Papers

Quaternion algebra provides a natural representation of time-reversal-symmetric matrix structures in relativistic electronic-structure theory, whereas complementary time-reversal-antisymmetric structures extend this representation to complex quaternions, or biquaternions. Here, we explicitly exploit biquaternion algebra for fundamental objects, including operators, kinetically and magnetically balanced basis functions, and their expectation values, within a unified framework encompassing real, complex, and real quaternion subalgebras as special cases. To realize this framework computationally, we have developed HMATLIB, a biquaternion matrix library implemented within the ReSpect package for pure CPU and hybrid CPU/GPU execution. A key development is a bilinear algorithm for matrix-valued biquaternion multiplication that reduces the number of real matrix–matrix multiplications from 64 to 24 compared with the conventional component-wise approach, while the biquaternion representation effectively doubles the maximum accessible matrix dimension under the same 32-bit indexing constraint. Numerical benchmarks on modern CPU and GPU architectures demonstrate that the biquaternion formulation consistently outperforms its isomorphic complex-algebra counterpart. For the largest matrices reported, hybrid CPU/GPU execution accelerates bilinear matrix multiplication by approximately 5-8 times over pure CPU execution and matrix diagonalization by approximately 12 and 61 times relative to CPU oneAPI MKL and NVHPC OpenBLAS, respectively. These results establish biquaternion algebra as a general and computationally advantageous framework for modern relativistic electronic-structure calculations.

Quantum efficiency (QE) is a key property of photocathodes, and its uniformity is essential for producing high-brightness electron beams. Cathode imaging provides an in-situ and real-time approach for QE mapping, but in RF guns, a high charge per bunch is often needed to obtain a sufficient signal-to-noise ratio. Under such conditions, space charge effects can significantly degrade the imaging resolution and may even make point-to-point cathode imaging ineffective. In this paper, we propose a novel cathode imaging method based on a magnetized electron beam. Its feasibility is examined through theoretical analysis and beam dynamics simulations. The results show that the proposed method enables point-to-point cathode imaging in the ten picocoulomb charge regime. For a 10 pC, 3ps beam with a cathode magnetic field of 1200 Gauss, simulations indicate an imaging resolution of 11 um, representing nearly an order-of-magnitude improvement over the non-magnetized beam method.

Surface enhanced Raman spectroscopy (SERS) in the ultraviolet (UV) region offers important advantages for biomolecular detection, including resonance enhancement and reduced fluorescence interference. However, the development of UV SERS substrates that combine low cost, reproducibility, and chemical stability remains challenging. Here, we employ a dry synthesis approach to fabricate nanoporous Cu and copper oxide (CuO) films on silicon substrates and systematically evaluate their UV SERS performance using adenine as a Raman reporter under 325 nm excitation. Among the substrates investigated, nanoporous Cu exhibits the strongest enhancement, enabling adenine detection down to 10 microM. In contrast, no detectable adenine Raman signal is observed under 532 nm excitation, indicating that the enhancement is dominated by a UV induced chemical, charge transfer mechanism rather than conventional electromagnetic enhancement. The Cu substrates further enable the UV Raman spectroscopy of streptavidin, as a test protein, and, more interestingly, the discrimination between L and D tryptophane based solely on differences in UV SERS intensity, without chiral selectors or additional surface functionalization. By varying the substrate rotation speed during metal evaporation, the enantioselective response can be tuned, yielding L over D intensity ratios from 1.10 to 2.35 and demonstrating the critical role of substrate morphology in chiral discrimination. The dry synthesized nanoporous films provide a simpler, scalable, and ligand free fabrication strategy while offering additional capability for enantioselective detection. These findings establish dry processed nanoporous Cu films as promising platforms for UV-SERS biosensing and label free chiral analysis.

