Papers

Integrals of the Liouville $1$-form, known as the first Poincaré integral invariant, provide a computable figure of merit for monitoring the conservation of symplecticity in the numerical integration of Hamiltonian systems. For smooth loop data, these integrals may be approximated with spectral convergence in the number of sample points, with rates limited by regularity. We devise a numerical integral invariant diagnostic for checking preservation of symplecticity in particle-in-cell (PIC) kinetic plasma simulation codes. As a first application of this diagnostic tool, we check the preservation of symplecticity in symplectic electrostatic particle-in-cell (PIC) methods. Surprisingly, such PIC methods fail to have symplectic time-advance maps if the charge is interpolated to the grid using linear shape functions, as is commonly done in practice. It is found that at least quadratic interpolation is needed to avoid this failure of symplecticity preservation.

Non-Markovian stochastic processes are ubiquitous in biology. Nevertheless, we lack a general framework for quantifying historical dependencies. In this Letter, we propose an information-theoretic approach to decompose history dependence in systems with non-Markovian dynamics, quantifying the information encoded in dependencies of each order. In minimal models of non-Markovian dynamics, we show that this framework correctly captures the underlying historical dependencies, even when autocorrelations do not. In prolonged recordings of fly behavior, we find that the scaling of non-Markovian dependencies is invariant across timescales from fractions of a second to minutes. Despite this invariance, the overall amount of non-Markovian information is non-monotonic, suggesting a unique timescale on which historical dependencies are strongest.

In this book chapter, we review how systems of simple motile agents can be used as a pathway to intelligent systems. It is a well known result from nature that large groups of entities following simple rules, such as swarms of animals, can give rise to much more complex collective behavior in a display of emergence. This begs the question whether we can emulate this behavior in synthetic matter and drive it to a point where the collective behavior reaches the complexity level of intelligent systems. Here, we will use a formalized notion of "intelligent matter" and compare it to recent results in the field of active matter. First, we will explore the approach of emergent computing in which specialized active matter systems are designed to directly solve a given task through emergent behavior. This we will then contrast with the approach of physical reservoir computing powered by the dynamics of active particle systems. In this context, we will also describe a novel reservoir computing scheme for active particles driven ultrasonically or via light refraction.

Accurately forecasting winter precipitation type and its transitions is critical for high-impact decision making. However, existing methods struggle in thermodynamically ambiguous regimes, and most do not quantify forecast uncertainty from a single model run. We developed an evidential neural network that predicts calibrated probabilities for four winter precipitation types (rain, snow, freezing rain, and ice pellets) along with epistemic uncertainty estimates at the computational cost of a standard neural network. The model was trained on quality-controlled and curated observations from the crowd-sourced mPING dataset paired with vertical thermodynamic profiles from the NOAA Rapid Refresh model analyses. Rigorous physical quality control removed thermodynamically implausible reports. Bulk evaluation against held-out mPING observations from June 2020 through June 2022 shows the ML model outperforms area-based deterministic methods in success ratio for freezing rain and ice pellets while maintaining comparable or better performance for rain and snow. A reduced freezing rain probability of detection reflects genuinely ambiguous thermodynamic environments rather than a uniform model deficiency and is more robustly represented through the full probability distribution than through the dominant predicted class alone. Thermodynamic regime analysis demonstrates that model prediction errors are physically structured and concentrated in interpretable regions of diagnostic space consistent with the known difficulty of freezing rain and ice pellet discrimination. We further demonstrate the model's physical consistency and operational utility through two contrasting mid-western U.S. winter storm case studies and an interactive visualization tool that enables dynamic interrogation of model predictions and uncertainty in real time.

We propose a one-dimensional viscoelastic seismic-wave model driven by the Atangana-BaleanuCaputo fractional derivative with a non-singular Mittag-Leffler kernel. A finite-difference discretization in space and an Adams-Bashforth-Moulton predictor-corrector scheme in time are used to compute solutions for several fractional orders. Simulations indicate that fractional memory alters both attenuation and dispersion, leading to non-exponential energy decay compared with the classical integer-order case.

A macroscopic reconstruction is developed to infer iRPC performance using Geant4 observables and one experimental anchor. The Geant4 energy deposition is used to estimate the primary ionization yield, while the efficiency turn-on is modeled through an induced-charge description encoded in an effective gain G(E). The absolute scale is fixed by calibrating the standard CMS mixture to its GIF++ efficiency curve and extracting macroscopic Townsend parameters (A,B). The same procedure is propagated to four alternative mixtures, including two HFO and CO2 eco-friendly blends, to reconstruct efficiency curves and working points, enabling detector mixture screening without microscopic transport inputs.

