Papers

Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the "last mile" problem: even with strong automation, verification and correction consume substantial human effort. Standard approaches train models to directly predict annotations, discarding the rich supervision in how experts navigate, click, verify, and correct. We introduce a framework for studying behavioral cloning on scientific annotation: 9 synthetic tasks paired with synthetic annotations that simulate realistic human strategies including exploration, mistake correction, and strategic decision-making. Our experiments reveal several findings. First, skills emerge hierarchically: models learn GUI mechanics before task-critical decisions, and commit fewer mistakes than the training data while retaining the ability to correct errors when they occur. Second, scaling models on multi-task behavioral cloning shows that larger models are more data efficient within our scale range. Third, multi-task pretraining enables efficient fine-tuning to new tasks, while training from scratch fails entirely. Fourth, linear probes reveal that models internally represent latent variables of the annotation process such as task phase and data position; interestingly, we find a shared mistake representation that generalizes across different annotation tasks. Overall, our framework establishes systematic benchmarks and identifies key bottlenecks, providing a foundation for scaling behavioral cloning to real-world scientific data annotation.

FENE-type conformation-tensor models impose a finite-extensibility constraint that is absent from Oldroyd–B flow: the conformation tensor must satisfy $\CC\succ0$ and $\tr\CC<L^2$. Positive definiteness alone is therefore insufficient, since a numerical state can remain positive while crossing the singular trace barrier. Even a trace-preserving logarithmic parametrization is not enough by itself: high-order reconstruction can remain inside the finite-extensibility domain while injecting artificial FENE entropy. We develop and analyze a barrier-preserving entropy-compatible discretization for FENE-P type flows with polymer center-of-mass molecular diffusion and for trace-singular FENE-family closures with the same entropy structure. The method combines a trace-barrier free energy, a finite-extensibility logarithmic parametrization, a least-damping entropy-compatible barrier-log reconstruction, molecular diffusion paired with the barrier entropy variable, compatible quadrature for polymeric work, and a scaled FENE stress variable for the small-Weissenberg limit. For admissible discrete states we prove finite-extensibility preservation at entropy quadrature points, existence and bisection computability of the maximal entropy-admissible reconstruction parameter, a fully discrete free-energy inequality with relaxation and molecular-diffusion barrier dissipation, a quantitative AP stress closure, and a fixed-discretization Newtonian limit. A conditional relative-entropy estimate is derived on compact subsets of the finite-extensibility domain. Numerical diagnostics verify barrier preservation, entropy-compatible reconstruction, energy decay, AP closure, coupled velocity–pressure–stress accuracy, and high-Weissenberg robustness near the trace constraint.

We study mobility heterogeneity in a two-dimensional Gaussian lattice polymer using dynamic Monte Carlo simulations. The polymer dynamics is generated from a local three-monomer move dictionary, which explicitly enumerates allowed bond-preserving updates on a square lattice. As a homogeneous benchmark, this dictionary reproduces the expected Rouse-like behavior of an ideal chain, including the crossover in monomer mean-squared displacement (MSD) and the center-of-mass diffusion scaling $D_{\rm cm} \sim N^{-1}$. We then introduce a two-block version of the model in which the two halves of the chain are updated with different attempt rates, $\omega_A$ and $\omega_B$, while the local move dictionary remains unchanged. For $\rho=\omega_A/\omega_B>1$, the more frequently updated block shows a larger block-resolved MSD at early and intermediate times, producing a positive normalized MSD asymmetry. However, numerical measurements show that the center-of-mass diffusion coefficient remains consistent with $D_{\rm cm} \sim N^{-1}$ for all rate ratios studied. We invoke a simple coarse-grained Rouse argument to explain this result analytically. In this minimal Gaussian setting, rate-induced mobility heterogeneity modifies internal relaxation without changing the Rouse scaling of center-of-mass transport.

