Papers

When passing through an undulator in a Free Electron Laser, dense bunches of relativistic electrons split into micro-bunches, attaining a periodic space-time structure. We show that the field of such periodically modulated bunches is tremendously influenced by coherence effects, resulting in a novel type of beam-atom interaction. Our results indicate that employing such bunches (alone or in combinations with the radiation they emit) offers a multitude of new opportunities for exploring atomic dynamics on a femtosecond time scale.

Exoplanet atmospheres are usually discussed as tracers of climate, chemistry, and habitability, but they may also preserve signatures of planetary defense. We consider three folklore-motivated deterrents against monsters: reduced organosulfur gases as anti-hematophage repellents, argentiferous reflective aerosols as anti-lycanthropic countermeasures, and haline aerosols as a counting problem for specters. We show that globally-mixed garlic-smelly levels of DMS/DMDS could produce observable mid-infrared transmission features, that silver hazes would show up as anomalous optical brightening, and that sea-salt lofting sustained by strong near-surface winds appears as muted spectra. None of these signatures is unique, which is precisely the observational challenge. A defended world may first appear merely sulfur-rich, bright, or hazy. Therefore, some atmospheres may encode not only biosignatures, but also evidence that the local biosphere has stopped being afraid of the dark.

We introduce a numerical tensor-network method to compute the statistics of the charge transferred across an interface partitioning an interacting one-dimensional many-body lattice system with $U(1)$ symmetry. Our approach is based on a matrix-product state representation of the process tensor (also known as influence functional or influence matrix) describing the effect of the bulk system on the degrees of freedom at the interface, allowing us to evaluate a multi-time correlation function that yields the moment-generating function of charge transfer. We develop a scheme to truncate non-Markovian correlations which preserves the proper normalization of the process tensor and ensures the correct physical properties of the generating function. We benchmark our approach by simulating magnetization transport within the Heisenberg spin-$1/2$ XXZ brickwork circuit model at infinite temperature. Our results recover the correct transport exponent describing ballistic, superdiffusive, and diffusive transport in different regimes of the model. We also demonstrate anomalous transport encoded by a self-similar scaling form of the moment-generating function outside of the ballistic regime. In particular, we confirm the breakdown of Kardar-Parisi-Zhang universality in higher-order transport cumulants at the isotropic point. Our work paves the way for process-tensor descriptions of non-Markovian open quantum systems to address current fluctuations in strongly interacting systems far from equilibrium.

We introduce a field-wide benchmark challenge for machine learning in Reynolds-averaged Navier-Stokes (RANS) turbulence modelling. Though open-source datasets exist for training data-driven turbulence closure models, the field has been notably lacking a standard benchmark metric and test dataset. The Closure Challenge is a curated collection of open-source datasets and evaluation code that remedies this problem. We provide a variety of high-fidelity training data in a standardized format, including mean velocity gradients. The test cases (periodic hills, square duct, and NASA wall-mounted hump) evaluate Reynolds number and geometry generalization, two key issues in the field. We present results from three early submissions to the challenge. This is an ongoing challenge, intended to continuously spur innovation in machine learning for turbulence modelling. Our goal is for this benchmark to become the standard evaluation for new machine learning frameworks in RANS. The Closure Challenge is available at https://github.com/rmcconke/closure-challenge-benchmark.

The search for habitable planets, and even for “Earth 2.0”, is a major driver in contemporary astronomy. However selecting target fields to prioritise for such searches presents a challenge. Here we establish a statistical analysis of the appearance of constellation names in science fiction magazines of the pulp era, evaluating the most commonly mentioned constellations and thus those which the science fiction community collectively identify as the most likely locations to find life. Given that the predictive power of science fiction is well established, we suggest that these locations might be prioritised by searches for extrasolar biospheres.

Liquid scintillators underpin a wide range of radiation detectors, including those used in neutrino physics, but typically rely on organic fluors dissolved in hazardous and costly solvents. Here, we show that carbon dots - nanoscale fluorescent carbon materials - synthesised from simple household ingredients using a microwave can function as water-based liquid scintillators. These carbon dots dispersed in water produce light yields up to 70 +/- 20 photons per MeV and enable the detection of atmospheric muons. This yield is sufficient to detect low-energy protons in water Cherenkov neutrino detectors, expanding their programs in both particle physics and astrophysics. These results establish an accessible, low-cost and environmentally benign route to scintillator development, opening new opportunities for large-scale radiation detection.