Microwave angle-of-arrival(AoA) measurement is essential for radar, communication, and spectrum mon-itoring. Existing Rydberg-atom-based AoA schemes employ phase-difference measurements with local oscil-lators, standing-wave fluorescence imaging, or amplitude-ratio readout with internal metal reflectors. Here we demonstrate a new approach: a cylindrical glass vapor cell serving directly as an angle-encoding dielectric structure, eliminating the need for multiple apertures, local oscillators, or imaging optics. The cylindrical geometry produces angle-dependent reflection and field redistribution, mapping the incident AoA onto the effective microwave field sampled by the Rydberg ensemble. This effective field is read out optically via the Autler-Townes(A-T) splitting in the electromagnetically induced transparency(EIT) spectrum. Full-wave simulations and experiments at 11.64 GHz confirm a deterministic, geometry-mediated response over 0{\deg}-90{\deg}.Over the monotonic operating range(55{\deg}-125{\deg}), the angular resolution(minimum distinguishable increment)is 0.05{\deg}, and the angular accuracy(RMSE of repeated measurements) is 0.13{\deg} across the full range, improving to 0.05{\deg} in the optimal region(65{\deg}-115{\deg}). Occupying a sensing volume of~2.5 cm3, this method offers a compact, single-sensor pathway toward Rydberg AoA receivers.

This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles of hazard-relevant variables. The system is trained on the regridded CONUS404 data with ERA5 forcings, and is evaluated on 20 TCs in 2020–2024. Verification shows stable downscaling performance across Atlantic hurricane seasons, with energy spectra closely matching the CONUS404 reference. The system is also verified to produce skillful TC-relevant weather extremes, largely improved over a deterministic AI baseline. The system performs well with forcing data from other models (GDAS/FNL) and can produce detailed eyewall, rainband, and landfall structures in TC case studies. The study provides a good example of how AI-based dynamical downscaling systems can be designed to resolve small-scale extreme weather events.

As ambitious space telescope concepts such as ultra-lightweight planar diffractive optical elements (DOEs) emerge, validating the performance remains a major computational challenge. Conventional Fourier propagation algorithms were observed to fail at meter-class apertures due to severe memory limits caused by rigid grid-sampling requirements, and the scaled-down proxy models used for reflector telescopes cannot be applied, since scaling compresses the outermost zones that govern resolution. We benchmarked five Fourier-based propagators against a common Fresnel diffraction integral and found that only those decoupling the focal-plane grid from the input aperture converge within a 1% error threshold. With these findings, we implemented an optimized, stripe-processed Chirp Z-Transform (CZT) framework, evaluating the focal spot strictly within a fixed region of interest to reduce peak memory usage. Applied to five full-aperture configurations from 1.0 m to 5.0 m at f/# = 5, the framework predicted spatial resolution and diffraction efficiency to within 0.001% and 0.16% of analytical references, with modulation transfer function results cross-checked by two analytical extraction methods, all within 6.4 GB of memory on a single consumer-grade GPU. This simulation study represents first steps toward quantifying the expected results of ambitious space telescope concepts and aids the mission development (or selection) phase. With a highly accurate, memory-efficient validation tool, the findings obtained will be used to guide the fabrication decisions of future hardware, optical testing, and physical deployment mechanisms of large-scale diffractive telescopes.

We introduce a mesoscopic physics-based framework that transforms conventional transmission optical micrographs into quantitative maps of tissue structural heterogeneity using the Inverse Participation Ratio (IPR). For the first time, to our knowledge, IPR-based light-localization analysis is applied to cancer tissue imaging to quantify nano- to submicron-scale structural alterations through spatial fluctuations in tissue mass density or refractive index. Unlike conventional morphology-based assessment, this physics-driven approach provides objective structural biomarkers from label-free or routinely stained tissue images. The method establishes a scalable, reproducible platform for quantitative computational pathology and enhanced cancer diagnosis by integrating mesoscopic optical physics with standard optical microscopy.

The Big Bang singularity marks a fundamental breakdown of General Relativity, yet all proposed quantum gravitational remedies invoke microphysical assumptions beyond the observational reach. We show that a minimal, purely classical modification of the gravitational potential — preserving the inverse-square law where tested, but free to depart at unmeasured scales — is sufficient to produce a non-singular, cyclic universe. The resulting modified gravitational formulation leads to a cosmic evolution exhibiting a bounce at a finite minimum size, followed by a regime closely akin to the concordant $\Lambda$CDM model, and an eventual collapse to another bounce. Cosmic Chronometer, Supernovae type Ia, Baryon Acoustic Oscillation, Cosmic Microwave Background and Nucleosynthesis data constrain both the new length scales, implying a cycle period of $\simeq 89\,$Gyr, and this framework — subsuming a broad class of bounce scenarios within a single testable model — opens a direct route for distinguishing cyclic cosmologies from $\Lambda$CDM.