In decaying magnetohydrodynamic turbulence, energy can be transported from small to large scales, known as inverse transfer. We explore the mechanism behind this phenomenon using shell-to-shell transfer functions. Independent of magnetic net-helicity, large magnetic scales receive energy directly from the integral scale in both the magnetic and kinetic reservoirs, leading to increasingly non-local transfer for larger receiving scales. The resulting rate of energy increase in each receiving scale is proportional to its energy, resulting in self-similar, multiplicative growth. Even though the system is magnetically dominated, contributions from kinetic-magnetic and magnetic-magnetic energy-exchange are similar in magnitude. In the case of vanishing net-helicity, transfer functions between the positively and negatively helical parts of the field are computed. We find that inverse transfer only occurs within each helical sector, not across them. Our findings are consistent with the theory underlying the conservation of the Hosking integral, which explains inverse transfer as merging of local magnetic islands with equal-signed helicity.

Background in General Relativity (e.g. black holes, wormholes, or spacetime models in general) is needed to comprehend more recent efforts around understanding quantum phenomena like entanglement (e.g. >>It from qubit<< as well as >>ER = EPR<<). The former in turn requires a lot of knowledge from differential topology and differential geometry. While this knowledge is available in very good mathematics textbooks, it is scattered i.e. quite a bunch of sources need to be consulted to acquire it. The goal of this contribution is to provide the major background in a single place; in this sense, this contribution is some sort of guided tour through the corresponding literature.

High-fidelity computational fluid dynamics is essential for aerospace design, but engineering-scale simulations of practical three-dimensional aircraft remain computationally expensive. Learning-based flow-field initialization can improve efficiency by reducing the numerical distance between the initial and converged solutions, yet existing deep learning approaches remain difficult to scale to large three-dimensional aircraft flows with multiscale regional heterogeneity. Most prior studies therefore focus on two-dimensional problems, surface quantities, integral aerodynamic coefficients, or simplified three-dimensional cases with limited grid resolution.Here we propose MHLF, a multigrid-hierarchical learning framework for accelerating engineering-scale aircraft flow simulations while preserving high-fidelity numerical accuracy. MHLF combines a topologically consistent geometric multigrid representation with a hierarchical strategy that captures regional flow heterogeneity during both prediction and subsequent CFD correction. Across three engineering-scale aircraft cases spanning Mach 0.15 to 6.0 and covering subsonic, transonic and supersonic regimes, MHLF accelerates convergence without sacrificing flow-field accuracy, achieving a 3 to 8 times efficiency improvement over conventional initialization. These results demonstrate practical full-flow-field prediction for large three-dimensional aircraft within the CFD domain and provide a foundation for data-driven acceleration of high-fidelity aircraft flow simulation.

Extending discrete-time causal Prior-data Fitted Networks for time series to continuous time invites writing the mechanism as a stochastic differential equation (SDE) – but if the SDE is integrated once per observation gap, the trajectory law depends on when it is observed, and the prior remains a discrete-time Markov model in SDE clothing. We propose a precise continuity criterion – trajectory-law invariance to the observation schedule – together with a three-tier taxonomy (discrete; naive observation-grid integration; fine-grid integration with decoupled observation) and a construction realising the top tier on a random DAG with OU or small-MLP nonlinear drifts, irregular observation schedules, and hard / soft / time-varying interventions. A $2 \times 2$ encoder $\times$ integrator ablation, run independently on a linear and a nonlinear prior, finds fine-grid integration beats naive on 8/8 cells (sign-consistency $p < 1/256$) with the gap growing as the eval grid refines; the encoder axis is null with fine integration but time-aware-leading with naive. We release the prior and a preliminary zero-shot protocol on pharmacokinetic and physical-system data.

In this paper, we present a detailed review/analysis of the Dirac quantisation of Hamiltonian systems with constraints. To this end, we use, as a guide, the physical example provided by the dynamics of a solid ball rolling, without slipping, down an inclined plane under the action of gravity. After all, however simple this physical system may be, it provides a rich framework for this analysis since, in addition to allowing us to discuss scenarios involving holonomic and non-holonomic constraints, it is also a gauge system. Indeed, due to this latter fact, we have carefully detailed how the transition, from classical to quantum mechanics, must be guided by the Dirac-Bergmann algorithm and by the consequent replacement of Dirac brackets with commutators. As a central result, we demonstrate that the restriction of the Hamiltonian operator of this system with constraints to the physical Hilbert subspace (which is identified with the quantisation of these constraints) reproduces the same Schrödinger equation that can be originally obtained in intrinsic terms, a fact that only reinforces the consistency of the Dirac quantisation method.