We propose a renormalization group (RG) approach to compare and collapse eigenvalue densities of random matrix models of complex systems across different system sizes. The approach is to fix a natural spectral scale by letting the model normalization run with size, turning raw spectra into comparable, collapsed density curves. We demonstrate this approach on generalizations of two classic random matrix ensembles–Wigner and Wishart–modified to have power-law variance profiles. We use random matrix theory methods to derive self-consistent fixed-point equations for the resolvent to compute their eigenvalue densities, we define an RG scheme based on matrix decimation, and compute the Beta function controlling the RG flow as a function of the variance profile power-law exponent. The running normalization leads to spectral collapse which we confirm in simulations and solutions of the fixed-point equations. We expect this RG approach to carry over to other ensembles, providing a method for data analysis of a broad range of complex systems.

Holographic beam shaping is a powerful approach for generating individually addressable optical spots for controlling atomic qubits, such as those in trapped-ion quantum processors. However, its application in qubit control is limited by residual intensity crosstalk at neighboring sites and by a nonzero background floor in the far wings of the addressing beam, leading to accumulated errors from many exposed qubits. Here, we present an all-optical scheme that mitigates both effects using a single digital micromirror device (DMD) operated in a double-pass configuration, in which light interacts with two separate regions of the same device. In the first pass, one region of the DMD is placed in a Fourier plane and implements a binary-amplitude hologram for individual addressing, while in the second pass a different region serves as a programmable intermediate image-plane aperture for spatial filtering. By multiplexing the Fourier-plane hologram to include secondary holograms, we generate weak auxiliary fields that interfere destructively with unwanted light at selected sites, while image-plane filtering suppresses the residual tail at larger distances. Together, these techniques maintain relative intensity crosstalk at or below $10^{-5}$ ($-50\,\mathrm{dB}$) across the full field of view relevant for qubit addressing, and further reduce the far-wing background to approximately $10^{-6}$ at large distances from the addressed qubit, approaching the detection limit. These results provide a compact, DMD-based solution for low-crosstalk optical holographic qubit addressing that is directly applicable to trapped ions and other spatially ordered quantum systems.

This paper presents a comprehensive analysis of the link budget for free-space optical systems involving Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites. We develop a detailed model of the satellite-to-ground channel that accounts for the primary physical processes affecting transmittance: atmospheric absorption and scattering, free-space diffraction, and turbulence-induced fluctuations. The study introduces a general method for computing transmittance along a slant path between a satellite and an optical ground station, incorporating zenith angle, slant range, and altitude-dependent attenuation. The proposed framework is intended to support the design and evaluation of space-based optical links and serves as a critical tool for defining technical specifications in satellite communication demonstrators and simulations. Numerical estimates are provided to illustrate the magnitude of losses under typical operational conditions, including the role of aperture averaging. In addition to the link budget analysis, we introduce a satellite-based quantum use case. We propose a scheme for quantum state tomography performed on states generated by an onboard photon source on an LEO or MEO satellite and transmitted to the optical ground station. This approach enables continuous verification of the quality of quantum resources that can be used to perform quantum protocols within quantum information networks.

We use total energy-momentum conservation and the Bianchi identity (magnetic-flux conservation) to construct second-order relativistic magnetohydrodynamics in a Zubarev's non-equilibrium statistical operator (NESO) framework. We obtain all dissipative tensors in the medium by focusing on a relativistic magnetized plasma that preserves parity and is symmetric to charge-conjugation. We also provide Kubo formulas for all transport coefficients that arise at second order. Moreover, we extend the NESO formalism to systematically take into account for nonlocal contributions.

