Papers

The increasing demand for deep learning in seismic interpretation has highlighted significant challenges, particularly the reliance on massive, labeled datasets and the inefficiency of training isolated models for individual tasks. To address these limitations, we introduce a unified, prompt-guided flow-matching framework (SeisDiff-intp) capable of executing multiple seismic interpretation tasks within a single model. By conditioning on varying prompts, the model dynamically switches between interpretation objectives without requiring structural modifications. Furthermore, to overcome the scarcity of labeled data for complex subsurface features, we propose an integrated generative augmentation strategy. By employing the flow matching setting, the framework can synthesize diverse and geologically realistic training pairs, specifically targeting structurally complex. Experimental results demonstrate that the proposed approach, coupled with generative augmentation, delivers high-quality, task-specific interpretations with stable and reproducible inference behavior. Ultimately, this approach provides a scalable, flexible, and robust alternative to single-task deep learning based seismic interpretation models.

The odd Euler ensemble, the locally stable attractor of the momentum-loop dynamics of freely decaying incompressible turbulence, has a self-similar energy spectrum \(E(k,t)=L^{-3/2}H(kL)\), \(L\sim\sqrt{\tilde\nu t}\), which does not depend on the dimension of space. We compute its scaling function as a Mellin–Barnes integral whose amplitude contains the ratio \(\zeta(p+15/2)/\zeta(p+17/2)\) of Riemann zeta functions; the odd ensemble differs from the even one by a prime-\(2\) Euler factor. The integral is evaluated by deforming the contour to Lefschetz thimbles. The non-trivial zeros of \(\zeta(s)\) generate a vertical wall of complex poles. As \(\xi=\log(k\sqrt{\tilde\nu t})\) increases, the thimble traps them in a Stokes staircase, first collectively and then a few at a time, and Berry smoothing turns each trapping into an analytic activation of an exponentially small oscillatory term. Assuming the Riemann Hypothesis and simplicity of the zeros, all these poles lie on one wall and their activations accumulate only at infinite time: the spectrum is analytic at every finite time and has an essential singularity at \(t=\infty\). The same analysis gives the second-order structure function, whose index falls continuously from \(2\) to \(0\) without an inertial plateau, and the decay laws, energy \(\propto t^{-5/4}\) and enstrophy \(\propto t^{-9/4}\). The operator representation and the stability of the Euler ensemble are derived in the first paper of this series, and the comparison with simulations and experiments is given in the third.

Autonomous LLM agents now produce complete research artifacts in machine-learning sandboxes, but real computational physics is harder: experiments are first-principles calculations against re-runnable physical ground truth, and meaningful new work almost always builds on a key existing paper. We ask whether such an agent can perform grounded scrutiny of published computational physics - reading a paper, reproducing it from scratch, and surfacing methodological concerns from execution. We deploy a single Claude Opus 4.6 configuration at two complementary scopes. At scale, across 111 open-access Quantum ESPRESSO papers, an autonomous agent runs the read-plan-compute-compare loop and, although never asked to critique, raises substantive methodological concerns on ~42% of papers; 85 of 88 of these critiques (96.6%) surface only after the agent has actually run a calculation, with a reading-only ceiling of 1.8%. Critique emerges from reproduction, not from reading. In depth, on one Nature Communications paper on multiscale device simulation of a 2D-material MOSFET, a fresh agent inheriting a verified reproduction pipeline autonomously produces a 14-concern physics inventory and a complete, submission-form six-page Comment that revises the paper's L_G = 5 nm headline. Two of its L_G = 5 nm headline-challenging attacks - a source-degeneration contact-resistance bound and a Sb-doping degradation ratio - are absent from the published 21-reviewer peer review.

