Papers

In this paper, it is shown that in spacetimes of maximal proper acceleration, there is a reduction of the mean lifetime of the muon. We consider several candidates for the maximal proper acceleration. For all of them, the induced corrections are small, but for the case when the maximal proper acceleration is identified with the Schwinger's acceleration, the effect is detectable in future muon collider facilities.

Fano resonances based on coherent interference between localized Lorentz modes and Fabry-Perot radiation continua have been widely explored for microwave metamaterial sensors, filters, and photonic devices. However, conventional Fano metasurfaces suffer from angular dispersion and polarization-dependent spectral variations, limiting robustness under dynamic illumination. Here, we propose a band-folding-enabled flatband engineering strategy to realize robust Fano resonances in a planar microwave metasurface with a 2x2 enlarged cell of orthogonally arranged H-shaped metallic resonators. The enlarged cell induces Brillouin-zone folding, generating a nearly dispersionless flatband near 15.3 GHz with suppressed momentum dependence and enhanced photonic confinement. This flatband state provides a high-Q localized resonance strongly coupled to the FP radiation continuum, producing a stable asymmetric Fano response. Furthermore, the orthogonal resonator configuration supports two polarization-decoupled dipole modes with comparable excitation efficiency, reducing polarization-induced spectral distortion. Near-field measurements and simulations verify localized flatband features, while far-field experiments demonstrate stable Fano responses under oblique incidence from 0{\deg} to 30{\deg} with minor resonance variation. A temporal coupled-mode theory model is developed to quantitatively describe Fano spectral evolution under different illumination conditions. This work establishes a flatband-mediated route toward robust microwave Fano metasurfaces and offers a general strategy for stable resonant photonic device design.

State production and its subsequent decay can each require a different angular-momentum coupling treatment. Although long recognized, this distinction is often overlooked in the two-step model, where $LS$ coupling is traditionally applied to both production and decay ($LS/LS$), even when the decay requires $LSJ$ coupling. We introduce a hybrid $LS/LSJ$ formulation that uses established angular-momentum recoupling theory to project the initial $LS$ alignment onto the fine-structure $J$ states, evaluates their decay in $LSJ$ coupling, and, for well-separated $J$ levels, sums their contributions incoherently. The $1s2s2p\,{}^{4}\!P_J$ manifold produced by single-electron capture in $\mathrm{C}^{4+}(1s2s\,{}^{3}\!S)+\mathrm{He}$ collisions provides a good test case, with $M_L$-resolved theoretical production cross sections and absolute measurements of the Auger single-differential cross section available. Using the same $LS$ production cross sections in both treatments, we find that the hybrid $LS/LSJ$ treatment strongly suppresses and can even invert the Auger anisotropy relative to the traditional $LS/LS$ treatment, substantially improving agreement with the measurements. Such an incoherent hybrid formulation provides the appropriate framework when $LS$ coupling governs the initial interaction dynamics while well-separated fine-structure levels require an $LSJ$ description of the decay.

Imperfect photon sources degrade the performance of interference-based linear-optical entangling gates. We derive a universal fidelity law for these gates in terms of two standard source metrics: Hong-Ou-Mandel visibility and second-order coherence. We model the multiphoton component as an arbitrary overlap between noise and signal photons. While different values of the signal-noise overlap produce different error structures, associated with different physical platforms, all result in the same fidelity law. We test the model on our actively synchronized CNOT-gate experiment and against published results across different platforms, with no free parameters. This framework turns source characterization into a unified benchmark for photonic quantum computing.