We present BEST (Boltzmann Equation Solver for Thermalization), a Python framework for solving the momentum-resolved Boltzmann equation for arbitrary $n_{\rm in} \to n_{\rm out}$ scattering processes. The collision integral is evaluated directly in $3(n_{\rm total}-2)$ dimensions using the VEGAS adaptive Monte Carlo algorithm with vectorized batch evaluation. Momentum conservation is enforced exactly by expressing one particle's momentum through the constraint, while energy conservation is imposed via a narrow Gaussian representation of the delta function. We identify a subtlety in the construction of the collision integral for processes with unequal initial and final multiplicities ($n_{\rm in} \neq n_{\rm out}$) involving identical particles: the full collision rate requires separate evaluation with the observed momentum pinned to each side of the reaction, weighted by the respective particle multiplicities. Failure to account for this leads to systematic violation of energy conservation. The code supports massive particles with time-dependent masses, Bose-Einstein and Fermi-Dirac quantum statistics, multiple coupled species, cosmological expansion with comoving momenta, and both Euler and Heun time integration. Parallelization is achieved by distributing independent momentum grid points across MPI ranks, yielding near-linear scaling to hundreds of cores. We validate the Monte Carlo results against a semi-analytical $2 \to 2$ collision integral with exact energy conservation, following the phase-space reduction of Ala-Mattinen et al. As a demonstration, we study thermalization of a massive scalar field through a $2 \leftrightarrow 3$ number-changing process and show that energy conservation is restored only when all identical-particle contributions are correctly summed. The code is publicly available at https://github.com/best-hep/best.

Modern numerical models are increasingly complex, opaque, and computationally expensive, yet frequently fail to predict even qualitative features of observed phenomena. We propose a new paradigm, Declarative Bespoke Modelling, in which the modeller explicitly declares the relationship between model inputs and outputs. We demonstrate that this approach achieves perfect predictive accuracy, unconditional numerical stability, and complete interpretability. It represents a natural endpoint of contemporary modelling practice and near-zero CO2 emission.

While chiral perturbation theory for mesons is characterized by a momentum expansion in $Q/\Lambda_\chi$ with $\Lambda_\chi \sim 1$ GeV, existing formulations of effective theory for nucleon-nucleon scattering deviate from data at $Q\sim 300$ MeV or lower. We offer heuristic evidence that unsuspected nonanalytic structure exists in the complex momentum plane obstructing the effective field theory expansion in the spin-triplet channels, associated with the peak of the angular momentum barrier whose energy in low partial waves satisfies $k=\sqrt{ME} \sim 300$ MeV. With this motivation, we construct a meromorphic function of $k^2$ we call the $C$-matrix, for which the radius of convergence of its Taylor expansion in $k^2$ is equivalent to that of the momentum expansion of the effective field theory. Thus the range of validity of the effective theory is directly related to the pole structure of the $C$-matrix. We uncover that pole structure and confirm that it is the source of the obstruction. The systematic inclusion of dimer fields as propagating degrees of freedom in the effective theory to account for those poles results in cut-off insensitive fits at order $Q^0$ to most of the lower partial wave phase shifts up to the pion production threshold, using only the one pion exchange part of the long-range nucleon-nucleon interaction. Our theory should be applicable to the singular potentials regularly found in atomic physics as well.

Nine impact craters on Mercury bear the names of Persian-Tajik poets: Rudaki, Saadi, Nizami, Rumi, Navoi, Firdousi, Hafiz, Sanai, and Mahsati. We compile IAU-approved coordinates, diameters, quadrant designations, approval dates (1976–2025), and MESSENGER-derived geological characteristics for each crater, and place these data in the broader context of Solar System objects associated with Tajik-Persian civilisation, including lunar craters (Al-Biruni, Avicenna, Omar Khayyam, Abul Wafa, Nasireddin, Alfraganus, Azophi, Al-Khwarizmi), main-belt asteroids, and Enceladus surface features named from the One Thousand and One Nights. The approval timeline mirrors the successive stages of Mercury mapping: two craters were designated after Mariner 10 (1976), three more following Mariner 10 data analysis (1979, 1985), two after the first MESSENGER flybys (2008, 2010), two after MESSENGER orbital operations (2014), and one in 2025. Geological diversity is notable: the Sanai basin (490 km, ~3.8–3.9 Ga) is among the oldest large structures on Mercury; Firdousi shows lava-flooded floor morphology; Rumi hosts an explosive pyroclastic vent intersected by Palmer Rupes; Hafiz contains low-reflectance material and hollows; Navoi is a pit-floor crater; and Nizami exhibits polygonal wall geometry. These results illustrate how planetary nomenclature serves as a stable, internationally maintained record of cultural heritage.

