves $20\%$ suppression of the lift coefficient fluctuation compared to the optimal forcing without amplitude penalty. The current amplitude-penalized technique may offer an efficient path for fast flow modification without causing detrimental fluctuations in periodic aerodynamic and aeroelastic systems with fluid-structure interactions.

We derive the angular resolution limit of an augmented-reality (AR) waveguide combiner from guided mode theory. A slab of finite thickness supports only a discrete set of total-internal-reflection (TIR) angles, giving an angular resolution that worsens with thinner waveguides and smaller TIR angles. At 500~$\mu$m thickness the resolution is worse than the 1 arcmin (60 ppd) acuity target over most of the usable TIR range, and reaching 1 arcmin for TIR < 42$^\circ$ requires a substrate thicker than 1 mm. The limit is washed out by a source linewidth above $\sim$0.1~nm or a thickness variation above $\sim$100~nm across the pupil-replication period, but only partially by pupil truncation and not at all by out-coupling loss. LED- and micro-LED-based displays are therefore unaffected, owing to their broad spectra, whereas a narrow-linewidth laser-based display in a thin, uniform waveguide is subject to this limit.

The 2026 Nobel Prize in Physics was awarded to Francis Halzen @for decisive contributions to the IceCube Neutrino Observatory and the discovery of high-energy neutrinos of astrophysical origin.@ This article examines the Soviet contribution to the broader historical development of high-energy neutrino astrophysics. Four interconnected strands are distinguished: the physics of Cherenkov radiation and its application to cosmic rays; early proposals for large-scale neutrino detection underground and in natural water; the theory of atmospheric, cosmogenic, and astrophysical neutrinos; and the development of underground, underwater, radio, and in-ice detection techniques. The article shows that M. A. Markov s 1960 proposal was concerned primarily with atmospheric neutrinos and with detecting charged secondary particles through Cherenkov radiation in water, whereas the use of Antarctic ice was initially developed in the Soviet Union in the radio domain. The optical concept of a detector in deep polar ice was formulated by F. Halzen and J. G. Learned in 1988. The Pamir cosmic-ray school, the theoretical work of G. T. Zatsepin, V. A. Kuzmin, and V. S. Berezinsky, the Baksan Neutrino Observatory, Soviet participation in DUMAND, and the formation of the Lake Baikal neutrino program are considered separately. The Pamir material is included as historical and methodological context; it does not imply a direct experimental lineage from Pamir installations to IceCube. The historical links of these strands to IceCube are therefore different in character: conceptual and citation-based in some cases, methodological or institutional in others. This distinction helps separate documented continuity of ideas from the broader scientific context in which modern high-energy neutrino astronomy emerged.

Interactive quantum circuit simulators are increasingly used to support introductory quantum computing education for learners from varied disciplinary backgrounds. These environments can also generate detailed response logs of submitted circuits, but such data are rarely transformed into evidence that instructors can use for course redesign and development. We present a learning analytics framework for quantum circuit simulator tasks and report its task-level branch on anonymised logs from an introductory quantum computing course delivered in the TIM learning environment. The task branch characterises exercises through participation, completion, first-attempt accuracy, valid-attempt counts, sessionised active time, and observed answer-space reporting of common correct and incorrect constructions. The dataset covers the 2024-2025 cohort of 492 learners, 47,533 valid submissions, and 21 obligatory quantum circuit exercises. The framework identifies bottleneck tasks, broad solution spaces, and recurring wrong-answer patterns that are not visible from final scores alone. Its most distinctive output is a plain-text representation of the observed answer space, designed to make open-ended circuit submissions directly interpretable to instructors. We show how response logs can reveal actionable task-design issues and support concrete course redesign. A learner-level branch is retained only as an ongoing research extension and is not a central empirical contribution of the present paper.