Accurate urban surface characterization is essential for environmental modeling, risk assessment, and climate adaptation. However, existing classifications of urban surfaces lack the global consistency and physical detail to fully represent present-day urban heterogeneity. To address this need, we developed a globally unified, Data-driven Urban Environmental Zone (DUEZ) framework. By applying unsupervised clustering to high-resolution (500-m) datasets of building morphology, vegetation, and surface imperviousness, we classified global urban surfaces into 27 DUEZs, representing the exposure setting for approximately 85% of the global population. Compared to the Local Climate Zone scheme, DUEZ framework provides a more detailed representation of urban form, capturing the fine-scale mixing of built and vegetated surfaces in modern cities. Further aggregation of DUEZ patterns revealed nine predominant urban textures globally with regional differences and socioeconomic relevance. The DUEZ framework enhances physical representation of complex urban surfaces in numerical models and establishes a consistent, data-driven basis for global urban environmental studies.

Hyperbolic phonon polaritons (HPPs) are coupled oscillations of anisotropic lattice vibrations and electromagnetic fields that confine the latter to the nanoscale, enabling novel nano-polaritonic devices. While HPPs have been identified in multiple layered materials, achieving advanced control and manipulation - particularly polariton canalization for unidirectional energy flow - often necessitates complex device fabrications or crystal modifications. Here we visualize and elucidate the properties of in-plane hyperbolicity in alpha-V2O5, a layered compound with a highly anisotropic permittivity tensor. We show unidirectional Poynting-vector propagation of polaritons in alpha-V2O5 without additional treatments. Combined with theoretical modeling, our infrared nano-imaging studies unveil a novel form of polariton canalization, with its dispersion contour continuously tunable by the incident light frequency. Additionally, we provide a theoretically calculated permittivity phase diagram for tailoring polaritonic wavefronts. These findings suggest that the metal-oxide alpha-V2O5 holds great promise for on-demand light canalization and control at the nanoscale.

We learn parameterized nonlinear elasticity on curved surfaces using a physics-informed neural network that enforces governing equations and boundary conditions directly through the loss function, enabling a single trained model to represent a continuous family of elastic equilibria across geometric and material parameters. Nonlinear elasticity on curved manifolds underlies the mechanics of crystalline shells, elastic membranes, and viral capsids, where curvature and topological defects determine equilibrium structure and stability. Traditional exact and finite element solvers rely on symmetry reduction and must be reinitialized for each parameter choice, limiting scalability when symmetry is broken or parameters vary. We validate the proposed learning-based solver on a benchmark problem from curved elasticity, namely the one-dimensional single disclination on a spheroidal surface with known exact and numerical solutions. The network accurately reproduces these solutions, including parameter combinations excluded from training, demonstrating generalization across geometry and material regimes. This study establishes a scalable framework for learning nonlinear elastic systems on curved manifolds and lays the groundwork for extensions to fully two-dimensional and multi-defect configurations relevant to protein shells and other curved elastic networks.

Ferromagnetic materials are widely used in Tesla thermomagnetic engines, whereas diamagnetic counterparts have remained unexplored. Here, we demonstrate the first diamagnetic Tesla engine by exploiting the strong diamagnetism of graphene. A graphene disk, fabricated by stacking graphene sheets, serves as the engine wheel. We first show that the conventional Tesla engine design using a permanent magnet placed near the disk edge to create unbalanced thermomagnetic forces under asymmetric local heating fails to generate rotation. We achieve stable operation by laterally displacing the levitated disk from equilibrium, creating a strong restoring force that drives rotation under light excitation. Calculations and measurements establish the displacement-dependent force, with an optimal offset of 0.8 mm yielding speeds up to 2000 rpm under laser heating and 1000 rpm under direct sunlight. Adding vanes allows the disk to function as a gear, powering a graphene vehicle and transferring energy to another disk. This design utilizes the strong and anisotropic diamagnetism of graphene and paves the way for light-powered sensors, actuators, and micro-vehicles.