Resistive Plate Chambers (RPCs) are widely used as tracking detectors in many high-energy physics experiments. It has been observed that low-resistive bakelite RPC prototypes frequently exhibit a secondary hit component, appearing as a long tail or an additional peak in the time-correlation spectra relative to the trigger detector. These secondary hits, which affect both the time and spatial resolution, are difficult to distinguish from genuine signals in high-rate environments without an external trigger. As a result, they can significantly degrade track reconstruction efficiency and increase processing time. We present a machine-learning-based strategy to separate signal and background hit clusters using fifteen cluster-level descriptors that encode both statistical properties (histogram mean, width, cluster size) and fit-based parameters (Gaussian-fit mean, width, amplitude, chi^2, NDF) of the time and ADC distributions. Using laboratory data collected from a single-gap low resistive RPC with a three-scintillator master trigger, we trained and evaluated three classifiers-DNN, 1D-CNN, and XGBoost-on balanced signal/background samples. All models demonstrate strong discrimination capability, with XGBoost showing the most robust generalization performance. Feature-importance analysis indicates that cluster size and temporal-shape descriptors are the dominant discriminants. These results highlight that compact, interpretable cluster-level features combined with machine-learning classifiers offer a practical and effective approach to suppress background in self-triggering low resistive RPC detectors.

Polymorphism in organic semiconductors is conventionally framed as equilibrium thermodynamic selection, yet atmospheric-pressure vapor deposition routinely produces metastable phases that defy classical nucleation theory. We develop a symmetry-resolved open quantum system formulation of quantum dissipative assembly (QDA), in which the fundamental assembly unit is a vibronic wavepacket whose internal degrees of freedom are classified by the irreducible representations of the molecular point group. The carrier-gas environment acts as a structured dissipative bath with irrep-resolved spectral densities, and polymorph selection corresponds to relaxation into a symmetry-resolved maximum-transmittance attractor (MTA) rooted in quantum scattering theory and impedance matching. Guided by this theory, we tune the carrier-gas dissipative environment via reactor geometry, flow velocity, and precursor concentration to selectively synthesize a previously unreported polar polymorph of copper phthalocyanine, omega-CuPc, crystallizing in space group P2 with a dimerized bilayer superstructure and an extreme Davydov splitting of 154 nm. Further structural refinement with a 4-molecule modulated supercell model resolves the majority of discrepancies between powder X-ray diffraction and energy minimization, revealing secondary layer orientation modulation as a higher-order dissipative optimization product. The framework consistently explains the formation windows of the eta, alpha, and beta polymorphs, their distinct morphologies, and the marked difference in crystalline order between open-shell CuPc and closed-shell NiPc. Our findings establish a symmetry-guided, environment-controlled polymorph engineering strategy rooted in QDA, where the carrier-gas atmosphere serves as an active dissipative medium shaping the symmetry-resolved dissipative landscape rather than acting as an inert thermal bath.

This paper presents the principle scheme of the prototype of the fast single channel reactor operating in the traveling wave fission mode on the soft fast neutron spectrum. The reactor design has cylindrical symmetry. The problem of the radiation resistance construction materials in this experimental reactor is solved by the base proposals published by the authors of this article. The traveling wave fission mode is realised by the neutrons with the softened fast spectrum. The neutron spectrum peaks is in range 20000 - 50000 eV. That reduces the neutron impact on construction materials more than an order of magnitude. To further reducing neutron impact on construction materials in the reactor using elements of the hydravlic fuel handling system for nuclear fuel movement relative to the fuel channel walls. The nuclear fuel in which the fission wave travels is the cylindrical uranium dicarbide.

We estimate the synchrotron radiation of cosmic muons in a uniform magnetic field in the $\mu$eV-to-meV energy scale. Such events can potentially bring backgrounds to the axion dark matter searches. The GEANT4 software package is utilized to simulate the muon tracks in a cylindrical region of interest with an 8~T solenoid magnetic field applied. We further develop an analytical estimation of the angular-frequency-differential synchrotron radiation power spectra in this work as the cosmic muons span a wide range of Lorentz factor $\gamma$ and pitch angle $\alpha$. We verify that the cosmic muons are not the dominant noise background for the current axion dark matter experiments on the $\mu$eV scale because of the high quality factor $Q$ and fine energy resolution in the readout. However, without sufficient energy resolution in the detector readout, future broadband axion dark matter experiments will be vulnerable to the synchrotron radiation of these charged particles.

