Medical Physics · Cross-listed from eess.IV
A Foundational EDM2-Based Generative Model for High-Resolution Synthetic Fetal Ultrasound Imaging from Open Datasets
Harvey Mannering, Yilin Zhang, Ziao Liu et al. (+3 more)
Prenatal ultrasound imaging is key for assessing fetal health, but AI progress is limited by scarce,
privacy-restricted, and hard-to-annotate
datasets. We propose a high-resolution fetal ultrasound synthesis framework based on the EDM2
diffusion architecture, trained on multiple public
datasets to generate 512x512 images across six anatomical classes. Our method achieved improved image quality with lower FID scores and enhanced downstream fetal plane classification, reaching 93.36% ensemble accuracy after fine-tuning, surpassing real-data-only training. Clinical evaluation by an experienced fetal ultrasound specialist (10+ years) on 100 images yielded a mean realism score of 2.67/5, with real images rated higher than synthetic. Artefacts included smoothing, speckle irregularities, and anatomical inconsistencies. Code, data, models and other resources to reproduce this work are available at https://github.com/xfetus/fetal-ultrasound-edm2.
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All authors Harvey Mannering, Yilin Zhang, Ziao Liu, Zhiwu Huang, Jacqueline Matthew, Miguel Xochicale
Biological Physics
Toward a Dynamical Taxonomy of Insomnia: A Multiaxial Framework for Sleep-State Transitions and Architectural Failure
Alexander Poltorak
Insomnia disorder is defined at the syndrome level, yet similar complaints can arise from different abnormalities in sleep regulation, state transition, state stabilization, spatial recruitment, architectural sequencing, and state perception. We propose a multiaxial dynamical framework whose principal contribution is organizational: a candidate profile is specified by the dynamical operation that fails, the sleep stage or boundary at which it fails, and its causal status. Objective sleep duration, age, circadian phase, comorbidity, medication exposure, and night-to-night variability are modifier/
covariate dimensions rather than additional mechanistic classes. A local Landau-Ginzburg formalism, adopted from prior cortical and sleep-dynamics work, supplies a phenomenological
language for nested hypotheses. Its relaxational form applies only to boundary-local dynamics under an approximate gradient description; non-gradient escape requires an action or quasipotential treatment, and whole-night REM-NREM sequencing requires reactive or oscillatory dynamics. Routine polysomnography usually identifies effective combinations rather than
curvature, escape action, bias, noise, and relaxation separately. The strongest boundary-level evidence concerns sleep onset, where published results are consistent with bifurcation-like or bistable dynamics but do not yet exclude a driven smooth transition produced by the homeostatic-circadian ramp. The remaining operation classes are hypothesis-generating extensions. The taxonomy is judged by pragmatic utility, including improved communication, stratification, and prediction; the scalar-field implementation and specific dynamical profiles are separately falsifiable. The framework is a phenomenological organizing model rather than a new diagnosis, validated biomarker, or treatment-selection system.
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General Physics
Moving Magnetic Flux and Electromagnetic Induction
Allan Walstad
Magnetic flux can move, and moving
magnetic flux is associated with induced electric fields. Where there are multiple sources of flux, the induced electric field is the vector resultant of the induced fields attributable to the fluxes acting individually; it is not necessarily associated with the resultant
magnetic field. The key insight is that flux can move through regions in which the
magnetic field itself vanishes, much as electric charge can move (as electric current) through a region in which the net charge density is zero. For this reason, moving flux can account for the induced emf around a long solenoid in which the current is changing. It also accounts for unipolar induction, and this account is testable.