Modeling relaxation phenomena in complex media is central to understanding multiscale dynamics in materials science, bioengineering and condensed matter physics. Existing fractional-order models, while flexible, sometimes lack physical interpretability, closed-form time-domain expressions, and compatibility with physically realizable architectures. In this work, we propose a novel passive element whose impedance and admittance are defined analytically via modified Bessel functions of first kind, through the electro-mechanical analogy. This approach preserves key physical properties such as analyticity, passivity, BIBO (bounded-input, bounded-output) stability and monotonicity, while enabling the direct use of its time-domain representation in simulations and system modeling. As an application, we demonstrate that this model accurately captures the broadband dispersive behavior of biological tissues, offering a physically grounded and tractable alternative to fractional-order formulations.

Accurately treating strong electron correlation in quantum chemistry typically requires multireference wave-function methods with steep computational scaling. The recently proposed i-DMFT method promises near configuration-interaction accuracy at mean-field cost by invoking an empirical linear relation between correlation energy and entropy (Collins' conjecture), whose validity remains unclear. We systematically assess this relation across a range of di- and polyatomic molecules, including diverse bond types, third-row elements, different types of geometric distortions, and excited states. We find that the conjectured linearity holds for bond-breaking processes dominated by electron redistribution within orbital pairs, but breaks down for heterolytic dissociation and excited states. In simple molecules, i-DMFT provides a reasonable description of total energies, but does not reliably reproduce reduced density matrices or individual energy components. It further degrades in more complex cases such as ethylene. Based on these results, we formulate criteria for the validity of the conjecture and outline implications for entropy-based reduced density matrix functionals.

Electron spin-dependent chemical reactions in proteins, often discussed under the 'radical-pair mechanism', remain the leading microscopic proposal for magnetic field sensing in biology. Yet the essential physics is often obscured by the complexity of realistic models. In this work, we present a physicist-friendly primer on the simplest radical-pair Hamiltonian that already captures many of the mechanism's best-known qualitative features. The contributions of this work are fourfold. First, we place on record a complete analytical solution of this toy model, which has previously been studied extensively, mostly through numerical and partial analytical approaches. Working in the experimentally relevant singlet-triplet basis, we derive closed-form expressions for the instantaneous singlet population and for two related time-averaged singlet yields. Second, we introduce a new interpretation of these results that makes several familiar features of radical-pair physics transparent. In particular, we show that the dynamics admit a bright-dark decomposition (in the sense of spin mixing), similar to structures studied in atomic physics. Third, through this bright-dark perspective, we clarify experimentally relevant features of the toy model. In particular, we show that the so-called 'low-field effect' arises from a coherence term between bright and dark sectors, and that the special role of zero field is best understood as a phase-locking phenomenon rather than merely as enhanced mixing. Fourth, we import methods developed in the context of technological quantum sensing to obtain further insight into the model. This allows us to clarify the role of initial state preparation and the trade-off between coherent phase accumulation and time-averaging penalties. The resulting toy model serves both as an analytically tractable benchmark and as a conceptual starting point for future work.

The velocity field within a steady toroidal vortex is found for arbitrary mean core radius and section ellipticity. The problem is solved by transforming to coordinates that define invariant sets. The method allows the properties of the coordinate system metric tensor to be exploited in the continuity equation in order to obtain the solution. The vorticity is found to decrease monotonically with distance from the symmetry axis. For a given outer radius and outer perimeter velocity, the circulation of the vortex ring can be either smaller or larger than that of Hill's spherical vortex.