We present the explicit theory of the Joss-Weinberg covariant field with spin $\frac{3}{2}$ which is a eight-dimensional massive covariant field transforming according to the representation $(\frac{3}{2},0)\oplus(0, \frac{3}{2})$ of the group $SL(2,\mathbb{C})$. As the transformation matrices of this representation are still unknown, we apply a new method for deriving them using exclusively maximally reducible representations, e. g. $(1,0)\otimes(\frac{1}{2},0)$ instead of the irreducible one $(1,\frac{1}{2})=(1,0)\otimes(0,\frac{1}{2})$ we meet in usual frameworks. After applying this method, we obtain a $12$-component covariant field transforming according to the representation $[(1,0)\otimes(\frac{1}{2},0)]\oplus [(0,1)\otimes(0, \frac{1}{2})]$ which is maximally reducible, up to subspaces of irreducible representations of the $SU(2)$ group. Consequently, after developing the theory in the direct product basis of the representation $(1,0)\otimes(\frac{1}{2},0)$, we can separate the sector of spin half revealing thus the genuine Joss-Weinberg covariant field of spin $\frac{3}{2}$, transforming according to the representation $(\frac{3}{2},0)\oplus(0, \frac{3}{2})$. In this manner the theory of Joss-Weinberg covariant field of spin $\frac{3}{2}$ can be build naturally deriving the field equation and associated matrices, Lagrangian formalism, inner product and the closed expressions of the orthonormal mode spinors.

Precision interferometry with atomic wavepackets confined in a one-dimensional optical lattice is an emergent paradigm in quantum sensing of forces and fields, with applications in gravimetry, accelerometry, geophysics, and fundamental physics tests. We report on the realization of a lattice-trapped interferometer where the two arms are sourced from a weakly-interacting ytterbium Bose-Einstein condensate, coherently split and trapped by pulsed optical standing waves before recombination. We directly observe atomic interactions through contrast changes and phase shifts of the interferometer. By changing either the atom number or the sample volume to vary the density, we demonstrate control over interactions and optimize interferometer performance. Our observations are effectively captured by a mean-field theoretical model of the system. This work experimentally probes the boundary where improved performance from source brightening through higher phase space density transitions into a regime beyond single-atom physics in lattice-trapped atom interferometry, and opens a door to incorporating many-body effects for metrological advances in such platforms.

Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations and energy dissipation through iterative, high-fidelity simulations. While traditional finite element solvers are computationally prohibitive, emerging operator learning frameworks provide rapid surrogate predictions; however, applying them to industrial-scale crash analysis, where complex geometry, contact nonlinearities, and rapidly evolving transient deformation coexist, remains an open challenge. In this paper, we demonstrate that the GeoTransolver framework provides a viable solution for accurate, high-fidelity crash dynamics prediction at industrial scale. Benchmarked on complex bumper beam and full-vehicle crash datasets, GeoTransolver captures multi-scale geometric context and accurately resolves plastic deformation patterns as well as acceleration profiles at critical occupant locations. Beyond the architecture itself, we propose and systematically evaluate a suite of temporal prediction recipes, including one-shot, time-conditional, and autoregressive rollout strategies, demonstrating that the one-shot approach achieves state-of-the-art accuracy with significantly reduced training overhead and inference latency. As a secondary contribution, we introduce a Fast Low-rank Attention Routing Engine (FLARE)-based modification to the GeoTransolver attention backbone that reduces memory overhead by approximately 2x while further improving predictive accuracy for O(N) long-range, high-frequency transients, preserving the geometry-aware cross-attention strengths of the base framework. Our results highlight the practical viability of geometry-aware operator learning for high-fidelity surrogate modeling of complex, safety-critical automotive dynamics.