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Plasma Physics · Cross-listed from nlin.CD
A Symplectic Map Approach to Magnetic Field-Line Dynamics in Tokamaks
Diego F. M. Oliveira and Edson D. Leonel
Magnetic field-line transport in tokamaks is governed by the interplay between chaotic dynamics and invariant phase-space structures that act as partial transport barriers. We investigate the conservative Tokamap, an exact
symplectic mapping for
magnetic field-line dynamics, using a unified geometrical, dynamical, and statistical framework. Phase-space portraits and Lyapunov exponents characterize the mixed
Hamiltonian dynamics, while ensemble-averaged transport exhibits universal dynamic scaling with growth, crossover, and saturation regimes connected through a homogeneous scaling theory. Poincaré recurrence statistics reveal that decreasing the
magnetic-shear parameter systematically slows transport, with the characteristic transport time following the algebraic scaling $\tau_c\propto x_q^{-0.213}$. This behavior reflects increasingly dominant stickiness associated with KAM islands, resonance chains, and cantori. Our results establish a direct connection between the geometrical organization of
Hamiltonian phase space and macroscopic transport properties, providing a comprehensive framework for understanding long-time
magnetic field-line transport in tokamaks and other
Hamiltonian systems with mixed phase space.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
Development of next-generation event-driven x-ray hybrid CMOS detectors
Timothy R. Emeigh, Abraham D. Falcone, Joseph M. Colosimo et al. (+14 more)
The Penn State University High Energy Astrophysics Detector and Instrumentation Lab, in collaboration with Teledyne Imaging Sensors, has developed a next-generation event-driven X-ray hybrid CMOS detector. This detector is an HCD with a 1,024x1,024 array of 21-micron pitch pixels, featuring a comparator in each pixel to enable event-driven readout. While the full frame operation of this detector can reach readout speeds of up to 150 Hz, this event-driven operation allows readout of only those pixels that surpass a user-set charge threshold, enabling effective rates of up to 10 kHz. Here we report on the past detector development efforts at Penn State and describe these new detectors and expected performance improvements.
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All authors Timothy R. Emeigh, Abraham D. Falcone, Joseph M. Colosimo, Kadri M. Nizam, Lukas R. Stone, Md. Arman Hossen, Laurel ONeill, Ian Ashcroft, Kyle Bonene, Brynn Bortree, Zachary E. Catlin, Sierra Deppe, Killian M. Gremling, Gavin Houlihan, Katie McWhirter, David M. Palmer, Abigail A. Raytsis
Computational Physics
Multiphysics tritium transport modelling of the ARC breeding blanket with FESTIM
James Dark, Arpan Sircar, Jin Whan Bae et al. (+2 more)
Accurate prediction of tritium behaviour in molten salt breeding blankets is essential for the design and safe operation of ARC-class fusion reactors. This work presents a fully open-source, component-scale multiphysics framework for modelling tritium transport in an ARC
liquid immersion blanket.
Neutron transport, thermal hydraulics, and hydrogen isotope transport are coupled using OpenMC, OpenFOAM, and FESTIM, leveraging dedicated tools enabling direct transfer of spatially resolved fields between solvers. Assuming a zero inlet concentration, steady-state simulations predict a total tritium inventory of approximately 243 mg, with the blanket reaching steady-state tritium throughput within approximately 30 min, which is of a similar order to previous system-level estimates. The results show that tritium transport is dominated by
turbulence-enhanced
diffusion, with strong localisation in flow stagnation regions and reduced accumulation in highly turbulent zones. Sensitivity analyses indicate that predicted inventories are governed primarily by the numerical stabilisation scheme, with only a modest dependence on the turbulent Schmidt number. The proposed workflow provides a transparent and extensible basis for high-fidelity analysis of tritium transport in ARC-class breeding blankets.
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All authors James Dark, Arpan Sircar, Jin Whan Bae, Jørgen S. Dokken, Rémi Delaporte-Mathurin
Chemical Physics
Next Generation of Ultra-Coarse-Graining: Self-Consistent Inference of Critical Internal States
Weizhi Xue, Xiao Shuai and Gregory A. Voth
Bottom-up coarse-graining expands the length and time scales accessible to molecular dynamics (MD) simulations, but information loss can hinder accurate representation of multistate phenomena in complex biomolecular dynamics. Ultra-Coarse-Graining (UCG) projects discrete "
quantum-like" extended degrees of freedom, or "internal states," onto coarse-grained (CG) molecules, extending CG model expressiveness. The rapid-local-
equilibrium (RLE) approximation in UCG depends on user-defined collective variables (CVs, e.g., local density) and neglects correlations between internal states within and between CG molecules. We present Self-Consistent UCG (SC-UCG), which uses the underlying UCG interactions directly to assign internal states without designing CVs in the CG ensemble. During simulation, internal state probabilities are determined self-consistently through graph message passing. We enhance the RLE
Hamiltonian with the Bethe approximation and AI-based inference to represent explicit correlations between UCG beads. For force-field training, we develop Multilayer Internal State Consistency (MISC), a machine-learning method derived from relative
entropy minimization that avoids iterative sampling of intermediate force fields. We apply SC-UCG to a tetramer exhibiting a second-order
symmetry-breaking phase transition from a supercritical racemic fluid to subcritical D-rich and L-rich fluids. SC-UCG captures collective switching of internal states in the subcritical region and recapitulates the phase transition across temperatures, despite being trained on a single-temperature
dataset.