We present a geometry-refinement workflow that adapts to the cost of an energy–gradient evaluation and the reliability of molecular topology. An initial Cartesian calculation and continuous bonding diagnostics determine whether to begin in projected Cartesian coordinates or in chemically adapted internal coordinates. Chemical perception generates local redundant measurements; sparse rank and conditioning tests combine them into nonredundant, symmetry-adapted coordinates; and a common optimizer uses the selected representation with resident or external calculators. For inexpensive gradients, the projected Cartesian route limits coordinate overhead; a single monitored internal-coordinate step can then provide chemical interpretability. When gradients are costly and topology is certified, symmetry-adapted internal coordinates reduce expensive energy–gradient calls. A quadraticized map improves localized anharmonic cases, while mixed Cartesian/internal charts cover uncertain or complex topologies. Local sparse operators and resident force-field and tight-binding calculators extend both routes to large systems. The same Cartesian calculator contract supports multilevel energy and gradient composition. The measured coordinate pipeline and recurrent resident tight-binding update show near-linear scaling over the tested size ranges; this statement applies to those qualified components, not to arbitrary electronic-structure calculations or cold starts. For certified, directly separable internal charts, the optimizer can use chemically designed Morse or Gaussian maps to quadraticize the local step problem. The quadraticized branch uses a short geometry-local history, while delocalized charts remain unchanged. This reduces iterations in difficult minima without changing the electronic potential or coordinate chart.

Probably not. For Airy-type initial data, although nearly all lobes accelerate, the principal lobe remains essentially stationary. Moreover, the overall wave packet gradually disperses.

We study various discretisation schemes for the regular Sturm–Liouville operator on a bounded interval and for the Laplace operator on an arbitrary planar domain, in both cases subject to zero Dirichlet boundary conditions. The objective is to identify a discretisation scheme such that the corresponding discretised operator—a matrix of size $N \times N$—produces $N$ eigenvalues that approximate as closely as possible the first $N$ eigenvalues of the corresponding operator at the continuous level. By means of numerical experiments we examine several conventional discretisation schemes, and we corroborate the known fact that the conventional discretisations fail to achieve the objective, with the failure attributable to the poor approximation of high-index eigenvalues, and, as a result, to the non-uniform spectrum approximation. In contrast, the newly proposed discrete sine transform based discretisation scheme is designed in such a way that it replicates the asymptotic behaviour of high-index eigenvalues, thereby providing the sought uniform spectrum approximation. Of equal significance is the fact that the proposed discrete sine transform based scheme can be, unlike many Fourier transform based methods, applied to non-rectangular domains.

All-optical diffractive neural networks (DNNs) offer low latency, low energy consumption, and massive parallelism. However, their scalability in terms of network scale and multi-task processing remains limited. Here, we experimentally demonstrate a large-scale metasurface-based diffractive learning machine for angle-multiplexed multi-task processing. Our system employs a metasurface-based reflection cavity to realize a five-layer DNN comprising 115 million fixed, untrained diffractive nodes within a compact volume of 216 mm^3. By integrating this large-scale, untrained DNN with a lightweight, trainable digital backend containing only 7,680 parameters, our system experimentally achieves an accuracy of 95.3% on the standard full-scale CIFAR-10 dataset, significantly outperforming previous optical neural networks and rivaling large-scale digital models. Using a novel incident-angle-multiplexing scheme, our system experimentally achieves parallel processing of 11 tasks with negligible performance degradation, representing the largest multi-task processing capacity reported to date. Our work provides a scalable and energy-efficient solution for high-performance edge computing.

Purpose: To develop a wearable, stretchable radio-frequency (RF) coil that conforms to the extremities and improves the filling factor in low-field MRI. Methods: A stretchable solenoid RF coil was fabricated by stitching a sinusoidally arranged Litz-wire conductor onto an elastic textile. The coil was compared with four rigid coils, including two routinely used designs and two prototypes designed to isolate the effects of conductor material and stretchability. Performance was characterized through quality-factor, loading-factor, transmit-efficiency, and image-based signal-to-noise ratio (SNR) measurements. Phantom and in-vivo knee experiments for one volunteer were performed using two portable MRI systems operating at 3.53 and 3.04 MHz. Results: The electrical performance of the selected Litz wire decreased with increasing frequency, becoming a slight disadvantage at 3.53 MHz. Nevertheless, the stretchable coil provided the highest phantom SNR in both systems, exceeding that of the best-performing rigid coil by approximately 8 % at 3.53 MHz and 20 % at 3.04 MHz. In vivo, its global SNR was 5 % lower than that of the best rigid coil at 3.53 MHz and 19 % higher at 3.04 MHz. Compared with the larger rigid coil required when knee positioning is constrained, the corresponding SNR improvements were approximately 18 % and 80 %. Conclusion: Anatomical conformity can compensate for the frequency-dependent electrical limitations of Litz wire in stretchable low-field RF coils. The proposed design provided SNR comparable to or greater than the best rigid designs while facilitating coil placement in subjects for whom smaller rigid coils may be impractical.