Linear dimensionality reduction methods such as proper orthogonal decomposition (POD) make high-dimensional data amenable to analysis by identifying the principal components, or modes, that capture the most variance, or energy, in the data and constructing a low-dimensional representation in the subspace they span. Such linear methods struggle, however, for data with slowly decaying Kolmogorov $n$-widths, such as advection-dominated and turbulent flows, which require many modes for accurate reconstruction; moreover, energy-based truncation can discard low-energy modes needed to capture small-scale features. Recent nonlinear manifold methods using polynomial mappings with alternating or greedy mode selection achieve better reconstruction with fewer modes, but fix the form of the nonlinear mapping a priori, limiting expressivity. In contrast, neural network (NN) manifolds offer greater expressivity yet employ energy-based selection. We present SparseModesNet, a dimensionality reduction framework that employs linear encoding and nonlinear NN decoding. The decoder leverages LassoNet, a method enforcing hierarchical sparsity through a residual connection with a linear skip layer, to simultaneously select informative modes and learn a nonlinear mapping that minimizes reconstruction error. On benchmark advection-dominated and chaotic flows, SparseModesNet matches or exceeds state-of-the-art performance. For turbulent channel flow at friction Reynolds number $Re_\tau = 5200$, our method reduces reconstruction error by 51-78% compared to existing polynomial manifold methods while maintaining interpretability through physically meaningful mode selection.

The Hong–Ou–Mandel (HOM) effect is often introduced through a single benchmark: coincidence suppression for \(\ket{1}\otimes\ket{1}\) at a balanced beam splitter. We present a classroom-oriented instructional module that broadens this treatment by comparing three output metrics – on/off coincidence probability \(P_c\), cross-correlation \(g^{(2)}_{12}\), and noise-reduction factor NRF – across four input families: Fock, superposition, coherent, and squeezed light. The module targets three instructional challenges in upper-division quantum optics: treating interference quality as a single observable, weakly connecting quantum–classical distinctions to output statistics, and limiting transfer from two-mode HOM reasoning to broader photonic benchmarking questions. A common beam-splitter sweep shows that different metrics probe different statistical properties of the same output state and can therefore support different source choices depending on the physical objective. The module combines a Jupyter/QuTiP simulator, guided activities, specification-style summary tables, and a grading rubric for use in a single upper-division or early graduate class meeting.

Pediatric patients with cardiac implantable electronic devices (CIEDs) face limited MRI access due to RF-induced heating, and computational modeling is increasingly used to characterize this risk. The validity of these simulations, however, depends on pairing body models with clinically realistic lead configurations, guidance that is currently lacking. We retrospectively analyzed 302 CIED surgeries in 281 pediatric patients to derive weight-based constraints for simulation design. Weight alone discriminated epicardial from endocardial lead implantation with AUC = 0.90, and adding age and height yielded no improvement, supporting weight as a sufficient single-parameter selection metric. The probabilistic crossover between approaches occurred at 44 kg, substantially higher than the 10 to 15 kg threshold commonly cited in the literature, with a broad transition zone of 21 to 66 kg in which both lead types were routinely used. Lead length was likewise weight-constrained: only 25 cm leads were observed in patients below 6 kg, and leads of 45 cm or longer were uncommon below 50 kg. These findings yield a three-tier framework, with epicardial-only configurations below 21 kg, dual configurations within 21 to 66 kg, and weight-thresholded lead lengths throughout, enabling MRI safety simulations to focus on clinically realizable anatomy and device combinations.

Polymer solutions exhibit anomalous flow thickening – marked by an abrupt increase in the macroscopic flow resistance – above a threshold flow rate in a porous medium, but not in bulk solution. This phenomenon has evaded a mechanistic description for over half a century. Here, we develop a model that quantitatively links pore-scale flow fields and fluid rheology to macroscopic flow thickening, and validate it in experiments in two- and three-dimensional (2D and 3D) porous media. We find that flow thickening in ordered media is governed by polymer extension at stagnation points – in contrast to disordered media, where viscous dissipation by unsteady flow fluctuations also contributes substantially. Our results provide a foundation to predict and control such flows in energy, environmental, industrial, and microfluidic applications.