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Optics
High Spectral Energy Density All-Fiber Nanosecond Pulsed 1.7 $\mu$m Light Source for Photoacoustic Microscopy
Seongjin Bak, Sang Min Park, Yuon Song et al. (+7 more)
We present a high spectral energy density all-fiber nanosecond pulsed 1.7 $\mu$m light source specifically designed for photoacoustic microscopy (PAM). The system targets the first overtone absorption of C-H bonds near 1720 nm within the near-infrared-III (NIR-III) window, where lipids exhibit strong optical absorption and tissues benefit from reduced scattering and high permissible fluence. To achieve narrow linewidth, high pulse energy, and high pulse repetition rate (PRR), we developed a master oscillator fiber amplifier architecture based on stimulated Raman scattering. A 1589.80 nm Raman pump and a custom-built narrow-linewidth Raman seed
laser were employed to generate spectrally pure 1719.44 nm pulses with an approximately 0.10 nm linewidth. The proposed light source delivers nanosecond pulses of approximately 5 ns with high pulse energy of at least 2.2 $\mu$J and tunable PRRs up to 300 kHz, resulting in a spectral energy density of approximately 22 $\mu$J/nm, which is significantly higher than that of conventional 1.7 $\mu$m light sources. The performance of the NIR-PAM system was validated through resolution testing with a 1951 USAF target, demonstrating a spatial resolution of approximately 4.14 $\mu$m and an axial resolution of approximately 85.5 $\mu$m. Phantom imaging of CH$_2$-rich
polymer films and ex vivo lipid-rich biological tissues confirmed the system's high spatial fidelity and strong contrast for lipid-specific structures. This compact, stable, and spectrally refined light source with high spectral energy density can offer an effective solution for high-resolution, label-free molecular imaging and represents a promising platform for clinical photoacoustic imaging applications involving lipid detection and metabolic disease diagnostics.
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All authors Seongjin Bak, Sang Min Park, Yuon Song, Jeesu Kim, Tae Won Nam, Dong-Wook Han, Chang-Seok Kim, Soon-Woo Cho, Brett E. Bouma, Hwidon Lee
Optics · Cross-listed from astro-ph.IM
Comparative analysis of fiber Bragg grating filter losses inscribed by continuous wave UV and femtosecond-IR lasers for astrophotonics
Aashia Rahman, Ria G. Krämer, Abani Shankar Nayak et al. (+8 more)
Fiber Bragg grating (FBG) filters have been demonstrated as promising components in astrophotonic instrumentation for near-infrared ground-based observations. Given the
photon-starved nature of astronomical applications, it is critical to minimize insertion losses across astrophotonic components. In addition to the insertion loss (IL) introduced by specialty fibers and inscription techniques, FBGs exhibit cladding mode (CM) losses. In this work, we studied the loss characteristics of five filter lines in three photosensitive fibers, i.e., a low-numerical-aperture (NA) fiber, a high-NA bend-insensitive fiber, and a cladding-mode-suppressed (CMS) fiber, and in a non-photosensitive fiber, SMF-28. The filters were inscribed using two phase mask-based illumination methods: a continuous wave ultraviolet (UV)
laser with a complex phase mask allowing for multi-channel filters, and a femtosecond infrared (fs-IR)
laser with phase mask integrated shaping apertures for spectral profile control. Our results show that UV-inscribed gratings in high-NA bend-insensitive fiber yield the lowest CM losses ($\approx$ 0.5 dB) among photosensitive fibers, but exhibit the highest IL (4.6 dB), and FBGs in non-photosensitive SMF-28 fiber, inscribed with fs-IR, achieve the lowest IL (< 0.05 dB) with a comparatively higher CM loss (0.93 dB). To reduce the high IL in high-NA fiber, we explored tapering and bridging methods and report that bridging reduces IL by $\sim$ 3 dB. We show that both filter platforms remain viable for integration into an astrophotonic system, with IL below 1 dB. Finally, we propose a compact bridge-fiber scheme with the potential to further reduce IL to below 0.5 dB while reducing the number of bridging fibers and, consequently, the number of splice junctions by 50%.