Photon statistics provides a new control dimension for strong-field bound-state formation. Using a quantum-optical phase-space formulation coupled to time-dependent Schrödinger-equation calculations, we show that field fluctuations enhance multiphoton Rydberg excitation by broadening AC-Stark-shifted resonances, but suppress tunneling-mediated recapture even as ionization increases. A quantum-statistical recapture model attributes the latter to ensemble-induced dispersion of relative path phases. Photon statistics also reshapes Rydberg-manifold parity, linking field statistics to recapture coherence and final-state symmetry.

Nuclear excitation by electron capture (NEEC) provides a unique pathway for coupling electronic and nuclear dynamics, but its description in dense matter commonly relies on electronic structures of isolated ions. Here we show how dense environments reshape the available NEEC capture channels. Using a finite-temperature average-atom model, we assess channel availability by jointly considering electronic localization, resonance energy matching, and vacancy availability. Near solid density, the shallow $6p$ states remain sufficiently localized to support resonant electron capture that drives the 8.356-eV isomeric transition in $^{229}$Th, whereas higher valence-like states merge into the continuum and no longer constitute localized capture channels. Calculations at different temperatures and densities reveal distinct windows of channel availability arising from the interplay among pressure-induced delocalization, shifts in resonance energy, and vacancy formation. Coupling the reconstructed channels to particle-in-cell simulations of laser-driven $^{229}$Th further shows that electronic structure at finite density can substantially alter the predicted cumulative NEEC yield. These results demonstrate how the electronic environment governs resonant capture pathways, highlighting its essential role in nuclear excitation driven by electrons in dense matter.

Cavity and waveguide magnonics have recently emerged as frontiers for investigating light-matter interactions. In these systems, coupling magnons into meticulously engineered photonic lattices with rich band structures provides a promising platform for tailoring magnon-photon interactions. However, how these hybrid interactions behave in the bandgaps of photonic lattices—specifically, the formation of magnon-photon bound states (MPBSs)—remains largely unaddressed. Crucially, these bound states arise through a mechanism that is fundamentally distinct from bound states in the continuum (BICs). Here, we experimentally demonstrate intra-bandgap bound-state magnonics by coupling yttrium iron garnet (YIG) spheres to a microwave photonic crystal waveguide. At room temperature, we observe MPBSs featuring highly tunable spatial localization, power-dependent interaction modulation under microwave pumping, and long-range magnon-magnon interactions mediated by mode overlap. These unique features bridge fundamental insights into intra-bandgap light-matter interactions with practical applications, offering an avenue for scaling up coherent networks and investigating many-body physics through engineered interactions.

Verification of probabilistic weather forecasts remains a crucial aspect of numerical weather prediction, as new AI-based models become more widely used alongside the more traditional physics-based ensemble forecasting systems that continue to be developed and improved. We present a first-of-its-kind idealized probabilistic dataset composed of one-dimensional cases aimed at analyzing the behavior and properties of verification methods for probabilistic forecasts and comparing their behavior. It covers a wide range of probabilistic cases, such as constant, localized events, gradients, fronts, noisy, bimodal, and limiting cases. Moreover, the code associated with the dataset provides great flexibility for customizing the experiments it covers. The dataset represents the first building block of the more extensive comparison dataset of the Bridging The Gap project, which aims to facilitate the development and comparison of spatial verification methods for probabilistic forecasts.