High-fidelity computational fluid dynamics (CFD) is widely used for thermal-fluid design, but repeated CFD solves remain expensive for design optimization, uncertainty analysis, and digital-twin workflows. Recently, our team has demonstrated that a proper orthogonal decomposition and neural-network (POD-NN) surrogate can predict two-dimensional thermal fields in an electronics-cooling cold plate with large inference speedups while preserving physically interpretable modal structure. Reproducing and extending such workflows, however, typically requires custom scripts for parameter sampling, Fluent automation, data extraction, reduced-order model construction, neural-network training, validation, and prediction. This paper introduces CFDTwin, an open-source Python package and optional desktop graphical user interface (GUI) that packages these steps into a reusable workflow for ANSYS Fluent simulations. CFDTwin allows users to define simulation inputs and output quantities, generate design-of-experiments samples, run and resume Fluent batch simulations, train POD-NN surrogate models for scalar, surface-field, and cell-zone outputs, inspect validation metrics, and evaluate trained models at new design points without re-running Fluent. The same workflow is exposed through a scriptable Python API and a GUI, supporting reproducible studies, user-facing model validation, and automated design exploration. CFDTwin extends the prior POD-NN modeling study from a case-specific research implementation to a reusable research-software platform for CFD surrogate modeling and digital-twin development.

We propose a velocity sensor based on a two-memory Mach–Zehnder interferometer fed by a coherent probe and squeezed vacuum, read out by balanced homodyne detection. One memory is taken as a stationary reference, while the second memory moves during storage, so that its velocity is mapped onto a differential interferometric phase at readout. The two memories are otherwise assumed identical and are described by a Gaussian write–store–read lifetime together with the associated unconditional noise floor. Using the classical Fisher information, we derive the velocity sensitivity, the transmission threshold required for a target quantum gain, and the optimum storage time. The squeezed scheme improves on equal-resource coherent homodyne within an operating window set mainly by total transmission and phase stability. For representative near-term parameters, unconditional memory noise floors up to about $10^{-1}$ photons per trial do not by themselves remove the advantage; after optimization the improvement remains at the few-percent level and is limited chiefly by loss.

Strong light-matter coupling in optical waveguides provides a versatile platform for engineering hybrid polaritonic modes and their dispersion. Here we investigate multimode exciton-photon coupling in visible semiconductor waveguides supporting several transverse electric modes. Using rigorous coupled-wave analysis combined with a coupled-oscillator model, we show that the photonic band structure can be engineered across a range of regimes, from conventional multimode strong coupling to the superstrong coupling regime, where the Rabi splitting becomes comparable to the spacing between adjacent photonic modes. In the latter regime, hybridization between orthogonal electromagnetic modes is enabled by restricting the active material to a subregion of the mode volume where the photonic modes exhibit strong mutual overlap. This breaking of the orthogonality leads to polaritonic branches whose composition can be tuned among several photonic modes and the exciton. We demonstrate that small shifts of the exciton resonance produce pronounced changes in the propagation constants of different polariton branches, enabling exciton-controlled phase modulation through modal interference and, in the superstrong coupling regime, direct modal switching across a continuous S-shaped dispersion. The resulting figures of merit predict $\pi$ phase shifts for exciton energy shifts of only a few meV over propagation lengths of tens of micrometers, while larger shifts are still required for mode switching. These results establish multimode waveguide polaritons as a versatile platform spanning multiple coupling regimes, for compact phase and intensity control in integrated photonic architectures.

Hot-exciton relaxation in semiconductor nanocrystals (NCs) is often described using perturbative theories, but their accuracy is difficult to assess for realistic exciton–phonon Hamiltonians. Here, we benchmark the perturbative quantum master equation (QME) and several mixed quantum–classical (MQC) methods against fully quantum mechanical dynamics. Using atomistically parameterized models for CdSe core and CdSe/CdS core–shell NCs, we find that bare CdSe exhibits an ultrafast initial decay followed by slower cooling, whereas the core–shell system is dominated by the slower component. Analysis of reduced models shows that the ultrafast component arises from rapid diabatic state mixing driven by thermal fluctuations of low-frequency phonons, rather than from nuclear-assisted energy relaxation. The QME captures the initial fast decay but can fail for the slower relaxation in the diabatic representation, while the mapping approach to surface hopping (MASH) gives the most consistent agreement with both benchmark dynamics and equilibrium populations. These results establish a benchmark for exciton-cooling dynamics in NCs and clarify the physical regimes in which widely used approximate methods are reliable.