The 100,000-year problem concerns the dominant period of glacial-interglacial cycles over the past 800,000 years and their correlation with Earth's orbital eccentricity, despite eccentricity's weak influence on solar radiation. Two theories compete: the astronomical theory, in which orbital forcing drives the cycles with amplification from Earth system feedbacks, and the geochemical theory, in which internal dynamics dominate with orbital forcing synchronising oscillations. We investigate these theories using conceptual models. Augmentations to the Budyko energy balance model fail to reproduce the 100,000-year period, revealing formulation limitations. Linearised versions of existing non-linear ice volume models perform comparably to their full counterparts, indicating the data does not necessitate non-linear dynamics. We develop two simple linear models: a feedforward model aligned with the astronomical theory and a feedback model aligned with the geochemical theory. The feedforward model reproduces the ice volume record well and offers a novel explanation for the absence of eccentricity's 400,000-year period, arising from oceanic heat storage and tropospheric energy responding with differing phase lags. Conservative estimates show bulk ocean temperature variation can be explained by eccentricity alone, challenging the geochemical theory's core assumption. We also show that widespread use of Q65 may bias models towards geochemical explanations by underrepresenting eccentricity. The feedback model's improvement is concentrated around Marine Isotope Stage 11, suggesting this anomalous interglacial reflects Earth-based events rather than a general requirement for feedback mechanisms. We conclude that 800,000 years of glacial cycles can be largely reproduced by a linear astronomical model, emphasising the importance of parsimony when interpreting palaeoclimate data.

We present XANE(3), a physics-based E(3)-equivariant graph neural network for predicting X-ray absorption near-edge structure (XANES) spectra directly from atomic structures. The model combines tensor-product message passing with spherical harmonic edge features, absorber-query attention pooling, custom equivariant layer normalization, adaptive gated residual connections, and a spectral readout based on a multi-scale Gaussian basis with an optional sigmoidal background term. To improve line-shape fidelity, training is performed with a composite objective that includes pointwise spectral reconstruction together with first- and second-derivative matching terms. We evaluate the model on a dataset of 5,941 FDMNES simulations of iron oxide surface facets and obtain a spectrum mean squared error of $1.0 \times 10^{-3}$ on the test set. The model accurately reproduces the main edge structure, relative peak intensities, pre-edge features, and post-edge oscillations. Ablation studies show that the derivative-aware objective, custom equivariant normalization, absorber-conditioned attention pooling, adaptive gated residual mixing, and global background term each improve performance. Interestingly, a capacity-matched scalar-only variant achieves comparable pointwise reconstruction error but reduced derivative-level fidelity, indicating that explicit tensorial channels are not strictly required for low intensity error on this dataset, although they remain beneficial for capturing finer spectral structure. These results establish XANE(3) as an accurate and efficient surrogate for XANES simulation and offer a promising route toward accelerated spectral prediction, ML-assisted spectroscopy, and data-driven materials discovery.

Turbulent mixing remains one of the primary uncertainties in the modeling of stellar interiors. In stellar evolution simulations, regions where mixing occurs are typically identified using instability criteria. A particularly interesting situation arises when nuclear reactions produce inversions in the mean molecular weight within stellar interiors. Under these conditions, the material can become unstable to either thermohaline or a Rayleigh-Taylor instabilities. We demonstrate that the standard criterion adopted in stellar evolution calculations does not accurately distinguish between these two regimes. We derive an alternative criterion and show that chemically driven convection in stellar interiors might be viable under much smaller mean molecular weight inversions than it is normally assumed. We investigate whether inversions in the mean molecular weight can trigger chemically driven convection above the red giant branch bump (RGBB) or during the helium core flash. We find that the inversion at the base of the convective envelope above the RGBB is too weak and short-lived to sustain steady-state convection. In contrast, rapid carbon production at the base of the He-flash-driven convective zone can maintain a steady chemically driven convective region. This process could significantly alter our understanding of the He-core flash and warrants further study.

Mott physics - the interplay between itinerancy and localization of electrons - is undergoing a paradigm shift from the binary "bandwidth - filling" tuning framework to an intertwining of geometric, topological, and fractionalized degrees of freedom. Based on a series of breakthroughs in 2024 - 2025, this paper proposes five pioneering discoveries: (1) Prediction of the golden-ratio scaling of quantum metric fluctuations near the Mott critical point, supported by functional renormalization group arguments and DMRG numerical verification (phi = 0.618 +/- 0.005); (2) Establishment of a correspondence between the denominator q of fractional Chern insulator charge and the subgroup index of the quantum geometry group, predicting that allowed q values follow the Fibonacci sequence {2,3,5,8,13,...} with specific material realizations; (3) Proposal of the Provability Hierarchy Theorem, classifying critical states like strange metals as "true but unprovable" QMA hard problems, establishing a rigorous connection to the complexity of the Consistency of Local Density Matrices(CLDM) problem; (4) Prediction of interference oscillations in the nonlinear Hall conductance within the pseudo gap phase, induced by geometric phase differences, supported by tight-binding numerical simulations; (5) Unveiling the quantum geometric tensor as a unified descriptor of band geometry and topology. These findings provide an experimentally testable theoretical framework for understanding strongly correlated quantum materials.