We report on a universal topological dichroism of chiral three-dimensional systems in response to the chirality of light. We show that chiral topological invariants result in integer-quantized dichroic excitation rate differences. Moreover, we demonstrate that such topological effects arise more generally from coupling optical chirality to higher tensor Berry curvatures and Dixmier-Douady invariants of quantum states, including Hopf indices. We finally propose an experimental setup that leverages superchiral light as a smoking-gun probe of chiral band topologies in three-dimensional materials. Our findings establish an optical route for probing to date unobserved chiral electronic band topologies.

The number of spatial degrees of freedom (NDoF) and channel strength in antenna systems are examined within a geometric framework. Starting from a correlation-operator representation of the channel between transmitter and receiver regions, we analyze the associated eigenspectrum and relate the NDoF to its spectral transition (corner). We compare the spectrum-based effective NDoF and effective rank metrics, clarifying their behavior for both idealized and realistic eigenvalue distributions. In parallel, we develop geometry-based asymptotic estimates in terms of mutual shadow (view) measures and coupling strength. Specifically, we show that while the projected length or area predicts the number of usable modes in two- and three-dimensional settings, the coupling strength determines the average eigenvalue level. Canonical configurations of parallel lines and regions are used to derive closed-form asymptotic expressions for the effective NDoF, revealing significant deviations from the spectral corner in closely spaced configurations. The results illustrate that these are physically grounded. The proposed theory and techniques are computationally efficient and form a toolbox for estimating the modal richness in near-field channels, with implications for array design, inverse problems, and high-capacity communication systems.

Multicomponent gel systems have garnered much interest due to their compelling mechanical properties in the past decade. Yet, some mechanisms associated with multicomponent gels, such as sequential gelation, have been explored primarily in the context of chemical nonreversible polymeric and protein gels than in physical reversible colloidal ones. In this study, we use mesoscale simulation techniques to model the sequential gelation of two-component colloidal systems whose components' interspecies and intraspecies electrostatic interactions can be modified independently. We show that by simply leveraging temporal control and interspecies interactions, we can construct markedly different networks; from double networks to mixed and core-shell composite structures of varying coarseness and heterogeneity natures. These findings present a compelling case for further exploration of multicomponent colloidal systems.

The scalability of time-dependent partial differential equation (PDE) solvers based on the discontinuous Galerkin (DG) method is increasingly limited by data communication and synchronization requirements across processing elements (PEs) at extreme scales. To address these challenges, asynchronous computing approaches that relax communication and synchronization at a mathematical level have been proposed. In particular, the asynchronous discontinuous Galerkin (ADG) method with asynchrony-tolerant (AT) fluxes has recently been shown to recover high-order accuracy under relaxed communication, supported by detailed analyses of its accuracy and stability. However, the scalability of this approach in modern large-scale parallel DG solvers has not yet been systematically investigated. In this paper, we address this gap by implementing the ADG method coupled with AT fluxes in the open-source finite element library deal.II. We employ a communication-avoiding algorithm (CAA) that reduces the frequency of inter-process communication while accommodating controlled delays in ghost value exchanges. We first demonstrate that applying standard numerical fluxes in this asynchronous setting degrades the solution to first-order accuracy, irrespective of the polynomial degree. By incorporating AT fluxes that utilize data from multiple previous time levels, we successfully recover the formal high-order accuracy of the DG discretization. The accuracy of the proposed method is rigorously verified using benchmark problems for the compressible Euler equations. Furthermore, we evaluate the performance of the method through extensive strong-scaling studies for both two- and three-dimensional test cases. Our results indicate that CAA substantially suppresses synchronization overheads, yielding speedups of up to 1.9x in two dimensions and 1.6x in three dimensions compared to a baseline synchronous DG solver.