Machine learning-based anomaly detection methods are able to search high-dimensional spaces for hints of new physics with much less theory bias than traditional searches. However, by searching in many directions all at once, the statistical power of these search strategies is diluted by a variant of the look elsewhere effect. We examine this challenge in detail, focusing on weakly supervised methods. We find that training and testing on the same data results in badly miscalibrated $p$-values due to the anomaly detector searching everywhere in the data and overfitting on statistical fluctuations. However, if these $p$-values can be calibrated, they may offer the best sensitivity to anomalies, since this approach uses all of the data. Conversely, training on half of the data and testing on the other half results in perfectly calibrated $p$-values, but at the cost of reduced sensitivity to anomalies. Similarly, regularization methods such as early stopping can help with $p$-value calibration but also possibly at the expense of sensitivity. Finally, we find that k-folding strikes an effective balance between calibration and sensitivity. Our findings are supported by numerical studies with Gaussian random variables as well as from collider physics using the LHC Olympics benchmark anomaly detection dataset.

Direct numerical simulation (DNS) of turbulent reactive flows has been the subject of significant research interest for several decades. Accurate prediction of the effects of turbulence on the rate of reactant conversion, and the subsequent influence of chemistry on hydrodynamics remain a challenge in combustion modeling. The key issue in DNS is to account for the wide range of temporal and spatial physical scales that are caused by complex interactions of turbulence and chemistry. In this work, a new computational methodology is developed that is shown to provide a viable alternative to DNS. The framework is the matrix product state (MPS), a form of tensor network (TN) as used in computational many body physics. The MPS is a well-established ansatz for efficiently representing many types of quantum states in condensed matter systems, allowing for an exponential compression of the required memory compared to exact diagonalization methods. Due to the success of MPS in quantum physics, the ansatz has been adapted to problems outside its historical domain, notably computational fluid dynamics. Here, the MPS is used for computational simulation of a shear flow under non-reacting and nonpremixed chemically reacting conditions. Reductions of 30% in memory are demonstrated for all transport variables, accompanied by excellent agreements with DNS. The anastaz accurately captures all pertinent flow physics such as reduced mixing due to exothermicity & compressibility, and the formation of eddy shocklets at high Mach numbers. A priori analysis of DNS data at higher Reynolds numbers shows compressions as large as 99.99% for some of the transport variables. This level of compression is encouraging and promotes the use of MPS for simulations of complex turbulent combustion systems.

Molecular simulations are widely regarded as leading candidates to demonstrate quantum advantage–defined as the point at which quantum methods surpass classical approaches in either accuracy or scale. Yet the qubit counts and error rates required to realize such an advantage remain uncertain; resource estimates for ground-state electronic structure span orders of magnitude, and no quantum-native method has been validated at a commercially relevant scale. Here we address this uncertainty by executing the iterative qubit coupled-cluster (iQCC) algorithm, designed for fault-tolerant quantum hardware, at unprecedented scale using a quantum solver on classical processors, enabling simulations of transition organo-metallic complexes requiring hundreds of logical qubits and millions of entangling gates. Using this approach, we compute the lowest triplet excited state (T$_1$) energies of Ir(III) and Pt(II) phosphorescent organometallic compounds and show that iQCC achieves the lowest mean absolute error (0.05 eV) and highest R$^2$ (0.94) relative to experiment, outperforming leading classical methods. We find these systems remain classically tractable up to $\sim$200 logical qubits, establishing the threshold at which quantum advantage in computational chemistry may emerge and clarifying resource requirements for future quantum computers.

Chiral photonic metasurfaces provide unique capabilities for tailoring light-matter interactions, which are essential for next-generation photonic devices. Here, we report an advanced optimization framework that combines deep learning and evolutionary algorithms to significantly improve both the design and performance of chiral photonic nanostructures. Building on previous work utilizing a three-layer perceptron reinforced learning and stochastic evolutionary algorithm with decaying changes and mass extinction for chiral photonic optimization, our study introduces a refined pipeline featuring a two-output neural network architecture to reduce the trade-off between high chiral dichroism (CD) and reflectivity. Additionally, we use an improved fitness function, and efficient data augmentation techniques. A comparative analysis between a neural network (NN)-based approach and a genetic algorithm (GA) is presented for structures of different interface pattern depth, material combinations, and geometric complexity. We demonstrate a twice higher CD and the impact of both the corner number and the refractive index contrast at the example of a GaP/air and PMMA/air metasurface as a result of superior optimization performance. Additionally, a substantial increase in the number of structures explored within limited computational resources is highlighted, with tailored spectral reflectivity suggested by our electromagnetic simulations, paving the way for chiral mirrors applicable to polarization-selective light-matter interaction studies.