I argue that there is a straightforward way to understand the occurrence of wavefunction collapses or 'quantum events' in relational approaches to quantum mechanics: we necessarily encounter a discontinuity in our description when a system interacts with the reference relative to which we are describing it, since the reference system cannot be described relative to itself. This makes it clear how recent concerns around quantum events in relational quantum mechanics should be resolved. However, the solution requires accepting that quantum mechanics is not a complete description of all physical facts, and moreover I argue that this is most likely inevitable if we want to be able to give a precise description of quantum events.

Renewable liquid fuels are essential for achieving emissions targets for hard-to-electrify sectors such as aviation and shipping. While biofuels and synthetic e-fuels have been well-studied, e-biofuels, produced by adding renewable hydrogen to biomass conversion to better utilise the biogenic carbon, remain understudied and lack a clear role in EU fuel regulations. In this paper, using a sector-coupled European energy system model, we find that e-biofuels are cost-effective to meet stringent emissions targets if biomass availability is limited and fossil fuels are ineligible, either due to limited carbon sequestration capacity or to high renewable fuel mandates. By directly increasing utilisation of biogenic carbon instead of synthesising fuels based on captured $CO_2$, there are savings from fuel production and carbon capture that reduce total system costs by up to 2.7% and liquid fuel costs by more than 10%. Our results highlight the role of e-biofuels as a potential hedge against uncertainty in biomass, hydrogen, and carbon storage availability, as well as evolving policy implementation.

Multiscale Proper Orthogonal Decomposition (mPOD) decomposes fluid flows into energy-optimal modes within prescribed frequency bands by combining Proper Orthogonal Decomposition with a multiresolution analysis (MRA). In its classical formulation, mPOD relies on a filter bank of finite impulse response (FIR) filters, enabling lossless reconstruction while mitigating Gibbs oscillations and temporal ringing. However, the smooth transition bands required for this purpose introduce partial spectral overlap between adjacent scales and require, for each band, the solution of an eigenvalue problem spanning the full temporal dimension. This work introduces a fast spectral formulation of the mPOD that substantially reduces the computational cost. The proposed approach replaces time-domain FIR filters with compact spectral masks enforcing strictly disjoint frequency supports, thereby exactly decoupling the problem across scales. This leads to a block-diagonal correlation operator in spectral space, so that each band can be treated independently. The resulting eigenvalue problems reduce to small systems whose size depends on the number of active frequencies per band rather than the full time dimension. The approach is validated on a synthetic dataset highlighting spectral windowing effects and on experimental particle image velocimetry (PIV) data of a cylinder wake at Reynolds number \(\mbox{Re} \approx 5000\). In both cases, the proposed formulation accurately recovers the modal structures and singular values of the classical mPOD while reducing the computational cost by orders of magnitude.

Turbulent channel flow controlled by spanwise wall oscillations is studied using direct numerical simulations to improve how spanwise forcing reduces skin-friction drag. Harmonic wall oscillations generate a periodic transverse Stokes layer whose thickness $\delta$ is determined by the forcing period $T$. Although an optimal $T$ that maximizes drag reduction is known to exist, its physical significance remains unclear. To elucidate it, we extend the spanwise Stokes layer by augmenting wall oscillation with an additional spanwise body force. In this formulation, $\delta$ and $T$ become decoupled and can be varied independently. The oscillating wall thus appears as a special and suboptimal case of spanwise forcing. Optimal performance is obtained for substantially smaller $T$ and larger $\delta$ than those of the classical Stokes layer. For the conditions examined, with Reynolds number and forcing amplitude held fixed, the maximum drag reduction increases by approximately one third, while the maximum net energy saving improves markedly from $-35\%$ to $+16\%$. These findings suggest that drag-reduction strategies based on spanwise forcing deserve renewed scrutiny: wall oscillation represents only one possible actuation method, and not necessarily the most effective one.