Due to its unique advantages, wakefield particle acceleration has been proposed as a promising pathway toward a 10 TeV collider. Several concepts, including Laser Wakefield Acceleration (LWFA), Plasma Wakefield Acceleration (PWFA), and Structure Wakefield Acceleration (SWFA), are being actively explored as potential approaches toward a 10 TeV collider. Each of these approaches requires particle sources (for the witness beam or for both drive and witness beams) with specific parameter sets to enable efficient wakefield acceleration. This work represents evaluation of existing and emerging particle generation technologies in the context of specific 10 TeV wakefield collider design requirements, with a particular focus on achievable brightness.

Light-based advanced manufacturing increasingly requires programmable, closed-loop tools that translate human design intent into executable operations at small length scales. Yet a key bottleneck persists across robotic and manufacturing modalities: turning user intent into machine-readable objectives that are reliably executable. While micro-robotics offers versatile manipulation via optical actuation of fluids, mathematically tractable goal specification remains manual and hard to reuse. Here, we introduce Speak-to-Objective, a modular agentic pipeline that uses a conditioned Large Language Model (LLM) to translate spoken or written commands into fully differentiable objective functions for assembling microparticles in a constraint-aware inverse solver (SLSQP) and on an experimental optofluidic platform. The approach employs a compact loop - perceive -> compose -> propose -> act -> report & learn - that treats the objective as the interface between intent and actuation, separating what to assemble or pattern from how to actuate, while learning from user feedback. The pipeline composes geometry, spacing, and assignment/topology terms to generate robust descriptive objectives that assemble from partial traces and recover after perturbations, as well as explicit objectives for precise placement, all in an actuator-agnostic fashion. Using laser-induced thermoviscous flows as the physical actuation modality, we demonstrate natural-language-programmable, light-based microscale assembly of particle patterns in a microfluidic environment. Beyond its immediate impact on programmable microassembly, and using laser-induced optofluidic actuation as a reduced-complexity experimental platform, our work points toward self-driving, AI-assisted optical manufacturing platforms in which natural language, differentiable objectives, and laser-based actuation are coupled into a reusable digital workflow.

Geometrically-resolved Direct Numerical Simulations of vertical-axis wind turbines are presented. Simulations were performed using the spectral/hp element method framework Nektar++ with a moving reference frame formulation. Three turbine geometries are considered with one, two and three blades. The full dynamic stall process is resolved, including the formation of large-scale vortices, the separation from the blade, and interaction with the near wake. Increasing blade number introduces blade-vortex interactions that interact with the dynamic stall process. For the three-bladed configuration, these interactions coincide with the early phase of dynamic stall during which the laminar separation bubble develops, and the resulting dynamic stall vortex is reduced in size. Further, the DNS outcomes identified a novel complementary mechanism, where direct vortex impingement causes the premature breakup of the dynamic stall vortices. With the dynamic stall vortex a defining feature of the VAWT near-wake, its accelerated breakdown removes the flow structures that distinguish the VAWT wake from that of a bluff body. Hence the near-wake transitions more rapidly towards bluff-body dynamics, with shear-layer-associated recovery. Self-similarity analysis is extended into the near-wake to quantify this transition, capturing the downstream rate at which the wake loses its dependence on blade-generated coherent structures and collapses onto a self-similar solution. Blade number is shown to be more influential than tip-speed ratio in setting the rate of this transition. The results have implications for closely-spaced turbine arrays and coupled-pair configurations, where the inflow experienced by a downstream rotor is shown to be blade-number-dependent.

