Papers

Conventional infrared (IR) imaging techniques depend on IR cameras based on narrow-bandgap semiconductors, which offer limited spectral bandwidth, coupled with a separate lens. Recently, advances in nonlinear flat optics have opened a novel pathway for converting IR signals into the visible through nonlinear generations, enabling the direct visualisation of IR images using standard visible cameras. However, the narrow spectral bandwidth and the requirement for an additional lens remain the key challenges. Here, we address both issues via a novel adaptive and multifunctional IR-to-visible imaging platform offering tunable bandwidth and focusing simultaneously. We utilise sum-frequency generation (SFG) to convert IR light into the visible, by introducing a pump beam modulated by a spatial light modulator (SLM) to construct a virtual metalens enabling precisely controlled focusing of the generated nonlinear optical field. As a result, we demonstrate both theoretically and experimentally an optical focusing mechanism with a tunable focal length, achieved by varying the pump and signal wavelengths and modulating the phase distribution. Furthermore, since the focal length depends on the input signal wavelength, the imaging plane position varies accordingly, indicating a promising potential for the multispectral IR imaging applications. Our upconversion platform delivers SLM-controlled, programmable multispectral focusing for next-generation IR imaging, opening new avenues in the fields of computational and multispectral imaging techniques.

We explore the ground state properties and excitation spectra of one-dimensional, three-component bosonic mixtures, where two majority components form a self-bound droplet coupled to a third, minority component. Within a self-consistent Lee-Huang-Yang framework, we identify distinct mixed self-bound droplet phases and characterize their phase transitions through variations of either the particle number of the majority components or the intercomponent coupling. The ensuing phases demonstrate that the minority component is untrapped, partially trapped, or fully trapped by the majority droplet species. These states are characterized by their binding energies captured by the chemical potentials and their low-amplitude excitation spectrum, including mode crossings at the particle-emission threshold. We further derive effective reduced models in the high-imbalance limit, which accurately reproduce the numerically computed ground states, while providing analytical insights into the role of quantum fluctuations. Our results establish the stability and structure of mixed droplet phases provide experimentally relevant signatures for their identification.

Structured optical materials provide a promising platform for photonic quantum information processing in free space. Beam splitters, a fundamental building block of photonic circuits, have recently been demonstrated in free space using geometric-phase optical elements. These devices coherently mix circularly-polarized transverse modes of freely-propagating optical fields, including modes carrying orbital angular momentum. In this work, we investigate liquid-crystal metagratings as electrically tunable beam splitters for transverse-momentum optical modes. By exploiting the voltage-controlled birefringence of liquid-crystal metasurfaces, we experimentally tune the splitting ratio of the device and thereby control the degree of two-photon interference between indistinguishable photons. At the output, photons are spatially resolved on different regions of a time-resolved single-photon-sensitive detector, enabling the reconstruction of coincidence maps in the Fourier plane. This approach is readily scalable and enables highly parallel coincidence measurements across a large number of optical modes.

A dissipative time crystal is an emergent phase in driven-dissipative quantum many-body systems, characterized by sustained oscillations that break time-translation symmetry spontaneously. Here, we explore nonequilibrium phase transitions in a dissipative Rydberg system driven by a microwave (MW) field and demonstrate their critical sensitivity to high-precision MW sensing. Distinct dynamical regimes are identified, including monostable, bistable, and oscillatory phases under mean-field coupling. Unlike single-particle detection–where the beating signal decays linearly with MW field strength–the time crystalline phase exhibits high sensitivity to MW perturbations, with rapid, discontinuous frequency switching near the monostable-oscillatory boundary. The abrupt transition is rooted in spontaneous symmetry breaking in time and is fundamentally insensitive to the background noise. On this basis, a minimum detectable MW field strength on the order of 1nV/cm is achieved by leveraging this sensitivity. Our results establish a framework for controlling time crystalline phases with external fields and advance MW sensing through many-body effects.