The puzzle of "sticky collisions," in which molecular collision complexes exhibit unexpectedly long lifetimes, remains an unresolved mystery. A central challenge to solving this mystery is that traditional close-coupling calculations remain limited by the vast computational cost needed to take into account all the degrees of freedom involved in the collision. In this work, we propose a statistical model designed to simulate the result of full close-coupling calculations, with the goal of collecting statistics about reasonable lifetimes of collision complexes. To do so, we numerically sample resonances using random matrix theory and utilize results from quantum defect theory to calculate scattering properties and lifetimes. We find that in the limit of dense resonances, our theory agrees well with the Rice-Ramsperger-Kassel-Markus (RRKM) prediction, whereas in the limit of sparse resonances, the physics is governed by threshold behavior rather than resonant effects. By comparing these predictions to experimental results in two limits, we argue that close-coupling calculations alone may be insufficient to resolve the issue of long lifetimes.

A recent study \cite{rajamanickam2025shear} of non-Boussinesq fluids in narrow channels identified a novel shear-induced horizontal buoyancy force that emerges upon depth-averaging the Navier-Stokes equations. This letter demonstrates that this force is formally equivalent to the divergence of a Korteweg stress tensor. Unlike classical Korteweg stresses, which are typically attributed to molecular-scale cohesive potentials or implemented through assumed constitutive relations, we show that this emergent stress arises purely from self-coupled transport where the internal Ostroumov flow is kinematically coupled to the local density gradient. We derive explicit expressions for the effective stress coefficients, revealing a fundamental dependence on the Prandtl number and Grashof number. This correspondence is contrasted with classical Taylor dispersion, where the absence of self-coupling yields only a uniaxial stress. Although derived within a narrow-channel framework, our results establish a general hydrodynamic template for how quadratic gradient stresses can emerge from subscale, self-coupled flows, such as Marangoni or active-matter flows, offering a continuous transport-driven alternative to molecular mechanisms.

A novel design for straight directional waveguide couplers and interferometers is demonstrated in glass, fabricated using femtosecond laser direct writing and operating at telecommunication wavelengths (~1550 nm). The devices consisted of parallel waveguides with a spacing of 15 um, where the coupling strength was controlled by scan-engineered refractive index modulation along the length of the waveguide. Using this approach, we realized a 50:50 directional coupler formed by two identical waveguides with a footprint of < 40 um x 15 um x 6 mm, as well as a Mach-Zehnder interferometer with unbalanced arms. A waveguide array with 15 um spacing was also demonstrated, highlighting the potential for compact, high-density, and three-dimensional photonic integration.

We present a full-vector finite element method (FEM) mode solver for dielectric waveguides based on a mixed Nedelec-Lagrange discretization of Maxwell's curl equations in the frequency domain. The formulation combines edge elements for transverse field components with nodal elements for the longitudinal component, enabling accurate modeling of hybrid modes while effectively suppressing spurious solutions. The solver is implemented in both MATLAB and Python with an emphasis on reproducibility, computational efficiency, and accessibility, including compatibility with cloud-based platforms. Numerical validation is performed on representative waveguide structures, demonstrating excellent agreement with COMSOL Multiphysics, with relative errors below 0.05%. Convergence studies confirm the expected accuracy trends with mesh refinement, while highlighting the trade-off between computational cost and precision. The proposed implementation provides a flexible and reliable open-source tool for integrated photonics research and education.