This study investigates the aerodynamic feasibility of high-speed evacuated tube transport (ETT) using an integrated CFD-machine learning framework for pod geometry optimization and a staged converging-diverging (CD) tube concept for supersonic operation. The pod geometry is parameterized using composite cubic Bezier curves with seven design variables. Latin Hypercube Sampling is used to generate 200 configurations across subsonic and supersonic regimes, which are evaluated using two-dimensional axisymmetric ANSYS Fluent simulations validated against published compressible-flow data. The resulting CFD dataset is used to train surrogate models for rapid drag prediction. XGBoost provides the best overall performance, achieving a test R^2 of 0.9524, and is coupled with Differential Evolution and Particle Swarm Optimization to identify low-drag geometries that are subsequently verified using CFD. The study further examines a staged CD tube architecture combining controlled area variation with gradual pressure reduction to mitigate choking during supersonic acceleration. In the idealized axisymmetric simulations, the proposed strategy enables transition from atmospheric entry at Mach 1.6 to near-vacuum cruise at Mach 2.5 and a blockage ratio of 0.50 without forming a choked annular flow region. Total drag decreases from 68,913.7 N under atmospheric supersonic conditions to 120.5 N in the final near-vacuum stage, associated with a transition from wave-drag-dominated to viscous-shear-dominated resistance. The results demonstrate the potential of combined pod-shape optimization and staged pressure-area management for reducing aerodynamic resistance in supersonic ETT systems.

Intrigued by the response of T cell receptors to the presence of a few agonist ligands, we propose a minimal model that can achieve similar performance. The model consists of a small cluster of immobile receptors that bind reversibly to two types (correct/incorrect) of ligands in the environment, with slightly weaker binding strength for the incorrect one. It features binding-state coupling between nearest-neighbor receptors, and receptors in the bound/free states are activated/deactivated by specific enzymes, with rates that allow kinetic proofreading. It is found that, for a range of binding-state coupling strength, incorrect ligands alone cannot activate the receptors, but the binding of merely one correct ligand to a receptor is sufficient to promote the activation of other receptors via induced binding to incorrect ligands. Both response time and signal amplification increase as the receptor binding-state coupling strength increases until it reaches an optimal range to achieve the most rapid and sensitive response. These results suggest a possible mechanism for a speedy and specific response of receptors to very few correct ligands in biological and artificial systems at the subcellular scale.

Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead. However, the predictions are under-dispersive in a state-dependent manner. Rank histograms reveal overconfidence issues for strong ENSO events and a warm bias for weak events. February predictability arises from canonical Bjerknes feedback dynamics reinforced by subtropical-tropical interactions resembling the North Pacific Meridional Mode (NPMM). The best February-initialized predictions feature warm western Pacific subsurface anomalies and La Nina anomalies at initialization, with equatorially confined anomalies and relatively weak sensitivities to extratropical influences thereafter. Second-year ENSO predictability in August-initialized predictions instead emerges through a delayed pathway characterized by cross-basin preconditioning. Skillful cases begin with a cold tropical equatorial South Atlantic and a warmer tropical western Indian Ocean, while Pacific processes play a reduced role until the following year. SMYLE matches the skill of the cross-validated XRO at both December targets, and its sensitivities agree with the observationally fitted benchmark, though the Atlantic couplings appear misrepresented. These findings suggest that improving the initial Pacific state and cross-basin coupling fidelity in GCMs may help improve ENSO prediction.

Post-selected metrology can exceed the standard quantum limit of single photon interference by concentrating all of the available Fisher information in a subset of post-selected outcomes. Here we apply this principle to a standard two-path interferometer, activating the Fisher information concentrated in the low probability output port by transferring the phase sensitivity to the circular polarizations of the photon. We obtain a phase gradient of the post-selected interference fringe that corresponds to that of a four-photon NOON state, providing a direct comparison of the performance of multi-photon interference with single-photon post-selection.

Thermosolutal dendritic solidification involves interface evolution, solute diffusion, heat transfer, and melt flow over markedly different time scales. In lattice Boltzmann simulations, a single numerical time interval may place different transport processes in unfavorable relaxation ranges, while asynchronous updates require consistent transfer of phase-change contributions. To address these issues, a unified multirate multiple-relaxation-time lattice Boltzmann method is developed for thermal, solutal, and thermosolutal dendritic solidification. The coupled fields share a common moment-space framework but evolve at different update rates. The concentration and temperature source terms are separated into transport-related and phase-change contributions, and each resolved phase increment is used to immediately transfer the corresponding solutal and latent-heat contributions. The method reproduces characteristic dendritic morphologies and tip-velocity trends under pure diffusion and forced convection, with weak sensitivity to the tested update factors. Directional solidification over Lewis numbers \(Le=1\)–\(1000\) captures the transition from nearly planar to cellular and strongly branched growth, including vertically aligned dendrites and solute-rich interdendritic channels for saline water, in qualitative agreement with experiments. Source-coupling ablation shows that delayed coarse-step transfer produces increasingly strong local source pulses and eventual loss of numerical stability as time-scale separation increases, whereas phase-step instantaneous transfer remains stable over the tested conditions. These results demonstrate the applicability of the proposed framework to dendritic solidification with strongly separated transport time scales. The source code is publicly available in the DendriteLBM repository.