Quantum techniques matter to optical astronomy when they preserve information in the parameter-bearing measurement channel after realistic loss, calibration, covariance, and inference are propagated to the final astrophysical observable. We compare 7 approaches by the quantity they change: internal optical-path delay, sub-Rayleigh source separation, coronagraphic planet throughput & stellar leakage, coherent field quadratures, nonlocal complex visibility, null depth, active phase sensing. In an Earth-analog astrometry case, 6-dB squeezed internal metrology improves the measured phase quadrature by 1.58 and the complete readout by ~1.20, but fixed-time planet-mass precision by only 1.010 (95 % interval 1.005-1.045); mission duration, useful baseline, cadence, calibration have greater modeled leverage. For a close young binary, signed spatial-mode demultiplexing preserves sub-Rayleigh orbital information that is substantially degraded in the tested direct-imaging comparator. A representative matched-model calculation gives a dynamical-mass RMSE gain 1.77 1.47-2.15). For illustrative equal-prior pre-main-sequence predictions at 0.10 and \(0.12M_\odot\), mass precisions of 15%, 10%, 7% correspond to correct-selection probabilities of ~73%, 82%, 90%. The numerical gain remains provisional because the event-level continuous-image comparator, strict tail convergence, externally measured broadband transfer matrix, and finite-photon calibration are not yet closed. Modal coronagraphy and quantum-enhanced heterodyne reception define conditional receiver opportunities, while the nonlocal, nuller, and prepared-NOON cases identify physical bottlenecks that prevent an astrophysical advantage in the stated configurations. The common result is that quantum technology is most valuable when it changes an information-bearing observable and that channel remains consequential in the final science measurement.

The quantum harmonic oscillator (QHO) is normally built on a structure that contains a Hilbert space, ladder operators, and a Born rule. Here we derive it from counting. We adopt three information-theoretic postulates: information is carried by binary sequences of length $n$; only symbol counts, not the sequences themselves, are observable; and the transition measure must account for exactly the $2^n$-configuration capacity of the register. The first two postulates force the counting problem into the Hamming/Delsarte association scheme and endow it with an exact symmetry under relabeling of the two symbols. Given an exchange-symmetric bilinear transition measure we prove a rigidity theorem: relabeling symmetry and normalization together force the alternating sign $(-1)^t$ which drives interference. The variable that carries the sign, i.e. the overlap between input and output sequences, or equivalently their Hamming distance $d_{ab}$, is a hidden quantum number of the transition as a whole, defined only by the two endpoints jointly and unknowable from either alone. Interference thus enters as alternating-sign bookkeeping over a nonlocal, relational variable $d_{ab}$, rather than as a separate dynamical ingredient. This forced weighting makes the transition probability proportional to the square of a Krawtchouk polynomial, whose difference equation has an exactly equally spaced spectrum and which converges to the QHO as $n\to\infty$. At fixed excitation, deviations from the QHO are $\mathcal{O}(1/n)$, and are testable in laboratory realizations of the oscillator, since any real system has finite information capacity. In this framework, interference, quadratic (Born-rule-like) probabilities, and the particle-hole symmetric oscillator spectrum are consequences of counting; only the bilinear form of the measure is assumed (though well motivated) rather than derived.

Dark Energy Spectroscopic Instrument (DESI) Collaboration reported an evidence for dynamical dark energy model using baryon acoustic oscillation (BAO) measurements from DESI Data Release 2 (DR2), cosmic microwave background (CMB), and three Type Ia Supernovae (SNe) data (Pantheon+, Union3, and DES-SN5YR) recently. The significances are from $2.8\sim4.2\sigma$ depending on which SNe sample is used, and the highest significance is obtained based on the DES-SN5YR data. But more recently, Dark Energy Survey (DES) Collaboration recalibrated the DES-SN5YR data, and a significance of only $3.2\sigma$ is obtained based on the calibrated data, which is called DES-Dovekie. In this work, we perform a comprehensive analysis of five representative dynamical dark energy parametrization models, including Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), exponential (EXP), logarithmic (LOG), and Jassal-Bagla-Padmanabhan (JBP), using DESI DR2 BAO, Planck CMB, ACT DR6, and SNe data from Pantheon+, Union3, and DES-Dovekie. Similarly, a significance of around $3\sigma$ is obtained based on the DES-Dovekie data. And the significances are $2.1\sim3.7\sigma$ for different SNe samples and different parametrization models, indicating an evolutionary dark energy dynamics. Furthermore, a phantom-like behavior in the past and has transitioned into quintessence-like behavior today for the dynamical dark energy is preferred.