The management of thermal effects in plasmonic nanostructures is frequently viewed as a detrimental waste rather than a useful, controllable entity. We show that optical coupling of plasmonic nanoparticles enables precise spatiotemporal control over nanoscale heating. Through numerical investigation of experimentally-achievable systems from individual nanoparticles and dimers to nanolenses and switchable clusters, we demonstrate how plasmon hybridization and near-field coupling dictate the magnitude and spatial distribution of temperature. Our results highlight the critical role of polarization and gap distance in tuning the thermal output of dimers, the ability of a trimer nanolens to focus heat into a sub-diffraction volume, and the pronounced thermal difference in a switchable nanoparticle cluster. This work establishes a framework for designing advanced thermoplasmonic systems where heat is not merely a detriment, but a dynamically controllable element for applications in catalysis, health, or active photonic devices.

Above the magnitude of completeness - the minimum threshold for which a 100% detection rate is assumed - earthquake magnitudes are typically modeled as a continuous exponential distribution. In practice, however, earthquake catalogs report magnitudes with finite resolution, resulting in a discrete (geometric) distribution. To determine the magnitude of completeness, the Lilliefors test is commonly applied. Because this test assumes continuous data, it is standard practice to add uniform noise to binned magnitudes prior to testing exponentiality. Here we show analytically that uniform dithering does not recover the underlying continuous exponential distribution from its discretized (geometric) form. It instead returns a piecewise-constant residual lifetime distribution, whose deviation from the exponential model becomes detectable as catalog size or bin width increases. Through numerical experiments, we demonstrate that this deviation yields a systematic overestimation of the magnitude of completeness, with biases exceeding one magnitude unit in large, high-resolution catalogs. We derive the exact noise distribution - a truncated exponential within each magnitude bin - that correctly restores the continuous exponential distribution over the whole magnitude range. Numerical tests show that this correction yields Lilliefors rejection probabilities that are consistent with the significance level across a wide range of bin widths and catalog sizes. Although illustrated for the Lilliefors test, the identified bias and the proposed correction are independent of the specific statistical test and apply generally to exponentiality testing of discretized magnitude data.

We present a proof-of-concept study showing that buckled aluminized polyimide films perforated with millimeter-scale cuts can redirect normally incident light obliquely and generate net in-plane force components parallel to the global solar sail surface. We use finite element simulations to obtain the buckled shapes of different periodic unit cell geometries and apply ray optics modeling to compute the resulting light-pressure forces. The simulations show that the buckled kirigami surfaces reflect light into different directions producing a net in-plane force parallel to the direction of stretching. We verify these trends experimentally by illuminating a tensioned kirigami sample with a laser and observing reflected beam patterns consistent with the ray optics simulations. These results suggest that kirigami films may offer a scalable, low-power, and lightweight way to achieve controllable in-plane forces for solar sail steering.

We investigate the broadband turbulent dynamics of attached and separated flows over a Gaussian bump, focusing on the origin of low-frequency coherent structures. The analysis combines time-resolved experimental measurements with physics-based linear models, using mean fields previously assimilated from the same dataset as base flows. Spectral proper orthogonal decomposition reveals coherent dynamics in low- and medium-frequency regimes for both flows, with the low-frequency dynamics being substantially stronger in the separated case. In the separated flow, these dynamics are linked to a three-dimensional zero-frequency modal instability that generates large-scale streamwise-elongated structures downstream of the bump. A standing-wave model based on resolvent modes, incorporating finite-span effects, reproduces the experimentally observed spanwise structure of the dynamics and highlights the limitations of simulations with small spanwise extent and periodic boundary conditions. In the attached flow, similar low-frequency structures are identified. These are weaker, do not form a prominent standing-wave pattern, and cannot be definitively classified as either modal or non-modal. The three-dimensional zero-frequency instability and finite-span standing-wave dynamics are identified as the main drivers of low-frequency coherent structures in the separated flow. They offer an explanation for persistent discrepancies between simulations and experiments on the Gaussian bump, and provide guidance on spanwise domain size and boundary conditions for future simulations.