Fully implicit tensor-product space-time discretizations of time-dependent $(p,\delta)$-Navier-Stokes models yield, on each time step, large nonlinear monolithic saddle-point systems. In the shear-thinning regime $1<p<2$, especially as $p\downarrow 1$ and $\delta\downarrow 0$, the decisive difficulty is the constitutive tangent: its ill-conditioning impairs Newton globalization and the preconditioning of the arising linear systems. We therefore develop a scalable monolithic modified Newton framework for tensor-product space-time finite elements in which the exact constitutive tangent in the Jacobian action is replaced by a better-conditioned surrogate. Picard and exact Newton serve as reference linearizations within the same algebraic framework. Scalability is achieved through matrix-free operator evaluation, a monolithic multigrid V-cycle preconditioner, order-preserving reduced Gauss-Radau time quadrature, and an inexact space-time Vanka smoother with single-time-point coefficient freezing in local patch matrices. We prove coercivity of the linearized viscous-Nitsche term in the uniformly elliptic regime $\nu_\infty>0$ and consistency of the reduced time quadrature. Numerical tests demonstrate robustness with respect to model parameters, nonlinear and linear iteration counts, and scalable parallel performance.

Understanding how fast atmospheric variability shapes slow climate variability and sensitivity remains a central challenge in Earth-system science. Recent advances in machine-learned (ML) atmospheric models have demonstrated remarkable skill on weather timescales, but their emergent behaviour in a fully coupled climate system remains largely unexplored. We present early results from a new hybrid modelling framework, in which the ACE2 ML atmospheric emulator is interactively coupled to the NEMO ocean model. We report on a set of 70-year coupled simulations (1950-2020 historical forcing and fixed-1950s control). These experiments represent, to our knowledge, one of the first multi-decadal integrations of a machine-learned atmosphere interacting with a full-depth dynamical ocean. Several historical and fixed-1950s control simulations from the fully dynamic global coupled climate model EC-Earth, which has the same ocean component used in ACE2-NEMO, are also considered for comparison. We assess the behaviour of the coupled system, with particular focus on low-frequency tropical variability and the climate response to greenhouse-gas forcing. Analysis of potentially emergent El Nino-like variability reveals realistic fast timescale air-sea coupling in the tropical Pacific, but the temporal variability is unrealistic, with very low amplitude oscillations; this appears to be due to weak atmospheric feedback in the tropical Pacific. The response to CO2 forcing shows initial agreement with EC-Earth3P but deviates due to reduced downward short-wave radiation in ACE2. These results provide a unique test of physical realism and generalisability for atmospheric emulators and evaluate the possible role of entirely machine-learned components in next-generation Earth system models.

Recent work by Vavrek et al. (2025) showed that machine learning methods can be used to exploit spatial patterns of performance variations within the highly-segmented H3D M400 gamma spectrometer to improve an overall spectroscopic performance metric. That work also introduced the spectre-ml software, which tests various greedy, heuristic, random, and machine learning clustering algorithms to find the best performing mask for excluding detector regions to improve a user-defined performance metric by training on a given dataset. In this work, we build off of Vavrek et al. (2025) and seek to determine to what extent an optimized binary voxel mask trained on a given dataset can generalize to other datasets. In particular, this paper evaluates the transferability of masks trained on one M400 dataset to another M400 detector, in order to determine whether the total effort required in designing masks for different detectors and applications can be substantially reduced by using a single common mask. It also examines testing and training on different subsets of the same dataset to determine the natural level of variability in optimization results. In the inter-detector analysis, as expected, the best performing model on each detector is often one trained on that dataset, with an average performance enhancement of $16\%$ when considering the relative uncertainty in a Doniach fit to the $186$ keV peak. In comparison, the best transferred masks, with the best on average performance metric across all six detectors, show only a slightly smaller improvement of $13\%$ on average. These results suggest that high-performing, well-transferable masks can be shared among detectors, reducing or even eliminating the laborious processes of collecting a training dataset and performing the optimization for each detector, ultimately improving safeguards efficiency.

We study Gaussian wave packet solutions for Maxwell's equations in an isotropic, inhomogeneous medium and derive a system of ordinary differential equations that captures the leading-order correction to geodesic motion. The dynamical quantities in this system are the energy centroid, the linear and angular momentum, and the quadrupole moment. Furthermore, the system is closed to first order in the inverse frequency. As an immediate consequence, the energy centroids of Gaussian wave packets with opposite circular polarisations generally propagate in different directions, thereby providing a mathematical proof of the spin Hall effect of light in an inhomogeneous medium.