The relaxation of ortho states of a helium-like Mg donor in silicon is investigated by measuring the modulation of background radiation transmission through impurity centers under pulsed photoexcitation. Long-lived states of the spin-triplet 1s(3T2) group with a lifetime of about 20 ms are observed. The temperature dependence indicates that the relaxation is governed by the Orbach mechanism with an activation energy ~13 meV, which is close to the exchange splitting energy of the excited 1s states of the Mg donor.

Biological and biomedical samples are routinely examined using focused two-photon (2P) fluorescence microscopy due to its intrinsic axial sectioning and reduced out-of-focus bleaching. However, 2P imaging often requires excitation intensities that can damage samples through ionization and radical formation. Additionally, the lateral resolution of 2P microscopy is lower compared to linear one-photon (1P) fluorescence microscopy. Widefield 2P microscopy, using cameras, holds promise for reducing photo-toxicity while maintaining high image acquisition rates. Widefield imaging trades the high power and short integration times of sequential single point scanning for the low power and extended integration times of parallel detection across millions of pixels. However, generating effective axial sectioning over arbitrarily large fields of view (FOVs) has remained a challenge. In this work, we introduce 2P Random Illumination Microscopy (2P-RIM), an easy-to-implement 2P widefield technique, that achieves low photo-damage, fast imaging, micrometric axial sectioning, and enhanced lateral resolution for arbitrarily large FOVs. By using widefield speckled illuminations in conjunction with an image standard deviation matching algorithm, 2P-RIM demonstrated multicolor imaging over FOVs greater than 200 um, lateral resolution 220 nm, axial sectioning 2 um, and peak excitation powers about 10 times lower than those used in focused laser scanning microscopy.

Physics-informed neural networks (PINNs) have recently emerged as a promising alternative for extracting unknown quantities from experimental data. Despite this potential, much of the recent literature has relied on sparse, high-fidelity data from direct numerical simulations (DNS) rather than experimental sources like particle image velocimetry (PIV), which are not suitable for validating all reconstructed quantities. In the case of PIV, for example, pressure is not directly measured and the data have imperfections such as noise contamination or a limited field of view. To overcome these limitations, we present a novel methodology where PINNs are first trained on a RANS simulation such that it learns all states at every location in the domain. We then apply transfer learning which updates the PINN using sub-sampled PIV data. The resulting predictions are in significantly better agreement with the full PIV dataset than PINNs which are trained on experimental data only. This work builds on the recent literature by integrating a Spalart-Allmaras turbulence model and applying hard constraints to the no-slip wall boundary condition. We apply this new methodology to a two-dimensional NACA 0012 airfoil inclined at an angle of attack, $\alpha$ = 15 degrees, for two Reynolds numbers of Re = 10,000 and 75,000. The proposed methodology is initially validated using large eddy simulation (LES) data and then demonstrated on experimental PIV data. Our transfer learning approach results in improved predictions and a reduction in training time when compared to using a random network initialisation.

Universities are widely expected to respond to technological transitions through rapid reconfiguration of programme demand and curricular supply. Using four decades of longitudinal administrative cohorts (1980-2019) from a large public university, we examine whether technological change is translated into observable shifts in programme hierarchy, or instead absorbed by institutional mechanisms that preserve structural stability. We show that programme rankings by entrant volume remain remarkably stable over time, while the translation of technological transitions into enrolment composition occurs with substantial delay. Short-run adjustment appears primarily in early persistence dynamics: attrition reacts sooner than choice, and "growth" in entrants can coexist with declining early survival - producing false winners in which expansion is decoupled from persistence. Macroeconomic volatility amplifies attrition and compresses between-programme differences, masking technological signals that would otherwise be interpreted as preference shifts. To explain why stability dominates responsiveness, we situate these patterns within nationally regulated constraints governing engineering education - minimum total hours and mandated practice intensity - which materially limit the speed of curricular adaptation (Ministerio de Educacion, 2021; Ley de Educacion Superior, 1995). National system metrics further support the plausibility of a high-friction equilibrium in which large inflows coexist with standardised outputs (Secretaria de Politicas Universitarias [SPU], 2022). These findings suggest that apparent rigidity is not an anomaly but the predictable outcome of a system optimised for stability over responsiveness.