Free energy calculations are widely used tools in computational chemistry, but their dependence on the assignment of partial charges during force field parametrization reduces their accuracy and reproducibility. In this work, we highlight the direct connection between the low accuracy of AM1-BCC charges on polar species and the poor accuracy of corresponding hydration free energy calculations. We then propose an XGBoost regressor trained on atomic descriptors to rapidly predict charges obtained with high-fidelity density functional theory calculations at PBE0-D3(BJ)/def2-TZVP level. The more accurate electrostatic description results in more reliable free energy calculations than those obtained with semi-empirical AM1-BCC charges. Finally, we leverage this predictive model in combination with a 1 ns gas-phase molecular dynamics simulation to propose the Boltzmann Percentile method for assigning charges representative of the conformational ensemble of a molecule. Charges obtained with this method are robust to different input conformations, and the resulting free energies, calculated on a subset of the FreeSolv dataset, show a root mean squared error of 1.69 kcal/mol against the 3.05 kcal/mol obtained with semi-empirical charges as well as a significantly better ranking. Our method is easily integrable in the traditional workflow and requires the same computational resources. These two aspects make it a realistic tool for enhancing already expensive free energy calculations, and more in general, molecular dynamics simulations in condensed phase.

Space-time modulation of refractive index can produce synthetically moving interfaces with arbitrary apparent velocities, including superluminal motion, offering new ways to control light in dynamic media. On the other hand, space-time wave packets are structured waves whose spatio-temporal spectra lie on tilted space-time planes, so their group velocity can be programmed, including superluminal values, even in a uniform medium. Here we develop a general theory of space-time refraction for such structured waves at a planar moving interface and show how a single boundary reshapes their velocity content. By identifying the invariants of a translating boundary, we obtain refraction laws for baseband, X-wave, and sideband packets that apply for arbitrary interface velocities and connect smoothly to static and purely temporal limits. These laws reveal regimes of "space-time anomalous optical push broom," where a moving interface compresses a wide range of incident velocities into a narrow transmitted band, and "velocity spectral optical fission," where an incident X-wave splits into two propagation-invariant branches with distinct velocities. The combined freedom to prepare waves with superluminal group velocity and to prescribe equally unconstrained interface speeds points toward reconfigurable time gating, optical buffering, velocity multiplexing, and controlled emission in moving media, and provides a route to photonic settings capable of emulating dynamical effects traditionally associated with gravitational or quantum processes.

We present a detailed study of common intrinsic defects in CuInS$_2$ and CuGaS$_2$ using the Heyd, Scuseria and Ernzerhof (HSE) hybrid functional scheme. The impact of the two HSE parameters, $\alpha$ and $\omega$ on the band gap and compliance with the generalized Koopmans' theorem is investigated. Using the formation energy formalism and calculated thermodynamic charge-transition levels, we assess the electronic properties of the defects and explore the connection of charge-transition levels with optical-transition levels. Calculated Franck-Condon shifts for emission highlight the importance of lattice relaxation for the attribution of defects to luminescence peaks. Our results show that once these effects are included, predictions become closer to photoluminescence measurements available in literature.

Recent topological tools offer a powerful way to classify how phases of nonlinear bosonic resonators are organized. Yet, they remain incomplete. In particular, self-sustained oscillations in the form of limit cycles act as robust organizing centers in phase space that are not captured by existing fixed-point-based approaches. In this work, we extend the flow topology framework for nonlinear resonators to include limit cycles as fundamental topological elements. Using a graph-based construction, we show how periodic attractors impact the global connectivity of phase-space flows. We illustrate the approach with a minimal nonlinear Van der Pol resonator model, where limit cycles coexist with stationary points. Our results provide a unified topological description of stationary and time-periodic phases in nonlinear bosonic systems, with direct relevance to photonic, superconducting, and optomechanical platforms, and raise new questions on synchronization and the extension of flow topology to the quantum regime.

In this work, we present the second version of the Donostia Natural Orbital Functional Software, an open-source program for natural orbital functional calculations. The new release incorporates improved optimization algorithms, capabilities for excited-state computations, support for ab initio molecular dynamics, and integration with the libcint library. DoNOF 2.0 also extends its property toolbox by enabling the evaluation of nonlinear optical responses, including static polarizabilities and higher-order hyperpolarizabilities via a finite-field Romberg-Richardson scheme. Program Summary [Title: DoNOF; Developer's repository link: http://github.com/DoNOF/; Program's Manual link: https://donof.readthedocs.io/; Licensing provisions: GPLv3; Programming language: Fortran; additional implementations available in Python (PyNOF) and Julia (DoNOF.jl); Multinode capability: Support for distributed execution through a hybrid OpenMPI and OpenMP implementation]