Black holes hold considerable fascination for the general public and students alike, and are commonly included in general-science courses on astronomy or modern physics. But teaching about the basics of black holes poses a considerable challenge: Any rigorous description requires concepts and techniques from general relativity, Einstein's theory of geometry and gravitation. And any half-way rigorous introduction to that theory, including the required mathematical tools, is significantly beyond the level of general-science courses. Inevitably, accounts of relativistic physics at the introductory undergraduate level make use of analogies, approximations and simplified models to teach about topics like black holes, gravitational waves, gravitational lensing, or cosmology. The purpose of this article is to given an account of one particular set of analogies for teaching about black holes, all of which are based on modelling the motions of observers in the vicinity of the black hole and rely on the concept of (relative) speed to describe properties of the black hole. While most elements of what I am about to describe can be found in the existing literature, I am not aware of any text that attempts to pull them together into a unified picture at a suitable level of presentation for undergraduate-level teaching; that is the goal of the present text.

This paper discusses the metaphor, originating with Whittaker, according to which Maxwell's model of molecular vortices was the scaffolding by means of which the equations of electromagnetism were erected, removed afterwards without consequence for the building. It is argued here that the metaphor describes badly what happened, and it is shown that whoever upholds it today transcribes, within that same text, the quotations that belie it. The paper examines what was actually removed in 1865, which materials of the model still operate inside the equations under other names, and proposes in its place the figure of the template, taken from sheet-metal layout work. The discussion is situated with respect to the work of Daniel M. Siegel and Cameron Lazaroff-Puck, who maintain from the history of science the constitutive character of the model. It closes with a criterion for telling one case from the other.

High-order CFD simulations provide detailed resolution of the heterogeneous interstitial flow in pebble-bed reactors, but their computational cost is high, especially during the initial flow-development period required to reach statistically stationary conditions. This work investigates the use of a Super-Resolution Graph Neural Network (SR-GNN) to improve the initialization of high-order NekRS simulations. Lower-order P = 2 velocity fields are used as inputs to reconstruct higher-order representations, which are then used as initial conditions for P = 7 restart simulations. The approach is evaluated using a 146-pebble bed at Re = 1000, Re = 2500, and Re = 5000, with pressure-drop convergence used as the main figure of merit. The SR-GNN models were trained using paired low- and high-order snapshots and were first evaluated through qualitative inference comparisons. High-order restart simulations showed that, for Re = 1000 and Re = 2500, the SR-GNN initialized cases produced pressure-drop histories similar to direct restarts from true P = 2 fields. For Re = 5000, however, the super-resolved field restart approached the statistically stationary P = 7 pressure-drop range faster than both the direct P = 2 restart and the reference P = 7 simulation initialized from a uniform velocity field. The trained Re = 5000 model was also applied to a larger 1568-pebble bed, demonstrating qualitative applicability of the workflow to a significantly larger packed-bed geometry. These results indicate that SR-GNN-based initialization is a promising strategy for reducing high-order flow-development cost, while also motivating further work on broader Reynolds-number and geometry generalization.

In this study, we aim to theoretically investigate antipolar cell-cell adhesion, in which adhesion sites are located on the opposite side of the leading edge of migrating cells, as a candidate for irregularly polarized adhesion that induces disorder in collective cell migration. We employ the cellular Potts model to simulate the effects of antipolar adhesion on collective migration driven by cell motility. Antipolar adhesion induces a collective motility disorder, which exhibits a disordered configuration in the motility direction, even when collective motion occurs in the absence of adhesion. Consequently, antipolar adhesion inhibits collective migration. The effect is in contrast to that of polar adhesion, which accelerates the directional intercellular order of cell motility. At a specific motility strength, a depinning transition emerges from a collective motility disorder to a collective motion. The collective motility disorder can be physically explained by the cooperative effect between antipolar adhesion and motility persistence within the mean-field approximation.

Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the loss of geometric and orientational information during coarse-graining. In this work, we present a generalized framework for incorporating molecular geometry and directionality into coarse-grained machine-learned potentials through two complementary approaches: anisotropic density-based descriptors (AniSOAP) and symmetry-adapted equivariant message-passing neural networks (MACE-CG). Using Gay-Berne particles and coarse-grained representations of benzene, formamide, and water, we demonstrate that explicitly retaining molecular anisotropy substantially improves the prediction of energies, forces, and torques relative to isotropic representations. AniSOAP provides an effective linear baseline when molecular shape is well approximated by ellipsoidal symmetry, while symmetry-adapted MACE-CG enables the incorporation of arbitrary molecular point-group symmetries. For water, whose orientational degrees of freedom are poorly represented by ellipsoidal descriptors alone, symmetry-adapted rigid-body features improve energy, force, and torque prediction by resolving orientational degeneracies inherent to isotropic and moment-of-inertia-based representations. These results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.

The physical mechanisms controlling the onset of gravity-driven spreading of elastoviscoplastic drops remain unclear because yielding, elastic deformation, and viscous dissipation can occur simultaneously in different regions of the material. To isolate these mechanisms, we perform direct numerical simulations of large axisymmetric drops resting on a thin precursor layer of the same material. Each drop is first equilibrated under gravity, $g$, before being subjected to an additional constant downward acceleration of varying magnitude. The drop rheology is described by the Saramito–Herschel–Bulkley model. Once the rate of deformation becomes negligible under the enhanced forcing, the additional acceleration is removed and the drop is allowed to relax again under gravity alone, enabling the global deformation to be separated into recoverable and unrecoverable components. We find that spreading does not occur simply because some portion of the material yields locally. At low imposed accelerations, yielded regions remain confined within a predominantly solid-like viscoelastic matrix, and the resulting deformation is mostly recoverable. Appreciable spreading is observed when these yielded regions connect to form a continuous fluidised pathway from the drop interior to the free surface, allowing radial material transport. The acceleration required for this transition is governed primarily by the yield stress, whereas elasticity controls the partition between recoverable deformation and spreading by regulating the extent and spatial distribution of yielded regions.

A charged test particle is confined to the round sphere in a uniform ambient magnetic field. We reduce its motion to quadrature by elementary means and solve explicitly the family of trajectories which reaches the poles. On that family the azimuth advances at a constant rate and the latitude obeys a pendulum equation. The winding gained per oscillation is a complete elliptic integral of the first kind. That integral is a strictly increasing bijection onto the positive numbers. Every positive rational winding is therefore carried by exactly one value of the half-cyclotron frequency. Then we ask which geometry has these trajectories. No affine connection has them among its geodesics. A Finsler metric of Randers type has all of them. The threshold which separates the two dynamical regimes is precisely the condition for that metric to exist. The closed trajectories are its closed geodesics. The example places a non-Riemannian geometry within reach of an upper-level undergraduate or beginning graduate course in classical mechanics. We supply the derivations, the numerical recipes and the exercises.

Near-omnigenous stellarators are susceptible to energetic particle (EP) losses due to resonances between trapped EPs and non-omnigenous perturbations to the magnetic field. Existing bounce-averaged objectives such as ${\Gamma}_c$, ${\Gamma}_{\delta}$ , and ${\Gamma}_{\alpha}$ target the non-resonant misalignment of drift and flux surfaces, but they do not capture resonant convective or diffusive motion. We develop a discrete map theory for the bounce points of trapped EPs in near-omnigenous fields, in which phase-space islands form due to resonance between the precession and bounce frequencies. We introduce ${\Delta}_{res}$, a differentiable, bounce-averaged objective function that penalizes the widths of these islands, promoting phase-space integrability. Optimizing a quasi-axisymmetric configuration using ${\Delta}_{res}$ combined with a two-term quasi-symmetry objective yields a factor-of-four improvement in EP confinement by displacing low-order resonances and forming EP transport barriers. ${\Delta}_{res}$ is a powerful tool to combat both resonant convective and diffusive losses in power plant-relevant stellarators.