Plasma wakefield accelerators (PWFA) represent one of the promising new accelerator concepts that are now being developed intensively for future applications in high-energy physics and industry. Among the unresolved problems of practical implementation of PWFA there are maintaining the required quality (emittance, size, energy spread of bunches) and stable transportation of drive and accelerated (witness) bunches over long acceleration distances. For improving the bunch transport, we propose to fill the bunch transport channel with the background plasma, the density of which is lower than the main plasma density building up an accelerating wake wave. We call this waveguide structure a layered plasma waveguide (LPW). The wakefield excitation by a regular sequence of electron bunches in the LPW of cylindrical configuration has been explored both analytically and numerically. The layered plasma has been modeled as a combination of a tubular plasma and a plasma column of significantly different densities. The plasma column has a lower density. The dispersion dependencies of the TM-modes of the LPW was obtained and analyzed, and it was found that there was a single TM wave resonant with the electron bunch. Based on the obtained analytical expressions, the structures of the axial and radial wakefield amplitudes have been numerically investigated for the cases of a single drive bunch and a regular train of bunches. It is shown that for certain density ratios of the outer and inner plasmas, it is possible both to accelerate and focus simultaneously the drive and witness bunches. The 2.5D particle-in-cell code was used to simulate the witness acceleration of an electron bunch by a wakefield created by drive electron bunches in a two-layer plasma wakefield accelerator. The simulation showed good agreement with the results of analytical calculations.

Phase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To accelerate the simulation of PF models governed by complex PDEs, machine learning methods such as PINNs and ConvLSTM have been introduced. However, current machine-learning-based approaches still suffer from limited flexibility, poor generalization, and short prediction horizons. To address these challenges, we present a joint framework that couples a voxel-flow network (VFN) with PF simulations in an alternating manner for long-horizon prediction of microstructure evolution with substantial computational acceleration. The VFN iteratively predicts future evolution by generating the next snapshot from the previous two snapshots. Periodic PF simulations suppress nonphysical artifacts, reduce accumulated error, and extend the reliable prediction horizon. The VFN was validated using a grain-growth example, and its accuracy outperforms that of similar prediction methods while preserving topological grain details. For an ultra-long grain-growth prediction of 82 frames from 2 input frames, the grain number decreases from 600 to 29 while the NMSE of the average grain area remains 1.64%. The framework also exhibits good generalizability across different PF models. Overall, this joint framework enables rapid, flexible, generalizable, and physically consistent microstructure forecasting from image-based data over ultra-long time scales.

Laser-cooled ions confined in electromagnetic traps provide a unique, tunable mesoscopic system where the interplay of the trapping potential, nonlinear Coulomb interactions, and laser-ion scattering generates rich, collective dynamics. In this work, we engineer thermally activated switching between two oppositely oriented, square-pyramidal configurations of five laser-cooled ions in a Paul trap. For identical ions ($^{40}\mathrm{Ca}^{+}$), the inversions proceed via a Berry pseudo-rotation mechanism with a low activation barrier, enabled by the permutation symmetry, in contrast to the umbrella inversion observed in ammonia. The experimentally measured inversion rates, spanning two orders of magnitude, are accurately captured by the multidimensional Kramers-Langer theory, enabling thermometry of the Doppler-cooled ion cluster at $1.8 \pm 0.1$ mK. By substituting the apex ion with a heavier isotope ($^{44}\mathrm{Ca}^{+}$), we break the permutation symmetry and observe a suppression of thermally activated inversions. Numerical analysis reveals that this symmetry breaking closes the low-barrier channel, forcing the system to invert through a high-barrier turnstile rotation. Thus, we demonstrate a structural analogue of molecular kinetic isotope effects, establishing trapped ions as a versatile platform to explore symmetry-controlled collective dynamics.