The interest to foster plasmonic applications at energies in the ultra-violet, has escalated research initiatives in clusters of unconventional plasmonic materials like aluminum and indium,for which the surface-plasmon resonance appears above the ionization potential. Naturally, the quantum mechanical description calls for the incorporation of the ionization process, thereby making the ab initio calculations challenging. We present a robust approach within the time-evolution formalism of the time-dependent density-functional theory to calculate surface plasmon resonance in the continuum of metal clusters. Using the much studied Al$_{13}^-$ as a system of reference, we show that accurate description of the continuum and of the ionization of the cluster allow to capture a broad surface-plasmon in the UV. Application of this approach in aluminum clusters has given the size-dependent evolution from discrete spectral features in Al$_{6}$ to the surface-plasmon in larger clusters in the deep ultra-violet.

Two-dimensional tsumoite (BiTe), a polymorph of Bi2Te3, has emerged as a promising candidate for nonlinear photonic devices owing to its strong spin-orbit coupling, tunable bandgap, and high carrier mobility characteristics. This work presents a thorough examination of the third-order nonlinear optical response of BiTe dispersions using spatial self-phase modulation (SSPM) spectroscopy. The nonlinear refractive index (n2) and third-order nonlinear susceptibility are quantitatively derived from the diffraction ring patterns, demonstrating third-order nonlinear susceptibility values, similar to or surpassing those of advanced 2D materials. The temporal development and distortion of the SSPM rings are examined using the wind-chime model, and thermal factors influencing the SSPM pattern are analyzed. First-principles electronic band structure studies reveal that the elevated nonlinear susceptibility arises from band dispersion. Direct correlation between carrier transport and third-order nonlinear susceptibility is established. Utilizing these qualities, all photonic devices, including a photonic isolator based on a 2D BiTe-2D hBN heterostructure, are depicted to show asymmetric propagation. A photonic information converter and a logic gate are designed using the cross-phase modulation technique. These findings establish 2D BiTe nanostructure as a formidable nonlinear optical platform for advanced photonic signal processing and integrated photonic applications.

We report the properties of electron distributions and turbulence during a Coronal Mass Ejection (CME) in April 2023 observed by Magnetospheric Multiscale (MMS). The CME exhibits a clear sheath and magnetic cloud (MC), and within the MC, the solar wind becomes sub-Alfvénic for two hours. We investigate plasma and turbulence properties of the sub-Alfvénic CME wind and compare them with those in the super-Alfvénic solar wind in the MC and CME sheath. Electrons within the sub-Alfvénic MC show significantly higher temperatures than those in the CME sheath and the super-Alfvénic MC, with their one-dimensional distributions revealing super-thermal tail and a depletion in electron populations between 15-50 eV. Within the CME sheath, isolated regions of electron heating are observed, where parallel energy flux is enhanced up to ~1 keV. Magnetic field fluctuations within the sub-Alfvénic MC interval exhibit negligible cross helicity and steeper-than-Kolmogorov scaling in the inertial range, with no clear spectral break. These fluctuations also show reduced intermittency at ion and sub-ion scales, emerging intermittency at electron scales, and weak magnetic compressibility. Together, these observations point to the presence of weak magnetohydrodynamic (MHD) turbulence within the sub-Alfvénic MC, resembling conditions commonly observed in planetary magnetospheres such as Jupiter's.

Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage of the knowledge that signal photons are spatially correlated whereas detector clicks due to noise are uncorrelated. However, in computer-vision tasks, the goal is often not ultimately to reconstruct an image, but to make inferences about a scene – such as what object is present. Here we show how correlated-photon illumination can be used to gain an advantage in a hybrid optical-electronic computer-vision pipeline for object recognition. We demonstrate correlation-aware training (CAT): end-to-end optimization of a trainable correlated-photon illumination source and a Transformer backend in a way that the Transformer can learn to benefit from the correlations, using a small number (<= 100) of shots. We show a classification accuracy enhancement of up to 15 percentage points over conventional, uncorrelated-illumination-based computer vision in ultra-low-light and noisy imaging conditions, as well as an improvement over using untrained correlated-photon illumination. Our work illustrates how specializing to a computer-vision task – object recognition – and training the pattern of photon correlations in conjunction with a digital backend allows us to push the limits of accuracy in highly photon-budget-constrained scenarios beyond existing methods focused on image reconstruction.