The Fokker-Planck partial differential equation (FP-PDE) governs uncertainty evolution in stochastic dynamical systems. In orbital dynamics, solving the FP-PDE is challenging because of nonlinear motion, high-dimensional states, and large space-time domains. We develop a physics-informed neural network (PINN) approach that approximates the FP-PDE solution as a single space-time probability density, while also quantifying its worst-case approximation error. This approach is, in principle, independent of the choice of state coordinates and neural network architecture. Specifically, to enforce probability density function (PDF) properties into the neural network, we design a Physics-informed Gaussian mixture model (PINN-GMM). Then a companion error PINN learns the dynamics of the approximation error and yields time-dependent bounds that define an ambiguity set of PDFs. This ambiguity set enables rigorous computation of upper and lower bounds on event probabilities through tractable linear programs. Numerical studies on illustrative 1D examples and several 4D–6D orbital test cases demonstrate accurate uncertainty propagation, correct and informative error bounds, and improved reliability over common uncertainty-propagation baseline methods (Gaussian approximation, unscented transform, and Gaussian mixture model). Constructing the PINN-GMM requires offline training, making it costlier than the baseline approximations; once trained, however, a single forward pass returns the density at any time in sub-millisecond time $(0.16~\mathrm{ms}$ in our implementation).

We describe a neural network architecture and training procedure designed to model electronic ground and excited states of arbitrary molecular systems. By indirectly learning a latent, implicit basis representation of the electronic-state Hamiltonian, the model offers a unified treatment of multiple electronic states, conical intersections, and non-adiabatic couplings. The formalism can be further extended to learn consistent latent representations of additional operators such as transition dipole moments, for example. To demonstrate the general capabilities of our architecture, we train and evaluate networks on two realistic photochemical systems, thymine and azobenzene. The resulting models accurately reproduce energies and oscillator strengths for the ground- and low-lying excited states relevant to the photochemistry of these systems. We highlight the performance of the trained networks by studying critical molecular geometries, including conical intersections and excited state minima. By construction, the proposed framework also recovers the emergence of Berry phase accumulation around conical intersections. By pairing key mathematical structure from quantum chemistry with the representation learning power of transformers, the presented architecture offers a qualitatively new path toward fast and accurate ground- and excited-state simulations.

A new icosahedral superatom UH$_{12}$ is reported and analyzed using post-Hartree-Fock multiconfigurational methods. A RASSCF/CASPT2/RASSI/SINGLE_ANISO workflow is employed to determine the electronic energy levels and magnetic properties. Superatomic characteristics, such as orbital topologies and shell filling order, are discussed. The high hydrogen-to-uranium ratio and potential for optically stimulated decomposition make UH$_{12}$ worth investigation in the context of hydrogen storage. The electric-dipole transition oscillator strengths of the molecule vanish, while its lowest electronic transitions fall in the THz and infrared ranges. The lowest septet manifold exhibits crystal-field splitting into a 1+3+3 pattern, as expected from icosahedral symmetry. In turn, the interlevel gaps within these crystal-field triplets are 30-150 GHz (1-5 cm$^{-1}$). The molecule is magnetically isotropic at low fields, while its levels exhibit higher-order Zeeman anisotropy that becomes significant around 2 Tesla. These properties suggest potential relevance for quantum computing, quantum communication, and quantum memory, including qubit- and qudit-based architectures.