In the decay region around the centreline of three qualitatively different turbulent plane wakes, the turbulence is non-homogeneous and two-point turbulent diffusion counteracts the turbulence cascade all the way down to scales smaller than the Taylor length. It is found that the sum of the inter-space transfer rate and the horizontal part of the inter-scale transfer rate of horizontal two-point turbulent kinetic energy is approximately proportional to the turbulence dissipation rate in the inertial range with a constant of proportionality between $-0.6$ and $-1$ depending on wake and location within the wake, except at the near-field edge of the decay region.

In the last thirty years, the Northern and Central Apennines (Italy) have been affected by three main destructive seismic sequences: the 1997 Colfiorito (three events $M_L > 5.5$), the 2009 L'Aquila (one event $M_L > 5.5$), and the 2016–2017 Amatrice–Visso–Norcia (three events $M_L > 5.5$). Several studies have analysed the spatio-temporal evolution and processes driving each sequence, focusing mainly on the foreshock–mainshock–aftershock periods. Here, we focus on the 2018–2024 interseismic phase, aiming to unravel the long-term seismogenic behaviour of this region. We first relocated the earthquake catalogue and identified clusters through a declustering algorithm. During this phase, background seismicity and most clusters were arranged in a 2–3 km thick low-angle layer. We found that (i) most clusters were driven by aseismic processes, (ii) the depth of both clusters and the seismicity layer increased toward the southeast, (iii) the volume of clusters decreased toward the southeast, and (iv) the low-angle layer almost disappeared in the L'Aquila area. Comparing two interseismic phases (2011–2016 and 2018–2024), we found striking similarities, with events occurring at the same sites and characterized by both foreshock–mainshock–aftershock and swarm-like behaviour. In addition, the L'Aquila area was seismically more silent compared to the northern sites during both interseismic phases. We propose that these different long-term seismogenetic behaviours reflect variations in the structure and rheology of the upper crust from the Northern to the Central Apennines. This highlights the important role of structural inheritance in controlling how active deformation affects the interseismic period.

Diffractive neural networks have recently emerged as a promising framework for all-optical computing. However, these networks are typically trained for a single task, limiting their potential adoption in systems requiring multiple functionalities. Existing approaches to achieving multi-task functionality either modify the mechanical configuration of the network per task or use a different illumination wavelength or polarization state for each task. In this work, we propose a new control mechanism, which is based on the illumination's angular spectrum. Specifically, we shape the illumination using an amplitude mask that selectively controls its angular spectrum. We employ different illumination masks for achieving different network functionalities, so that the mask serves as a unique task encoder. Interestingly, we show that effective control can be achieved over a very narrow angular range, within the paraxial regime. We numerically illustrate the proposed approach by training a single diffractive network to perform multiple image-to-image translation tasks. In particular, we demonstrate translating handwritten digits into typeset digits of different values, and translating handwritten English letters into typeset numbers and typeset Greek letters, where the type of the output is determined by the illumination's angular components. As we show, the proposed framework can work under different coherence conditions, and can be combined with existing control strategies, such as different wavelengths. Our results establish the illumination angular spectrum as a powerful degree of freedom for controlling diffractive networks, enabling a scalable and versatile framework for multi-task all-optical computing.

In radiation therapy (RT) treatment planning, multi-criteria optimization (MCO) supports efficient plan selection but is usually solved for population-based dosimetric criteria and ignores patient-specific biological risk, potentially compromising outcomes in high-risk patients. We propose risk-guided MCO, a one-shot method that embeds a clinical risk model into conventional MCO, enabling interactive navigation between dosimetric and biological endpoints. The proposed algorithm uses a special order relation to fuse the classical MCO sandwiching algorithm with bi-level optimization, restricting the Pareto set to plans that achieve improvement in the secondary risk objective for user-defined, acceptable loss in primary clinical objectives. Thus, risk-guided MCO generates risk-optimized counterparts of clinical plans in a single run rather than by sequential or lexicographic planning. To assess the performance, we retrospectively analyzed 19 lung cancer patients treated with RT. The endpoint was the risk of grade 2+ radiation pneumonitis (RP), modeled using bootstrapped stepwise logistics regression with interaction terms, including baseline lung function, smoking history, and dosimetric factors. The risk-guided plans yielded a mean reduction of 8.0% in total lung V20 and 9.5% in right lung V5, translating into an average RP risk reduction of 7.7% (range=0.3%-20.1%), with small changes in target coverage (mean -1.2 D98[%] for CTV) and modest increase in heart dose (mean +1.74 Gy). This study presents the first proof-of-concept for integrating biological risk models directly within multi-criteria RT planning, enabling an interactive balance between established population-wide dose protocols and individualized outcome prediction. Our results demonstrate that the risk-informed MCO can reduce the risk of RP while maintaining target coverage.