We experimentally demonstrate laser-driven deposition of single-walled carbon nanotubes (CNTs) in suspended core fibers (SCFs) for the first time. Two samples for each SCF type with three (SCF1) and four (SCF2) air holes are individually inserted in a syringe loaded with a 0.5 mL solution of CNTs dispersed in methanol, and a high-power laser at 980 nm is injected into the fibers during distinct periods: SCF1 (20 and 30 minutes), SCF2 (5 and 10 minutes). The CNT solution thermally expands, accelerating and depositing nanotubes on the fiber tip. The material and dimensions of the CNTs deposited on the SCFs' cross-section are characterized, revealing nanotube bundles increasing in thickness from the fiber core to the bridges and inside the holes. The average maximum CNT thickness widening the bridge edges is, respectively, for SCF1 (2.8 and 2 microns) and SCF2 (1.7 and 1.3 microns). These achievements are significant for nanoparticle deposition and fabrication of microscale devices on tips and inside specialty and hollow fibers, enabling innovative fiber sensors, mode-locked fiber lasers, in-fiber acousto-optic modulators, and broadband photoacoustic transmitters for imaging and neurostimulation.

An adaptive sampling approach for efficient detection of bifurcation boundaries in parametrized fluid flow problems is presented herein. The study extends the machine-learning approach of Silvester~(J. Comput. Phys., 553 (2026), 114743), where a classifier network was trained on preselected simulation data to identify bifurcated and nonbifurcated flow regimes. In contrast, the proposed methodology introduces adaptivity through a flow-based deep generative model that automatically refines the sampling of the parameter space. The strategy has two components: a classifier network maps the flow parameters to a bifurcation probability, and a probability density estimation technique (KRnet) for the generation of new samples at each adaptive step. The classifier output provides a probabilistic measure of flow stability, and the Shannon entropy of these predictions is employed as an uncertainty indicator. KRnet is trained to approximate a probability density function that concentrates sampling in regions of high entropy, thereby directing computational effort towards the evolving bifurcation boundary. This coupling between classification and generative modeling establishes a feedback-driven adaptive learning process analogous to error-indicator based refinement in contemporary partial differential equation solution strategies. Starting from a uniform parameter distribution, the new approach achieves accurate bifurcation boundary identification with significantly fewer Navier–Stokes simulations, providing a scalable foundation for high-dimensional stability analysis.

Enhanced lithological interpretation from well logs plays a key role in geological resource exploration and mapping, as well as in geo-environmental modeling studies. Core and cutting information is useful for making sound interpretations of well logs; however, these are rarely collected at each depth due to high costs. Moreover, well log interpretation using traditional methods is constrained by poor borehole conditions. Traditional statistical methods are mostly linear, often failing to discriminate between lithology and rock facies, particularly when dealing with overlapping well log signals characterized by the structural and compositional variation of rock types. In this study, we develop multiple supervised and unsupervised machine learning algorithms to jointly analyze multivariate well log data from Integrated Ocean Drilling Program (IODP) expeditions 390 and 393 for enhanced lithological interpretations. Among the algorithms, Logistic Regression, Decision Trees, Gradient Boosting, Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP) neural network models, the Decision Tree and Gradient Boosting models outperformed the others, achieving an accuracy of 0.9950 and an F1-score of 0.9951. While unsupervised machine learning (ML) provides the foundation for cluster information that inherently supports the classification algorithm, supervised ML is applied to devise a data-driven lithology clustering mechanism for IODP datasets. The joint ML-based method developed here has the potential to be further explored for analyzing other well log datasets from the world's oceans.