Nonlinear light-matter interaction and, in particular, high-harmonic generation (HHG) are fundamentally interesting and frequently discussed as versatile probes of quantum materials with potential for optical information processing applications. Meanwhile, there has also been significant progress in graphene-based multilayer systems to engineer interesting band structures and boost correlation effects. Motivated by the successful demonstration of HHG in graphene, we here study this effect in rhombohedral stacks of $n$ layers of graphene, a recent very prominent representative of correlated multilayer graphene systems. We show how the chiral Bloch states of the valleys of this system crucially affect the HHG. The "winding" of the Bloch states scales linearly with $n$, just like the dominant harmonic order. The location of the strongest quantum geometry in momentum space on a ring of finite radius is shown to be imprinted on the time-dependent momentum distribution at the beginning of the strong laser pulse. We further demonstrate that the presence of an interaction-induced splitting of the two valleys leads to a complex interplay of the opposite chiralities of the two valleys, directly visible in the $n$ dependence of the circular dichroism. We also analyze the impact of doping and identify a quantity that tracks the net chirality of the occupied states. Our findings show that rhombohedral graphene constitutes a promising platform for exploring rich nonlinear optical phenomena.

Our ability to predict, control, and ultimately understand complex systems rests on discovering the equations that govern their dynamics. Identifying these equations directly from noisy, limited observations has therefore become a central challenge in data-driven science, yet existing library-based sparse regression methods force a compromise between automation, statistical rigor, and computational efficiency. Here we develop Bayesian-ARGOS, a hybrid framework that reconciles these demands by combining rapid frequentist screening with focused Bayesian inference, enabling automated equation discovery with principled uncertainty quantification at a fraction of the computational cost of existing methods. Tested on seven chaotic systems under varying data scarcity and noise levels, Bayesian-ARGOS outperforms two state-of-the-art methods in most scenarios. It surpasses SINDy in data efficiency for all systems and noise tolerance for six out of the seven, with a two-order-of-magnitude reduction in computational cost compared to bootstrap-based ARGOS. The probabilistic formulation additionally enables a suite of standard statistical diagnostics, including influence analysis and multicollinearity detection that expose failure modes otherwise opaque. When integrated with representation learning (SINDy-SHRED) for high dimensional sea surface temperature reconstruction, Bayesian-ARGOS increases the yield of valid latent equations with significantly improved long horizon stability. Bayesian-ARGOS thus provides a principled, automated, and computationally efficient route from scarce and noisy observations to interpretable governing equations, offering a practical framework for equation discovery across scales, from benchmark chaotic systems to the latent dynamics underlying global climate patterns.

CORC$^\circledR$ wires are a promising superconductor for accelerator magnet applications. While their excellent uniaxial tensile properties have been well established, potential degradation under transverse compression remains a significant concern for accelerator magnets, in which transverse compression is a primary stress experienced by superconductors. To evaluate the critical transverse compressive stresses of ReBCO conductors, we developed an experimental system that enables testing of samples both with and without impregnation in liquid nitrogen. Furthermore, because bending strain induced during coil winding may influence the critical compressive response of CORC$^\circledR$ wires, the apparatus was also modified to allow testing under the bending condition. In this study tests were conducted for five configurations: (1) straight wires without impregnation, (2) bent wires without impregnation, (3) straight wires with Stycast 2850 FT impregnation, (4) bent wires with Stycast 2850 FT impregnation, and (5) bent wires impregnated with paraffin wax. The transverse pressures corresponding to 3% and 5% reductions in the critical current are reported. The effects of wire bending and impregnation on the critical transverse pressure are analyzed, and the implications for transverse stress levels in accelerator magnet conductors are discussed.