In this paper, we investigate the approximate analytical bound states with a linear combination of two diatomic molecule potentials, Yukawa and four parameters potentials, within the framework of the path integral formalism. With the help of an appropriate approximation to evaluate the centrifugal term, the energy spectrum and the normalized wave functions of the bound states are derived from the poles of Green's function and its residues. The partition function and other thermodynamic properties were obtained using the compact form of the energy equation.

We study the two-dimensional semigeostrophic system on the flat torus in the small-amplitude regime. We formulate the rescaled dynamics as the Lie–Poisson flow of a renormalized optimal-transport energy and expand this Hamiltonian in \(C^1\). The leading term is the Euler Hamiltonian, while the first correction is an explicit cubic Monge–Ampère functional. We then derive quantitative consequences for the semigeostrophic–Euler limit: a perturbative scale-uniform endpoint Monge–Ampère estimate under Hessian pinching, an explicit logarithmic perturbative lifespan for the strong branch, fixed-slow-time \(O(\eps)\) velocity convergence for canonically prepared strong branches, a conditional weak–strong rate-transfer corollary, and an \(O(\eps^2)\) Wasserstein comparison for the physical densities.

Stationary solutions of a shell model of turbulence defined on a dyadic tree topology are studied. Each node's amplitude is expressed as the product of amplitude multipliers associated with its ancestors, providing a recursive representation of the cascade process. A geometrical rule governs the tree growth, and we prove the existence of a continuum of fixed points, including the Kolmogorov solution, that sustain a strictly forward energy cascade. Sampling along randomly chosen branches defines a homogeneous Markov chain, enabling a stochastic characterization of extended self-similarity and intermittency through the spectral properties of the associated Feynman-Kac operators. Numerical simulations confirm the theoretical predictions, showing that multi-branch shell models offer a minimal yet physically rich framework for exploring the complexity of nonlinear energy transfer across scales.

We study how syntactic and semantic information is encoded in inner layer representations of Large Language Models (LLMs), focusing on the very large DeepSeek-V3. We find that, by averaging hidden-representation vectors of sentences sharing syntactic structure or meaning, we obtain vectors that capture a significant proportion of the syntactic and semantic information contained in the representations. In particular, subtracting these syntactic and semantic “centroids” from sentence vectors strongly affects their similarity with syntactically and semantically matched sentences, respectively, suggesting that syntax and semantics are, at least partially, linearly encoded. We also find that the cross-layer encoding profiles of syntax and semantics are different, and that the two signals can to some extent be decoupled, suggesting differential encoding of these two types of linguistic information in LLM representations.

We theoretically investigate a non-Hermitian optical dimer whose parameters are renormalized by dispersive and dissipative backaction from the coupling of the passive cavity with a ring-trapped Bose-Einstein condensate. The passive cavity is driven by a two-tone control laser, where each tone is in a coherent superposition of Laguerre-Gaussian beams carrying orbital angular momenta $\pm \ell \hbar$. This imprints an optical lattice on the ring trap, leading to Bragg-diffracted sidemode excitations. Using an exact Schur-complement reduction of the full light-matter dynamics, we derive a frequency-dependent self-energy and identify a static regime in which the atomic response produces a complex shift of the passive optical mode. This renormalized dimer supports a tunable exceptional point, enabling spectroscopic signatures in the optical transmission due to a probe field, which can in turn be utilized for estimating the winding number of the persistent current. Exploiting the associated half-integer topological charge, we propose a digital exceptional-point-based sensing scheme based on eigenmode permutation, providing a noise-resilient method to sense superfluid rotation without relying on fragile eigenvalue splittings. Importantly, the sensing proposals are intrinsically nondestructive, preserving the coherence of the atomic superfluid.