In storage-ring-based light sources, harmonic cavities are commonly employed to lengthen the bunch, thereby mitigating collective effects and enhancing beam lifetime. However, this dual-RF configuration also introduces significant challenges, as the impedance of the fundamental modes can drive the beam into a longitudinal coupled-bunch instability regime. To mitigate this effect, low-level radio-frequency (LLRF) feedback is proposed to reduce the effective impedance experienced by the beam. This work investigates longitudinal beam dynamics in the PETRA-IV dual-RF system with normal-conducting cavities, explicitly accounting for the LLRF feedback loop. Both analytical modeling and numerical simulations are employed to characterize the onset and growth of coupled-bunch instabilities. The results show that, with appropriately defined LLRF specifications, the destabilizing influence of the cavity fundamental modes can be effectively suppressed, enabling stable operation of the storage ring at the design current. This work highlights the critical role of RF feedback systems in ensuring robust longitudinal stability, thereby supporting the realization of PETRA-IV's design goals and contributing to the broader development of next-generation synchrotron light sources, where high brilliance and operational reliability are essential.

Strong-field enhanced ionization (EI) is a phenomenon in which stretching of interatomic bonds into a distorted molecular geometry leads to an increase in the tunneling ionization rate driven by a strong field. While the nuclear dynamics leading to EI are well established, the electron dynamics of the ionization step remain largely unexplored. Isolating the momentum distribution of the electrons involved in EI is critical to fully characterizing the phenomenon. We have measured the electron momentum distribution associated with EI in triple ionization of D$_2$O using 6-fs pulse pairs together with full fragment momentum imaging and electron-ion correlation methods. We find that the EI electron momentum distribution differs substantially from that of standard strong-field tunneling from molecules, exhibiting an increased yield of electrons with large momentum in the direction of the laser polarization, and a change from the expected Gaussian distribution. These observations indicate that the instantaneous EI tunneling rate is maximized at a critical value of the laser electric field, rather than at the peak of an optical cycle. This finding distinguishes EI from Keldysh tunneling rate predictions, where tunneling rate increases monotonically with field strength. These pronounced differences between EI and non-EI electron spectra are critical tests of models of enhanced ionization and suggest a route towards control of the sub-cycle timing of electron emission.

We investigate how proximity to a metal nanostructure, particularly to a flat mirror or a cavity confined between two mirrors, affects the vibronic structure of Raman scattering signals. We find that such proximity, particularly for the strong-coupling situation encountered in cavity environments, plays multiple roles in shaping Raman signals beyond the now-familiar signal enhancement known as surface-enhanced Raman scattering (SERS). First, in analogy to the electromagnetic SERS mechanism, near or between mirrors, the local field experienced by a molecule differ from that in vacuum. In particular, between mirrors, the cavity enhances the effective excited state population by trapping the EM field inside it. Second, the nearby metal surface provides a relaxation channel and a lineshape broadening mechanism, and inside a cavity this lineshape is inherited by the cavity polaritons. This relaxation results in a loss of yield but the associated broadening also leads to significant absorption over a larger frequency range. Third, near metallic interfaces interference between incident and reflected light can lead to a richly structured Raman spectrum. For instance, we find that the Rabi contraction (that results from depopulating the ground state) can interfere with Raman signals (and the effect appears to be the same order as Raman itself). These cavity effects are calculated by a full-scale FDTD simulation and highlight the convoluted but fascinating roles of photonic materials on optical signals.

Quantum squeezed states of light can enhance measurement sensitivity beyond classical limits and enable quantum information processing, but scalable low-loss sources remain challenging. We demonstrate continuous-wave quantum squeezing on a chip, achieving 18 dB of squeezing and 20 dB of anti-squeezing at 1570 nm in a 1.6-cm traveling-wave adaptively poled thin-film lithium niobate waveguide. A distributed model independently determines facet losses, phase noise, and nonlinear interaction strength without prior assumptions, enabling rigorous inference of on-chip performance. We estimate a 95% confidence interval of [-18.96, -17.25] dB squeezing and [19.96, 21.35] dB anti-squeezing. These values represent the highest squeezing reported for any integrated photonic platform and the first assumption-free statistical validation of integrated squeezing performance. Our results establish thin-film lithium niobate as a high-performance, scalable platform for continuous-variable quantum sensing, communications, and photonic computing.