We develop a structure-preserving, data-driven collision operator for spatially homogeneous multi-species kinetic systems from molecular dynamics (MD). The operator consists of diagonal self-collision blocks and ordered off-diagonal cross-species blocks to describe intra- and inter-species momentum and energy exchange. Within a local and point-wise identifiable kernel class, we develop the necessary and sufficient condition for the admissible kernel class satisfying the conservation laws, the H-theorem, and the frame indifference. Unlike the classical Landau operator, the off-diagonal kernels are not restricted to be symmetric under permutation of the two velocity variables. This unique structural freedom captures the distinct responses of different species to unresolved correlations and many-body effects arising from micro-scale particle interactions. The equivalent parameterizable kernel formalization enables us to learn a generalized data-driven collision operator directly from MD, where the low-rank tensor representations and random sampling are used to achieve efficient kernel training and numerical simulation. Numerical experiments show that the learned operator accurately predicts transport coefficients and the non-equilibrium relaxation, while retaining discrete conservation and entropy production. In particular, it captures plasma kinetics in the moderately coupled regime, where the predictions of both the Landau and the data-driven model restricted to velocity-permutation symmetry show significant discrepancies.

Digital silicon photomultipliers (dSiPM) are arrays of single photon avalanche diodes (SPADs) where each SPAD has its own electronic readout. To maximize the photodetection area, we developed a readout integrated circuit (ROIC) 3D-integrated to a custom-designed SPAD layer fabricated at Teledyne Dalsa (Bromont, Canada) to form a photon-to-digital converter (PDC). This paper presents an improved version of a previously demonstrated ROIC, fabricated in TSMC 180 nm technology to manage a 64 x 64 pixel architecture. This ROIC has different outputs, such as a flag output that produces a pulse whenever one of the pixels triggers and a digital sum that samples the amount of triggered SPADs. Improvements lead to a reduction of the timing jitter on the flag output from 72.0 ps RMS to 22.6 ps RMS through optimized H-tree design. A tunable hold-off circuit provides adjustable dead time from 32 ns to 18 {\mu}s, addressing both afterpulsing mitigation and SPAD-to-SPAD variations due to process defects. Power consumption is lowered by 30% at trigger rates exceeding 10 kHz through digital logic optimization and clock gating. These measurements validated that the ROIC is ready for 3D integration into a PDC for systems in medical imaging, particle physics, and quantum sciences.

Filament-generated air waveguides offer a route to low loss long-distance optical transport, but their viability in atmospheric turbulence has remained uncertain. Here, we demonstrate with experiments and simulations that guided beams in air waveguides can overcome prior estimated turbulence limits by orders of magnitude in the refractive index structure parameter C_n^2; turbulent index fluctuations much larger than the waveguide index contrast average out along the propagation path. Instead, turbulence imposes two distinct limits: scattering of the guided field beyond the waveguide acceptance aperture and, more importantly at atmospheric strengths, scintillation of the waveguide-forming beam. Scaling of experimentally validated models indicates that filament-generated air waveguides remain viable over kilometer-scale paths under strong near-ground turbulence. Our results establish turbulence limits for long-range optical guiding in the atmosphere.

Silicon Photomultipliers (SiPMs) based on the near-ultraviolet, metal-filled trench (NUV-MT) technology offer improved photon detection efficiency and reduced correlated noise relative to earlier designs, making them attractive for a broad range of particle- and astroparticle-physics applications. As such devices may be deployed in high-radiation environments, quantifying their performance after irradiation is essential. Commercially available Broadcom AFBR-S4N series NUV-MT SiPMs were irradiated with 1 MeV neutrons to fluences between 2x10^{9} and 1.1x10^{10} n_{eq}/cm^2 at the TANDEM accelerator facility of NCSR "Demokritos" in Athens, and characterised before and after irradiation and over successive thermal-annealing stages. Irradiation increased the dark noise by up to three orders of magnitude and degraded the single-photon resolution by up to a factor of ten, with resolution lost entirely at the highest fluence, while no shift in breakdown voltage was observed. Thermal annealing partially recovered the performance, reducing the dark noise by up to a factor of two and restoring the single-photon resolution.