Self-interacting dark matter (SIDM) is a well-motivated extension of cold dark matter that can modify halo structure on galactic and group scales while remaining consistent with large-scale structure. However, practical SIDM work often requires bridging several layers, including microphysical scattering models, velocity-dependent effective cross sections, phenomenological astrophysical constraints, and (separately) data-driven halo fits, such as rotation curves. In this paper, we describe sidmkit, a transparent and reproducible Python package designed to support SIDM “micro$\rightarrow$macro” calculations and to provide a robust batch pipeline for fitting rotation curves in the SPARC data. On the SIDM side, sidmkit implements velocity-dependent momentum-transfer cross sections for a Yukawa interaction using standard analytic approximations (Born, classical, and Hulthén-based) with a numerical partial-wave option for spot checks. It also provides consistent velocity-moment averaging for Maxwellian relative speeds, scattering-rate utilities, and curated literature summary constraints for regression tests and exploratory scans. On the rotation-curve side, we implement bounded non-linear least squares fits of NFW and Burkert halo models to SPARC baryonic decompositions, with optional mass-to-light priors and information-criterion summaries (AIC/BIC). For the demonstration dataset, we process 191 rotmod galaxies (LTG+ETG bundles) and fit both NFW and Burkert models (382 total fits). We find that Burkert is preferred by $\Delta \mathrm{BIC} > 0$ for $65.4\%$ of galaxies, with “strong” preference ($\Delta \mathrm{BIC}>6$) in $32.5\%$ of galaxies;

The efficient interfacing of quantum emitters and photons is fundamental to quantum networking. Quantum defects embedded in integrated nanophotonic circuits are promising for such applications due to the deterministic light-matter interactions of high-cooperativity ($C>1$) cavity quantum electrodynamics and potential for scalable integration with active photonic processing. Silicon-vacancy (SiV) centers embedded in diamond nanophotonic cavities are a leading approach due to their excellent optical and spin coherence, however their long-term scalability is limited by the diamond itself, as its suspended geometry and weak nonlinearity necessitates coupling to a second processing chip. Here we realize the first high-cooperativity coupling of quantum defects to hybrid-integrated nanophotonics in a scalable, planar platform. We integrate more than 600 gallium phosphide (GaP) nanophotonic cavities on a diamond substrate with near-surface SiV centers. We examine a particular device with two strongly coupled SiV centers in detail, confirming above-unity cooperativity via multiple independent measurements. Application of an external magnetic field via a permanent magnet enables optical resolution of the SiV spin transitions from which we determine a spin-relaxation time $T_1>0.4$ ms at 4 K. We utilize the high cooperativity coupling to observe spin-dependent transmission switching and the quantum jumps of the SiV spin via single-shot readout. These results, coupled with GaP's strong nonlinear properties, establish GaP-on-diamond as a scalable planar platform for quantum network applications.

Field geological studies highlighted the heterogeneous structure of fault zones from the meter- to millimeter scale, but such internal variability is not generally resolved by seismological techniques due to spatial resolution limits. The near-surface velocity structure of the Vado di Corno seismogenic fault zone was quantified at different length scales, from laboratory measurements of ultrasonic velocities (few centimeters rock samples, 1 MHz source) to high-resolution first-arrival seismic tomography (spatial resolution to a few meters). The fault zone juxtaposed structural units with contrasting ultrasonic velocities. The fault core cataclastic units were slower compared to damage zone units. A negative correlation between ultrasonic velocity and porosity was observed, with dispersion in fault core units related to varying degree of textural maturity and pore space sealing by calcite. Low-velocity outliers in the damage zone were instead linked to microfracture networks with local cataclasis and partial calcite sealing. P-wave high-resolution seismic tomography imaged distinct fault-bounded rock bodies, matching the geometry and size of field-mapped structural units. At this length scale, relatively fast fault core units and low-strain damage zones contrasted with a very slow intensely fractured high-strain damage zone. The discrepancy between higher ultrasonic velocities and lower tomography-derived ones was reconciled through an effective medium approach considering the effect of meso-scale fractures in each unit. This revealed a heterogeneous fault zone velocity structure with different scaling among structural units. Lastly, the persistence of a thick compliant high-strain damage zone at shallow depth may significantly affect fault zone mechanics and the distribution of near-surface deformations.