Reconstructing the stress field of a planar viscoelastic flow from optical measurements loses its direct evidence wherever optical coverage is interrupted, and no improvement in optical precision restores an observation that was never made. We characterize what a second, velocity channel adds, and what neither channel can supply. Two calibrated optical components determine the local deviatoric stress pointwise, while velocity constrains spatial stress variation through momentum balance, so the two channels are complementary rather than redundant. The isotropic part of the stress is unobservable to both: the divergence of an isotropic field is a pure gradient, which the Leray projection annihilates, so every representable isotropic mode lies in the joint null space. That accounts for the null space exactly when the optical field is complete, and bounds it from below otherwise, since finite incomplete sampling and aperture zeros can remove further directions. We verify the count directly on three discretizations. In paired synthetic tests with finite measurement apertures, spatially correlated noise and optical stripe dropout, adding velocity reduces the mean whole-domain deviatoric error from 50.40% to 27.82% at 3% reference noise, and the improvement survives shared gaps, inverse-grid refinement at fixed physical sampling, and a constitutively generated stress field. The improvement does not rest on how the regularization parameter is chosen: it holds under both the expected-norm discrepancy rule and generalized cross-validation, and we report each selection with its position in the search interval, which is where the two rules differ.

Optical chirality quantifies the geometrical twisting of electromagnetic fields and underlies chiral light-matter interactions, yet its conventional formulation captures only the spin-associated chiral geometry of light. We introduce orbital optical chirality and derive its continuity equation, revealing physical properties distinct from those of angular momentum. For vortex beams, spin and orbital optical chiralities follow the spin and orbital indices, respectively. Applied to a twisted nanorod dimer, orbital optical chirality gives rise to vortex dichroism through quadrupolar hybridized modes, while dipolar modes exhibit only circular dichroism. These results establish a unified framework for optical chirality and provide access to chiral geometries beyond those resolved by spin optical chirality alone.

Evaluating spin Hamiltonians with dense, long-range interactions requires repeated global summation over many pairwise couplings, making energy evaluation costly on conventional electronic hardware. Here, we realize a planar optical architecture that evaluates such interaction energies directly in momentum space. For translationally structured couplings, the interaction kernel is encoded in the spatial transmission of a subwavelength-thick planar optical element placed in the Fourier plane. Pointwise spectral weighting followed by total-power detection reduces the quadratic spin Hamiltonian to a single optical signal, avoiding repeated digital multiply-accumulate operations during energy evaluation. We experimentally apply this approach to frustrated finite-range models and fully connected spin systems with oscillatory long-range couplings. The measured energies agree closely with numerical calculations, while optical annealing reproduces low-energy configurations and landscape statistics, reaching a normalized solution quality of 99.9%. These results establish momentum-space planar optics as a compact physical computing primitive for global-interaction problems in statistical physics and optimization.

In this paper, we study the analogue of the Kolmogorov scale in surface gravity wave turbulence, characterized by the cutoff wavenumber $k_c$ at which the power-law inertial range transitions into the dissipation range. We perform numerical simulations of the primitive dynamical equations with a broad-scale dissipation of magnitude $\gamma_0 k^2$ in spectral space to establish the relation between $k_c$ and $\gamma_0$. Our results show a scaling $k_c\sim\gamma_0^{\beta}$, where $\beta$ depends on the slope $\alpha$ of the power-law spectrum. We find that $\beta(\alpha)$ agrees more closely with the prediction obtained by balancing the nonlinear and dissipation terms in the dynamical equations than with that based on the kinetic equation. This observation reveals that non-resonant triad interactions play a more significant role than resonant quartet interactions in the formation of $k_c$.