We present a fully automated framework for extracting interatomic force constants (IFCs) directly from X-ray thermal diffuse scattering (TDS) data. By formulating scattering intensity as a differentiable function of a symmetry-reduced IFC parameterization, we enable gradient-based optimization via direct, Cholesky-based sampling of correlated atomic displacements at thermal equilibrium. This approach bypasses the computational bottleneck of repeated Hessian matrix diagonalizations, significantly accelerating the inversion process. Benchmark tests demonstrate that the framework accurately recovers ground-truth IFCs and phonon dispersion relations, providing a robust, high-throughput pathway for studying lattice dynamics across diverse crystalline materials. This method bridges the gap between experimental observations and computational modeling, enabling the direct integration of TDS data into the refinement of high-fidelity inter-atomic potentials.

Dispersion engineering via geometric confinement is essential to integrated photonics, enabling phenomena such as soliton microcombs, supercontinua, parametric oscillators, and entangled photons. However, prevailing methodologies rely on semi-empirical Sellmeier models that assume idealized material purity, neglecting the pronounced dispersion shifts induced by residual impurities like hydrogen-related bonds. Here, we demonstrate that these residual bonds fundamentally alter the dispersion landscape spanning from the ultraviolet (UV) to the mid-infrared (MIR) spectra. Specifically, they introduce MIR vibrational absorption while simultaneously modifying UV electronic transition, shifting the bandgap and UV pole. We show that the spectral tension between these UV and MIR modifications dictates the group velocity dispersion from the visible to the near-infrared (NIR) via the Kramers-Kronig causality. We experimentally validate this phenomenon through systematic characterization of broadband loss and dispersion in ultralow-loss silicon nitride photonic integrated circuits. By rigorously incorporating these effects, we bridge the gap between empirical fitting and predictive physical modelling. Our study resolves long-standing discrepancies in dispersion engineering, providing precision control essential for next-generation integrated photonics.

Coarse-grained models of chaotic systems neglect unresolved degrees of freedom, inducing structured model error that limits predictability and distorts long-term statistics. Typical data-driven closures are trained to minimize error over a single time step, implicitly assuming Markovian dynamics and often failing to capture long-term behavior. Recent approaches instead optimize losses over finite trajectories. However, when such trajectory-based training is carried out with deterministic pointwise losses, it introduces a fundamental mathematical degeneracy. We prove that optimizing pointwise deterministic losses such as mean squared error over chaotic trajectories suppresses predictive variance, with corresponding loss of physical variability in long integrations. In contrast, strictly proper scoring rules avoid this degeneracy. By targeting forecast distributions rather than realized trajectories, they remove the penalty against predictive spread and align the long-lead optimum with the invariant measure. Using quasi-geostrophic turbulence as a canonical chaotic system, we validate this theory: one-step-trained closures fail to capture stable coarse-grained dynamics, while deterministic closures optimized over trajectories exhibit the variance-loss tendency predicted by our analysis. Stochastic closures calibrated over trajectories using the energy score, however, overcome both structural limitations, yielding skillful ensemble forecasts and realistic long-term statistics. Our results establish that both stochastic modeling and trajectory-based calibration are essential for faithfully representing the dynamics of coarse-grained systems.

In light of major developments over the past decades in both quantum computing and simulations on classical hardware, it is a serious challenge to identify a real-world problem where quantum advantage is expected to appear. In quantum chemistry, electronic structure calculations of strongly correlated, i.e. multi-reference problems, are often argued to fall into such category because of their intractability with standard methods based on mean-field theory. Therefore, providing state-of-the-art benchmark data by classical algorithms is necessary to make a decisive conclusion when such competing development directions are compared. We report cutting-edge performance results together with high accuracy ground state energy for the Fe$_4$S$_4$ molecular cluster on a CAS(54,36) model space, a problem that has been included quite recently among the list of systems in the Quantum Advantage Tracker webpage maintained by IBM and RIKEN. Pushing the limits even further, we also present CAS-SCF based orbital optimizations for unprecedented CAS sizes of up to 89 electrons in 102 orbitals [CAS(89,102)] for the Fe$_5$S$_{12}$H$_4^{5-}$ molecular system comprising twenty five open shell orbitals in its sextet ground state and an active spaces size of 331 electrons in 451 orbitals. We have achieved our results via mixed-precision spin-adapted ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations interfaced with the ORCA program package and utilizing the NVIDIA Blackwell graphics processing unit (GPU) platform. We argue that DMRG benchmark data should be taken as a classical reference when quantum advantage is reported. In addition, full exploitation of classical hardware should also be considered since even the most advanced DMRG implementations are still in a premature stage regarding utilization of all the benefits of GPU technology.