The deep sea is weakly stratified in density but shows considerable variations in turbulent motions in all three directions. When registered by moored high-resolution temperature 'T'-sensors, the motions cause variations of 0.01degrC or less and in time of minutes or less, which is much faster than hours or longer of internal waves. Occasionally, T-sensors close to the seafloor register minute-long flashes of 0.0005-0.001degrC warmer than the environment. When singular, such flashes may be artefacts. However, in a large mooring-array with 45 vertical lines at 9.5-m horizontal distances, near-seafloor heat flashes are seen to travel, most likely with internal-wave instabilities in overlying stratified waters. The instabilities seem to release the flashes from a geothermally heated seafloor of which turbulence convection is suppressed by warmer waters from above. The forms and turbulence intensity of these rare signals are compared with those induced by a Remotely Operated Vehicle working near the array. Other causes like unidentified marine mammal passing are hypothesized.

Reanalyses such as ERA5 have long been foundational for weather and climate science. They have also found a new use case, as training and verification data for machine-learnt weather prediction (MLWP) models. Here we compare short-lead time (6h) forecasts from the MLWP model GraphCast against ERA5. In doing so, we identify a recurrent, spatially coherent error in 2m Temperature centred on the Ethiopian Highlands, that occurs predominantly at 0600 UTC. We show that these error events are not an error in the forecast from GraphCast, but are in fact an error in ERA5, and are also present in the ECMWF operational analysis. They arise from the 2D optimal interpolation procedure, when surface reports are assimilated that are temporally displaced compared to the background forecast. This produces spuriously warm analysis increments over Ethiopia on approximately 7% of dates at 0600 UTC across the reanalysis record. The spread from the ensemble of data assimilation partially flags these cases but is underdispersive. We assess the impact on GraphCast, which was trained on ERA5. While GraphCast can largely ignore these unphysical error events, a small systematic degradation in forecast skill over the region is observed. We discuss implications for using reanalysis as truth in machine learning training and verification, and recommend simple changes to reduce such artefacts in future analyses.

Very weakly density-stratified, near-homogeneous 'NH' conditions are found in the deep Western Mediterranean Sea. Under these conditions, over vertical ranges of several hundreds of meters water temperature varies only a few 0.0001degrC and the buoyancy frequency is smaller than the local inertial frequency. While such waters are characterized as 'quiescent', they are not stagnant and demonstrate regular bursts of turbulent overturns across scales larger than 10 m that are relevant for deep-sea life. As will be shown from a 3D mooring-array with nearly 3000 high-resolution temperature 'T-'sensors, consecutive NH conditions can last up to a fortnight, before stratified waters are advected over the array. At the site, NH conditions occur about 60% of the time. The majority of NH periods is governed by convection turbulence that is driven by geothermal heating from below. The associated turbulence dissipation rate, which is calculated from Ellison scales after precise band-pass filtering, compares with historic geophysical heat-flux measurements. Convection turbulence leads to buoyancy-driven scaling of spectra, not only of temperature in the turbulence range, but also suggesting extensions across the internal-wave band into sub-mesoscales, and (limited observations of) kinetic energy and waterflow differences. Such spectra are found to be uniform over the 124-m vertical T-sensor range above the flat seafloor. Small spectral deviations are observed when very weakly stratified waters are advected sideways or from above, whereby turbulence levels increase by about 30%. Movies show the alternation between calm periods, turbulent clouds passing, and geothermal-heat flares of various sizes.