For over eighteen years, the electron current sheet measured in the Magnetic Reconnection Experiment (MRX) has stood a factor of 2–5 wider than predicted by MRX-like kinetic simulations, and the measured electron force balance has not closed with the classical terms alone. As a consequence, the dominant nonideal terms responsible for breaking the frozen-in condition have remained unexplained. Here, two-dimensional kinetic simulations with binary Coulomb collisions are performed in a cylindrical geometry representative of MRX, at the realistic hydrogen mass ratio $m_i/m_e = 1836$ and at MRX-relevant collisionality. For the first time, these simulations reproduce the measured electron current sheet half-width. The simulated value, $\delta_{BT} = 0.744 \pm 0.054$~cm or $6.26 \pm 0.45$ electron skin depths ($d_e$), lies within the experimental range of 5.5–7.5~$d_e$. The electron force balance closes through the classical channels alone: the pressure-tensor divergence supports 76% of the nonideal electric field and collisional friction the remainder. The historical force-balance deficit reappears only when the simulated layer is sampled at the experimental 3~cm outflow resolution, suggesting that the deficit reflects probe resolution rather than anomalous dissipation. Beyond this reproduction, an analytic model of the layer width is developed that orders the meandering electrons by the coherence of their orbits against collisions. In this model, the Dreicer ratio $E_D/|E_y|$ selects the electrons whose current-carrying motion survives, and the resulting width prediction brackets the measured values across a wide collisionality scan. A discrepancy remains in the width normalized to the local electron gyroradius ($\rho_e$), whose measured value lies a factor of 3–6 above both the model and the simulations.

Seismology increasingly depends upon large data retrieval across multi-datacenter platforms. Yet, data procurement often demands domain expertise, user burden, and is difficult to reproduce. As archives continue to grow, translating scientific intent into structured workflows that operate across multiple repositories and produce high quality, AI ready data is becoming an increasingly urgent challenge. We present TREMORS (Text Referenced Event Mapping and Output Renderer for Seismographs), an agentic framework that uses large language model reasoning within a constrained execution graph to automate seismic data retrieval. TREMORS translates natural language queries into a structured intermediate schema, which drives execution through a constrained LangGraph workflow. Example workflows demonstrate support for both event-based and continuous waveform acquisition. The framework is designed to extend across heterogeneous multi-datacenter systems through a portable schema and modular workflow components. This work positions agentic workflows as the catalyst that will connect scientific intent with distributed seismic data systems, enabling a future of reproducible, data-driven inquiry while reducing the burden of routine acquisition tasks.

A single oblique metallization step gives conventional gratings one-sided silver decoration, established by electron microscopy and elemental mapping. Opposite-face reversal measurements yield finite-aperture red/green/blue transmission ratios up to 1.74 for a 1-$\mu$m grating and 1.5-1.8 for a 30-$\mu$m grating, with reference-normalized transmittances for the latter. Flat-side coating and reduced silver dose give ratios near unity. Maxwell calculations on self-shadowed growth profiles reproduce the directional sign qualitatively and attribute the contrast to diffraction-channel redistribution. The structure remains reciprocal, including its normal-incidence zeroth-order transmission. These results establish directional metallization as a route to direction-selective optical transmission without multilayer alignment or overlay lithography.

Next-generation fusion reactor devices such as SPARC, ARC, and CFETR will operate in extreme neutron environments that compromise the magnetic sensors traditionally used to reconstruct plasma equilibria. However, reliable knowledge of the plasma equilibrium–including magnetic flux surfaces, safety factor profiles, and shaping parameters–is indispensable for real-time control, disruption avoidance, and physics interpretation. The Fusion Equilibrium Challenge invites the NeurIPS community to confront a deceptively simple but scientifically rigorous inverse problem: reconstruct the two-dimensional poloidal flux function psi(R,Z) and a suite of scalar equilibrium parameters from non-magnetic diagnostics alone, namely external poloidal-field coil currents and Thomson-scattering electron temperature/density profiles. The challenge provides the first open-access, harmonized multi-machine benchmark for fusion, releasing a curated dataset of 9,113 DIII-D shots and 2,416 MAST shots–filtered for Thomson-diagnostic availability, feature completeness, and EFIT-reconstruction quality. Each shot is packaged into a standard Parquet file containing approximately 260 (DIII-D) / approximately 80 (MAST) EFIT flux maps and rich high-rate diagnostics. Two complementary awards reward intra-machine reconstruction fidelity (S_model) on DIII-D and zero-shot cross-machine generalization (G_ratio) to the topologically distinct MAST spherical tokamak. We argue that the challenge functions as a benchmark for reactor-ready equilibrium inference and as a probe of how far machine learning can be pushed toward truly machine-agnostic plasma state estimation.