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All authors Aashia Rahman, Ria G. Krämer, Abani Shankar Nayak, Julius Göhring, Piyamas Choochalerm, Anna Maria Weiß, Samuel L. Döpfner, Tim Schleifer, Kalaga Madhav, Martin M. Roth, Stefan Nolte
Optics
Overcoming Scattering in High-Cell-Density Tomographic Volumetric Bioprinting Using Computational Light Optimization
Qianyi Zhang, Felix Wechsler, Viola Sgarminato et al. (+2 more)
Tomographic volumetric additive manufacturing has emerged as a transformative 3D printing technology for rapidly fabricating complex geometries. It offers significant advantages for bioprinting due to its contactless and short process time (a few tens of seconds). However, the presence of high
cell densities ($>10^7$
cells mL-1) in bioresins introduces substantial light scattering, which degrades printing resolution and fidelity, hindering the fabrication of biologically relevant microstructures such as vascular channels and cavities. To address this challenge, we utilize a computational patterning framework leveraging physically based inverse rendering to optimize light delivery in scattering environments. This method iteratively refines tomographic projections by simulating light-matter interactions in
cell-laden hydrogels, enabling precise compensation for scattering effects. Experimental results demonstrate that our approach achieves 500 ${\mu}m$ diameter vascular channels at $2*10^7$
cells mL-1. Furthermore, we integrate this computational method with refractive index matching strategies, reducing scattering artifacts by minimizing optical mismatch between
cells and the hydrogel
matrix, enabling printing at $4.1*10^7$
cells mL-1. The compatibility of these dual strategies enables unprecedented print fidelity in turbid bioresins. This advancement expands the scope of tomographic volumetric additive manufacturing for engineering functional tissues with intricate microarchitectures.
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All authors Qianyi Zhang, Felix Wechsler, Viola Sgarminato, Christophe Moser, Riccardo Rizzo
Fluid Dynamics
Confinement-induced evolution and breakup of viscoelastic filaments in microfluidic coflows
U. K. Kar, T. Sujith, D. Ghosh et al. (+1 more)
Viscoelastic filament thinning is classically described by elastocapillary dynamics in extensional flows, yet in confined microchannels the combined effects of wall-induced shear, elasticity, and capillarity remain poorly understood. Here, we experimentally investigate the breakup of a shear-thinning viscoelastic
liquid coflowing with an immiscible Newtonian fluid in a rectangular microchannel, focusing on the formation, stretching, instability, and breakup of the thin filament connecting the primary droplet to the upstream
liquid. Four regimes: stable coflow, squeezing, dripping, and jetting are identified and mapped using the capillary numbers of the dispersed and continuous phases and an elastocapillary parameter. Although elasticity weakly affects the onset of primary droplet formation, it strongly alters later filament dynamics by delaying capillary breakup and stabilising long-lived filaments. Scaling analyses based on capillary, viscous, and elastic force balances predict the primary droplet size, critical filament thickness at instability onset, maximum filament length, and critical jet length. Particle tracking shows that confinement creates a non-uniform wall-induced shear field along the inclined filament, producing spatial variations in interfacial velocity and initiating the first bead-on-a-string instability at the location of maximum shear. A Rayleigh-Plateau analysis incorporating an effective viscosity derived from the Oldroyd-B model predicts the instability wavelength and growth rate to the correct order of magnitude. These results show that confined viscoelastic breakup is governed not solely by classical elastocapillary thinning, but by a coupled wall-shear-elasticity mechanism controlling filament stretching, instability, and secondary droplet formation, thereby providing a predictive framework for filament-mediated breakup in confined viscoelastic multiphase flows.