Vertically stable in density, stratified-water conditions 'SW' exist in the deep Mediterranean Sea that are characterized by temperature differences of 0.0002-0.01degrC over 125 m above a flat seafloor. These result in a mean buoyancy frequency of N = (1.5-2)f, where f denotes the inertial frequency. Although the stability values are one order of magnitude smaller than found in the ocean, they govern a dynamical deep sea as demonstrated using observations from a 3D mooring-array equipped with nearly 3000 high-resolution temperature sensors. SW-conditions can last up to a fortnight, before waters become near-homogeneous, and occur about 40% of the time, slightly more often in winter than in summer. Under SW, up to 60 m above seafloor is dominated by convection turbulence that is partially driven by geothermal heating 'GH' suppressed by stratification above. The upper-half of the array shows dominant shear turbulence driven by two sources. Interfacial internal waves generate weakly-nonlinear, resonant parametric instabilities that, upon breaking, provide mean turbulence dissipation rates of about one-third of that via general GH. It is about equal to open-ocean values away from boundaries and may represent the dominant source of turbulence there. Like GH, the observed turbulence is local up- and down-going. Tenfold larger mean dissipation rates are observed when slanted convection drives turbulent overturns >10 m and unstable clouds are advected with the mean flow. It confirms theoretical marginal stability analyses, previous vertical waterflow observations, and suggests a relationship between turbulence and sub-mesoscale eddies across the internal wave band. Movies support the findings.

Controlled and precise fabrication of structures with heights in the range of single digit nanometres is one of the challenges for diffraction gratings operating near-normal incidence in the extreme ultraviolet (EUV) and soft X-ray range. Here, we expand on previous research utilizing swelling of silicon after irradiation with ions as alternative to conventional dry etching. By irradiating silicon through a mask with a broad beam of nitrogen ions, we realized lamellar gratings in a precise and well controlled process. We were able to fabricate gratings with structure heights between (1.00 +/- 0.05) nm to (10.0 +/- 0.5) nm and a pitch of 1 micrometre, which is suitable for both EUV and soft X-ray applications. A variation of ion energy from 20 keV to 40 keV further expands the foundations of this process and yielded an additional parameter to control the resulting structure height and shape.

We report the first experimental study of the influence of a dielectric surface on the transmission spectrum of cesium atoms for the $6S_{1/2} \rightarrow 7P_{3/2}$ ($D_2$) transition in vapor cells with thicknesses in the range $50$-$250\,$nm. The measurements were performed using a homemade optical nanocell filled with atomic cesium and featuring a wedge-shaped gap between the inner surfaces of sapphire windows. For atom-surface distances below approximately $300$\,nm, a significant red shift of the atomic transition frequency is observed due to van der Waals (vdW) interactions with the dielectric surface. An additional red shift arises at high vapor pressures owing to Cs-Cs interactions, with a measured contribution of $15$\,MHz/Torr, which must be accounted for in order to correctly determine the vdW coefficient $C_3$. By recording transmission spectra of the nanocell at different thicknesses, we determine for the first time the vdW coefficient for the Cs $6S_{1/2} \rightarrow 7P_{3/2}$ transition, obtaining values in the range $C_3 \sim 2- 20$\,kHz\,$\mu$m$^3$. These results are of interest for fundamental studies of atom-surface interactions and are also relevant for the development of miniature sensors based on atomic vapors, in particular compact frequency references exploiting atomic transitions in the blue spectral region.

We use classical computational methods to estimate the ground-state energy to chemical accuracy in a model of the FeMo-cofactor of nitrogenase which is widely studied as a target of quantum computing. Our result relies on the insight that the ground-state problem can be characterized as one of ranking many competing, but largely simple, states. This allows a combination of systematic high-order coupled cluster and density matrix renormalization group calculations together with an extrapolation protocol to obtain an accurate energy. Within the model we identify several spin isomer candidates for the ground-state that are degenerate to chemical accuracy. Beyond this model, we characterize the impact of additional electronic excitations and the cluster and protein geometric fluctuations on the low-lying electronic landscape. We find that many features of the landscape are retained in more detailed representations of nitrogenase, which points to the complexity of spectroscopic interpretations of the electronic structure of the FeMo-cofactor.