Trapped-ion quantum networks rely on efficient ion-photon interfaces, while objective-based free-space collection systems must balance high photon collection with sufficient working distance for practical integration with ion-trap hardware. Here, we design and characterize an ion-photon interface with a long working distance of 26.15 mm and a high numerical aperture of 0.5. The core of the interface is a photon-collection objective consisting of six commercial spherical lenses with optimized spacing. The interface achieved an overall fiber-coupled photon-detection efficiency of (1.29$\pm$0.09)% from the ion to the detector and maintains fiber coupling over several hours without active realignment, supporting stable operation of the ion-to-fiber interface. Photon-correlation measurements show clear antibunching in the fiber-coupled fluorescence, confirming the preservation of non-classical photon statistics. These results demonstrate a practical implementation of a long-working-distance photon collection interface for trapped-ion quantum network experiments.

Topological wave systems have largely evolved along two distinct paradigms: gapless topological semimetals and gapped topological insulators. While topological semimetals support bulk transport, they generally lack intrinsic selectivity among propagation channels; topological insulators enable robust transport but confine it to narrow interfaces, limiting spatial utilization. Here, we theoretically demonstrate and experimentally realize time-reversal-invariant spin-valley photonic half-semimetals (HSMs), which exhibit a dual semimetal-insulator topology within a single bulk band structure. In HSMs, the bandgap closes selectively in spin-valley space: for a given spin (valley), one valley (spin) is semimetallic while the other remains insulating. This coexistence of spin- and valley-resolved gapless and gapped band structures makes HSMs fundamentally distinct from both conventional semimetals and insulators. As a defining bulk phenomenon, an HSM functions as a spin-valley-locked beam splitter, intrinsically enabling valley-selective spin routing. Moreover, when four complementary HSMs are assembled into a periodic superlattice, the same dual topology enables reciprocal spin-valley-resolved multilane helical transport with 100% spatial utilization. These results establish HSMs as a platform for selective bulk wave control and multichannel topological transport beyond conventional topological phases.

Scaling quantum control for atoms, ions, and solid-state emitters requires gigahertz-frequency spectral control of high-power visible light in a volume-manufacturable platform. Silicon nitride photonics provides high power handling and CMOS-foundry compatibility but has no intrinsic mechanism for high-speed modulation. Integration with piezoelectric materials enables acousto-optic phase modulation, and mechanical resonant enhancement has made it efficient at gigahertz frequencies. However, a single resonance restricts the modulation waveform to a single tone, imposing Bessel-function sideband amplitudes and limiting frequency-shifting efficiency to 33.9%. Here we engineer a silicon nitride acousto-optic microstructure to support harmonically spaced resonances at 1.14 GHz and 2.28 GHz, each strongly optomechanically coupled to a 730 nm guided optical mode, so that tailored non-sinusoidal modulation waveforms can be resonantly synthesized. By piezoelectrically controlling the two mechanical amplitudes and their relative phase, we demonstrate 50% conversion to one sideband (1.5x the single-tone theoretical maximum), a flat seven-line comb, and a frequency shift with 60 dB carrier and 53 dB image suppression - to our knowledge the highest reported for an integrated modulator. The devices are fabricated in a 200 mm CMOS foundry, and we measure 91.7% yield without post-fabrication tuning across 36 devices from three wafers. We also show how the technique can be straightforwardly scaled to three or more harmonics. This result overcomes the trade-off between resonant enhancement and spectral programmability, with important consequences including improved single-qubit gate efficiency for hyperfine qubits.