Purpose: To develop DarkSPARC, a retrospective, training-free, self-calibrated spectral reconstruction method that converts routine bright-blood 3D left atrial (LA) late gadolinium enhancement (LGE) MRI into a dark-blood image, and to quantify its impact on LA scar-pool CNR, SNR, effective CNR (eCNR), and scar quantification accuracy. Methods: DarkSPARC embeds bright-blood LA LGE into a calibrator-conditioned (N+1)-dimensional spectral domain and reconstructs a dark-blood-like image using scan-specific spectral landmarks. A scan-specific 3D numerical phantom framework was built from LAScarQS post-ablation LGE by cloning remote myocardium into the LA wall and imposing controlled scar burden. Five baseline cases spanning the 5th-95th percentiles of native scar-pool CNR, each with multiple scar burdens and 10 CNR degradation levels, yielded 200 phantoms. For every phantom, LA scar-pool CNR, SNR, eCNR, and Scar% were measured on bright-blood and DarkSPARC images. In vivo performance was evaluated in 60 public post-ablation scans of atrial fibrillation patients. Results: In scan-specific phantoms, DarkSPARC increased LA scar-pool CNR, SNR, and eCNR over bright-blood in all 200 experiments, with DarkSPARC/bright-blood ratios up to about 30-fold for CNR and about 6-fold for SNR in the lowest-CNR conditions. At 70% CNR degradation, bright-blood underestimated ground-truth LA Scar% by -37% to -54%, whereas DarkSPARC reduced bias to about -3% to -5%. In vivo, DarkSPARC similarly improved metrics: median scar-pool CNR, SNR, and eCNR increased from 20.0 to 135.9 (6.8x), 70.6 to 200.6 (2.8x), and 0.22 to 0.75 (3.4x), respectively (all p<0.001), and LA Scar% increased from 3.9% to 9.75%. Conclusion: DarkSPARC is a self-calibrated, training-free reconstruction that yields dark-blood 3D LA LGE, boosting CNR/SNR/eCNR and stabilizing reliable scar quantification without extra scans.

We study the decay of a dense, ultracold sample of weakly bound DyK dimers stored in an optical dipole trap. Our bosonic dimers are composed of the fermionic isotopes $^{161}$Dy and $^{40}$K, which is of particular interest for experiments related to pairing and superfluidity in fermionic systems with mass imbalance. We have realized dipole traps with near-infrared laser light in four different wavelength regions between 1050 and 2002 nm. We have identified trap-light-induced processes as the overall dominant source of losses, except for wavelengths around 2000 nm, where light-induced losses appeared to be much weaker. In a trap near 1550 nm, we found a plateau of minimal light-induced losses, and by carefully tuning the wavelength, we reached conditions where losses from inelastic collisions between the trapped dimers became observable. For very weakly bound dimers close to the center of a magnetically tuned Feshbach resonance, we demonstrate the Pauli suppression of collisional losses by about an order of magnitude.

The question of how to interpret and compute arrival-time distributions in quantum mechanics remains unsettled, reflecting the longstanding tension between treating time as a quantum observable or as a classical parameter. While recent studies have primarily contrasted arrival-time predictions across different interpretations of quantum mechanics, here we investigate this ambiguity within the standard quantum-mechanical framework itself. Moreover, most previous studies have focused on the single-particle case in the far-field regime, where these distinct approaches yield very similar arrival-time distributions and a semi-classical analysis typically suffices. Recent advances in atom-optics technologies now make it possible to experimentally investigate arrival-time distributions for entangled multi-particle systems in the near-field regime, where a deeper analysis beyond semi-classical approximations is required. Even in the far-field regime, due to quantum non-locality, the semi-classical approximation cannot generally hold in multi-particle systems. Therefore, in this work, two fundamental approaches to the arrival-time problem—namely, the time-parameter and time-operator approaches—are extended to multi-particle systems. Using these extensions, we propose a feasible two-particle arrival-time experiment and numerically evaluate the corresponding joint distributions. Our results reveal regimes in which the two approaches yield inequivalent predictions, highlighting conditions under which experiments could shed new light on distinguishing between competing accounts of time in quantum mechanics. Our findings also provide important insights for the development of quantum technologies that use entanglement in the time domain, including non-local temporal interferometry, temporal ghost imaging, and temporal state tomography in multi-particle systems.

This paper presents prototype testing and a control allocation algorithm for the Cable-Actuated Bio-inspired Lightweight Elastic Solar Sail (CABLESSail) concept aimed at performing momentum management of a solar sail. CABLESSail uses actuated cables routed along the structural booms of the solar sail to control the shape of the solar sail and changes the solar radiation pressure disturbance torques acting on it. Small-scale prototype tests of CABLESSail are presented in this paper, which demonstrate the effectiveness of cable actuation on deployable booms. A novel control allocation method is also presented in this paper that provides a computationally-efficient manner to determine the deformations required in each of the structural booms to impart the desired momentum management torque on the solar sail. Numerical simulation results with the proposed algorithm demonstrate robustness to uncertainty in the shape of the sail membrane, resulting in reliable generation of momentum management torques that exceed or meet the capabilities of state-of-the-art solar sail actuators. Both the prototype tests and control allocation methods presented in this paper represent key steps in raising the technology readiness level of the CABLESSail concept.