Symmetry constrains and determines the physical properties of crystalline solids. Determining crystalline symmetry often requires large-scale diffraction facility and advanced analysis software and therefore cannot be realized with real-time feedback. Alternatively, optical anisotropy has also been exploited for elucidating the rotation, mirror and inversion symmetries of solids on smaller tabletop setups. Second harmonic generation, in particular, has demonstrated a remarkable sensitivity to subtle changes in these symmetries during phase transitions. To access the full nonlinear susceptibility of a crystal in a cryogenic or vacuum environment, angle-by-angle rotation of the scattering plane of an obliquely incident and outgoing beam in co-ordination with their polarization states is required. This typically results in a bulky and wobbly setup, significant feedback latency, and a focal spot size > 20 {\mu}m unsuitable for microscopic samples such as few-layer 2D materials. In this work, we make use of vector beams to realize a non-rotating oblique-incidence setup, demonstrating a real-time (> 10 fps) visualization of crystalline symmetry on a sample area of ~ 2 {\mu}m in diameter. Both the feedback latency and focal spot size represent more than an order of magnitude improvement over previous designs based on a Gaussian beam and mechanically-rotating stages. Here, the use of a vector beam greatly simplifies and reduces the cost of nonlinear optical anisotropy measurements, heralding widespread applications of this approach in both research and industrial settings, for example, for strain monitoring and crystal alignment. Our work may also promote broader integration of structured optical field into solid state and condensed matter research in the future.

We investigate the electromagnetic field produced by a harmonically oscillating point charge and measured by a spatially separated observer undergoing the same motion. We evaluate the exact retarded field on the observer's worldline and project the electromagnetic field tensor onto the observer's local orthonormal tetrad. This construction yields the electric and magnetic fields, electromagnetic energy density, and Poynting vector measured as functions of the observer's proper time. Our formulation separates unambiguous local observables from the observer-dependent classification of a field as radiation and provides a concrete setting in which to examine how a charge that is spatially static in a non-inertial coordinate system can nevertheless produce time-dependent locally measured fields.

We report, for the first time, a fully integrated Ho$^{3+}$-doped fluoride glass waveguide laser incorporating a femtosecond laser-inscribed waveguide and waveguide Bragg grating. The compact 12.4 mm cavity achieves stable single-frequency lasing at 2062.21 nm with a line width of better than 5 MHz. The laser emits linearly polarized light, which is efficiently delivered through an SM1950 single-mode fiber. This alignment-free architecture provides a compact and robust platform for integrated mid-infrared single-frequency laser sources.

Resolving the anomalous conductivity exponent of the symmetric $\beta$-Fermi–Pasta–Ulam–Tsingou chain by molecular dynamics can require very large systems because of long finite-size crossovers and thermal-contact resistance. Motivated by this computational challenge, we derive a long-wavelength continuum description and investigate whether the kinetic-theory scaling $\kappa\propto L^{2/5}$ becomes accessible with a moderate number of numerical degrees of freedom. The nonlinear elastic field retains the cubic stress of the microscopic interaction and exchanges heat with Langevin reservoirs. A flux-conservative spatial discretization constructs the force and energy current from the same stress, providing a consistent interior transport estimator. For $L>8$, corresponding to approximately $10^3$ mesh nodes and above at the reference resolution, the fitted exponent is $0.399\pm0.004$. Mesh refinement supports the stability of this exponent between the two finer resolutions, although the conductivity amplitude remains resolution-dependent. The main result is thus an accessible finite-size regime near $2/5$, rather than convergence of the absolute conductivity as the mesh spacing vanishes. This formulation provides a practical route to studying anomalous transport in anharmonic systems and a conservative continuum framework for investigating energy flow in nonlinear elastic media.

Quantum electrodynamics (QED) currently features prominently at the precision frontier in describing the structure of atoms and molecules. Dominant QED effects are governed by the electronic structure in the vicinity of the nucleus and are therefore generally regarded as atomic in origin, with molecular contributions largely arising from additive atomic contributions. By studying the nuclear magnetic resonance shielding tensors of thallium in Tl$X$, we reveal genuinely molecular QED effects that strongly depend on the bonding partner ($X$ = F, Cl, Br, I, At) and are absent in the Tl$^+$ ion.