We propose a two-stage production method to overcome existing supply-constraints for the alpha-emitter $^{149\mathrm{g}}$Tb, a promising candidate for Targeted Alpha Therapy (TAT) with no existing globally scalable production pathway. Although awaiting experimental measurement of the $^{150}$Gd(p,2n)$^{149\mathrm{g}}$Tb cross section, the proposed method could produce $^{149\mathrm{g}}$Tb at clinical scale and beyond on readily available proton cyclotrons, enabled by production of the extinct but long-lived isotope $^{150}$Gd, a pure alpha emitter with a 1.79 million year half-life. Stage one generates $^{150}$Gd feedstock by irradiating natural Eu or enriched $^{151}$Eu with $\gtrsim$10 MeV protons, neutrons or photons. Stage two produces $^{149\mathrm{g}}$Tb from fabricated $^{150}$Gd targets by driving the $^{150}$Gd(p,2n)$^{149\mathrm{g}}$Tb reaction with $\gtrsim$14 MeV protons, accessible on over 700 reported cyclotrons worldwide. Fast fusion neutrons appear to offer the most scalable pathway for $^{150}$Gd production: even with a large $^{149 \mathrm{g}}$Tb dose size of 1 GBq and 40 million administered doses/yr, we estimate this would require neutrons produced by only 6.8 megawatts of steady-state deuterium-tritium power to produce the required $^{150}$Gd, far below expected capacity in the next decade. The route described here, if validated, would enable $^{149\mathrm{g}}$Tb supply at the scale needed to support clinical development of $^{149\mathrm{g}}$Tb-based TAT.

Liquid scintillator detectors instrumented with distributed silicon photomultiplier (SiPM) arrays can be used in compact, topology-sensitive, and low-background experiments, but the temperature dependence of SiPMs immersed directly in the scintillation medium has not been widely characterized. We report the operation of a 125-liter linear-alkylbenzene-based liquid scintillator detector read out by 125 SiPM channels immersed in the active volume, over the range from room temperature to $-30\,^{\circ}\mathrm{C}$. The detector response was measured with cosmic-ray muons, including stopping muons followed by their Michel-electron decay. Cooling from $+15\,^{\circ}\mathrm{C}$ to $-30\,^{\circ}\mathrm{C}$ reduced the SiPM dark-count rate by a factor of 13.5, increased the single-photoelectron response by 49.1%, and increased the cosmic-ray muon light yield by 16.7%. The improved photoelectron separation and baseline stability at low temperature enabled a selection of stopping-muon events, from which the effective muon lifetime was measured to be $1959\pm132\,\mathrm{ns}$, consistent with the value expected for a hydrocarbon scintillator once $\mu^{-}$ capture on carbon is taken into account.

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land–atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency–intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency–intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.

Liquid scintillator detectors instrumented with photosensors inside the scintillation volume preserve local optical information that is largely lost in conventional boundary-readout geometries. We demonstrate that this information is sufficient for charged-particle topology reconstruction using a sparse three-dimensional lattice of silicon photomultipliers. After validating the Geant4 detector response against measured photon-count distributions, a simulation-trained, time-informed convolutional neural network reconstructs the entry and exit points of through-going muons with median residuals of 1.91~cm and 2.39~cm, respectively. The reconstructed endpoints are geometrically consistent with acceptance regions defined by external trigger counters in cosmic-ray muon data. The same framework also reconstructs the production vertices of simulated positron starting-track events with a median residual of about 4.5~cm. These results establish the feasibility of topology-sensitive reconstruction using sparse in-liquid photosensor arrays in homogeneous liquid scintillator detectors.

Transition states are defined by reaction pathways, yet most machine-learning methods predict them as isolated geometries. We introduce MARC-TS, a two-stage framework that learns a continuous, endpoint-conditioned path, queries it at any resolution and uses local path context to refine a transition-state candidate. We construct T1x-IRC-8K, a dataset of 8,209 reactions and 1,088,725 path-resolved geometries. On held-out reactions, the path model reduced complete-path error by 48.4% relative to endpoint interpolation, and the localizer achieved a mean aligned structural error of 0.127 Å. Quantum-chemical optimization and vibrational analysis yielded 405 frequency-confirmed first-order saddle-point candidates from 410 predictions. In a 100-reaction nudged elastic band comparison, learned-path initialization reached a joint geometry-and-force target for 66% of reactions, compared with 12% for geometric interpolation after 100 optimizer steps. By treating the path as a reusable representation rather than an auxiliary output, MARC-TS connects transition-state prediction, mechanistic interpretation and quantum-chemical refinement.