We develop and test a Lagrangian methodology for extracting finite-cloud geometry and reduced dynamics from particle trajectories. A cloud in a rough, incompressible, two-dimensional synthetic flow is represented by a mass/gyration ellipse describing the particle-weighted bulk and a minimum-area enclosing ellipse describing the outer envelope and its supporting particles. The homogeneous Gaussian–H"older velocity has finite Ornstein–Uhlenbeck memory, and the moving centroid is followed explicitly. The principal finding is unexpected: despite continual deformation by a non-smooth velocity field, the normalized aspect ratios of both ellipses reach broad, order-one distributions that are approximately stationary when conditioned on cloud scale. The mass ellipse is more elongated, whereas the enclosing ellipse spans a larger radial envelope and has the more scale-stable conditional shape distribution. To understand this saturation, we compare two finite-cloud descriptions of the velocity gradient. A gradient averaged over the enclosing ellipse predicts more aligned stretching than the particles experience. The resulting gradient correction supplies most of the negative contribution offsetting aligned stretching, while a smaller correction is specific to representing the evolving outer cloud by an enclosing ellipse. Finally, we demonstrate a data-to-model workflow. The measured time series is transformed to intrinsic variables, a constrained hierarchy of finite-lag stochastic models is fitted, and the models are compared on held-out realizations. The fitted coefficients and equations are specific to this synthetic experiment. The transferable contribution is the methodology: paired cloud geometries, finite-cloud coarse graining, intrinsic variables, transparent correction decomposition, and held-out validation for future simulations and experiments.

We present a theoretical description of atomic strong-field photoionization. Specifically, we consider an atom driven by a combination of two electromagnetic fields: a high-frequency field assisted by an intense, low-frequency laser. We investigate the photoelectron spectrum (PES) as the sum of two contributions: direct ionization due to the laser field and the photoionization term associated with the high-frequency field. We identify the contributions of above-threshold ionization (ATI) and laser-assisted photoemission (LAPE) structures in the total spectra, even when they overlap. As a particular case, we investigate the situation where an ATI-peak coincides with a sideband. Our theoretical scheme for the hydrogen initially in the 1s quantum state and based on strong field approximation, is general enough to be applied to other atomic species and field configurations.

Magic-angle twisted bilayer graphene Josephson junctions (MATBG-JJ) with a gate-tunable valley-polarized weak link exhibit an intrinsic Josephson diode effect originating from broken symmetries associated with valley polarization and band-structure anisotropy. Exploiting this nonreciprocal superconducting platform, we construct quantum Stirling (QSC), Otto (QOC), and Carnot (QCC) thermodynamic cycles, where the valley-polarization potential $\Delta_v$ acts as the principal control parameter, in contrast to conventional Josephson thermal machines driven by superconducting phase bias. We systematically compare the performance of MATBG-based Josephson diode thermal machines (MATBG-JDTM) with MATBG-based Josephson junction thermal machines (MATBG-JJTM) and AA-stacked bilayer graphene Josephson junction thermal machines (AABLG-JJTM). Owing to the flat-band-enhanced density of states and electrically tunable nonreciprocal transport in MATBG, both MATBG-JDTM and MATBG-JJTM exhibit significantly enhanced work output and efficiency over a broad operating regime compared to AABLG-JJTM. In particular, the gate-controlled MATBG-JDTM provides a flux-free alternative to conventional phase-driven architectures, mitigating limitations associated with magnetic-flux control and flux-noise effects. Our results establish MATBG Josephson diode platforms as a promising route toward electrically tunable quantum thermal machines and nonreciprocal superconducting caloritronics.

The natural electromagnetic modes spontaneously emitted by an atom in free space are spherical vector waves (SVWs). Each SVW mode is uniquely linked to a specific dynamical–spherical–multipole–moment of the atomic system. In this work, we introduce a general formalism for evaluating spherical multipole transition rates under different boundary conditions, considering the superposition between a given SVW and the modes resulting from the boundary conditions. This formalism is applied to study the radiative properties of an atomic system trapped near the focus of a 4$\pi$ optical array. By appropriately selecting the external light field, the juxtaposed lenses of the optical array allow the atom to be illuminated with approximate spherical vector waves. Explicit expressions for the resulting multipole transition rates are presented as a function of the numerical aperture of the lenses. The feasibility of enhancing and inhibiting electric dipole-forbidden transitions using such an array, under current experimental capabilities, is briefly discussed.