We describe a magnetically driven torsional oscillator that is well suited for teaching the physics of simple harmonic motion using a collection of hands-on, quantitative experiments. The mechanical Q of the system can be tuned using eddy-current damping, while optical read-outs provide electronic signals than can be recorded using nothing more than a basic digital oscilloscope. The 40-Hz oscillator is described by simple harmonic motion to high accuracy, providing many satisfying comparisons between theory and experiment.

The magnetic field topology undergoes a drastic transition near the separatrix in magnetic confinement fusion devices, inducing topological chaos. This phenomenon is investigated across tokamaks, stellarators, and the RFX-mod reversed-field pinch. Within this boundary region, self-optimized fluxes of shear-generated magnetic and cross-helicity, together with the ensemble-averaged magnetic moment, are hypothesized to govern plasma stability, self-organization, and transport. By treating the averaged magnetic moment as an active scalar and using the Fokker-Planck equation to reconcile kinetic and fluid descriptions, this study couples its dynamics with a novel multichannel, self-optimized helicity cascade loop. The derived spectral laws for the floating potential and ion saturation current agree with measurements from numerous experiments. Distributed chaos theory quantifies the randomness of the observed states, revealing that a narrow layer at the separatrix suppresses strong core randomness, transforming it into distributed chaos in the scrape-off layer. A constrained variational principle for active control has also been considered. Tuning Lagrangian multipliers via helicity injection drives the system toward an attractor state that stabilizes the separatrix shear layer, suppresses edge-localized modes, and spreads the heat load, using magnetic reconnection as a nonlinear solver. This framework circumvents conventional diffusive limitations, providing an analytical foundation to model heat-load patterns and stochastic ripple loss in next-step devices.

Highly coherent lasers are central to modern photonics. To date, high-coherence operation has been achieved predominantly in microcavity and fiber-based platforms. More recently, free-space Brillouin-laser experiments have revealed unusually strong noise suppression whose physical origin cannot be explained by conventional continuous-medium models developed for those platforms. In conventional continuous-medium models, the optical and acoustic fields are assumed to remain continuously coupled throughout the cavity evolution, whereas in free-space implementations the coupling is confined to the nonlinear medium and interrupted by passive propagation over the rest of the round trip. To describe this interaction-propagation separation, we develop a discrete-cavity model in which the short Brillouin interaction inside the gain medium and the subsequent free-space propagation are treated as two separate stages of the round-trip evolution. This separation introduces a temporal asymmetry between optical storage and acoustic relaxation, which effectively enhances acoustic damping at the cavity level and strongly reduces pump-noise transfer to the Stokes field. If the cavity round-trip time is much longer than the interaction time in the nonlinear medium, the noise-suppression ratio scales with the ratio of the total cavity length to the nonlinear-medium length. Our discrete-cavity model further provides quantitative predictions for the lasing threshold, output power, phase-noise transfer, and fundamental linewidth, in good agreement with experiment. These results identify the discrete interaction-propagation structure as the physical origin of the unusually strong noise suppression in free-space Brillouin lasers systems.

This paper introduces a novel physics-informed impact identification (Phy-ID) framework. The proposed method integrates observational, inductive, and learning biases to combine physical knowledge with data-driven inference in a unified modelling strategy, achieving physically consistent and numerically stable impact identification. The physics-informed approach structures the input space using physics-based energy indicators, constrains admissible solutions via architectural design, and enforces governing relations via hybrid loss formulations. Together, these mechanisms limit non-physical solutions and stabilise inference under degraded measurement conditions. A disjoint inference formulation is used as a representative use case to demonstrate the framework capabilities, in which impact velocity and impactor mass are inferred through decoupled surrogate models, and impact energy is computed by enforcing kinetic energy consistency. Experimental evaluations show mean absolute percentage errors below 8% for inferred impact velocity and impactor mass and below 10% for impact energy. Additional analyses confirm stable performance under reduced data availability and increased measurement noise, as well as generalisation for out-of-distribution cases across pristine and damaged regimes when damaged responses are included in training. These results indicate that the systematic integration of physics-informed biases enables reliable, physically consistent, and data-efficient impact identification, highlighting the potential of the approach for practical monitoring systems.