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All authors U. K. Kar, T. Sujith, D. Ghosh, A. K. Sen
Chemical Physics
Physics-Based Molecular Fingerprints from Spectral Graph Theory Provide Efficient Geometry-Aware Measures of Chemical Similarity
Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich et al. (+1 more)
Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite the known importance of 3D structure to determine chemical and physical properties, the most widely used molecular fingerprints encode only two-dimensional connectivity. Such representations fail to distinguish similar but distinct stereoisomers and conformers. Alternative 3D methods are typically defined pairwise, making their application to large chemical spaces prohibitive, while deep learning
embeddings are expressive but uninterpretable and limited by their training data diversity. Here, we introduce novel physics-inspired molecular fingerprints based on principles from spectral graph theory. We represent molecules as a complete graph in 3D space, with edge weights encoding heuristic physical interactions.
Eigenvalue decomposition of the resulting graph
Laplacian matrix results in a computationally efficient fixed-length chemical fingerprint that encodes 3D structure while obeying necessary physical
symmetries of permutation and E(3) invariance. Spectral fingerprints differentiate between unique molecular structures with identical 2D connectivity, overcoming a limitation of 2D descriptors, while maintaining the low computational cost needed for efficient screening of vast chemical spaces. We evaluate our fingerprints with community detection
algorithms and observe strong performance against representative baselines across
datasets from organic, inorganic, biological, reticular, and reaction chemistry. Nearest-neighbor property estimation and applicability domain analyses reveal the utility of our molecular representation in machine learning and cheminformatics. We anticipate that spectral fingerprints will serve as generalizable, interpretable, and efficient measures of chemical similarity that incorporate 3D information at minimal cost.
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All authors Jacob W. Toney, Ayleen Y. Farnood, Samir Darouich, Heather J. Kulik
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Nitrogen Vacancy Centers in Hexagonal Diamond Exhibit Long Coherence Times
Gabriel Kumar, Siyuan Chen, Victor Wen-zhe Yu et al. (+1 more)
We show that negatively charged nitrogen-vacancy (NV) centers in the hexagonal diamond polymorph lonsdaleite offer a route to
spin qubits with enhanced coherence relative to their cubic-diamond counterparts. Using first-principles calculations, we examine two distinct defect configurations, AA, with the same
symmetry as in cubic diamond and AB, with reduced
symmetry. We find that the AB configuration of the NV center exhibits a finite transverse zero-field splitting, giving rise to an approximate fourfold enhancement of the Hahn-echo coherence time $T_2$ at zero
magnetic field. The AA configuration, by contrast, closely reproduces the electronic structure and coherence properties of the cubic NV center. We further characterize the many-body electronic structure, vertical excitation energies, and photoluminescence spectra of both configurations, providing spectral fingerprints for their experimental identification. Our results establish
symmetry-broken NV centers in lonsdaleite as promising candidates for
quantum sensing and information science applications.
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All authors Gabriel Kumar, Siyuan Chen, Victor Wen-zhe Yu, Giulia Galli
Chemical Physics · Cross-listed from quant-ph
Machine learning for sample-based quantum diagonalization: a review of generative configuration recovery and the classical-simulability frontier
Nicolás Bonilla Vargas
Sample-based
quantum diagonalization (SQD), equivalently
quantum-selected configuration interaction (QSCI), has become a centre of
gravity of pre-fault-tolerant
quantum chemistry: a processor samples electronic configurations and the
Hamiltonian is diagonalized classically in the resulting subspace. Accuracy is governed entirely by which configurations enter it – a machine-learning selection problem, made acute by a coupon-collector bottleneck. We review the generative and learned selectors by what each generates and the signal it exploits, and identify one gap: no reward-proportional generative-flow-network proposer has been built for tail discovery. On the field's central question – whether the
quantum sampler beats classical selected CI – the negative verdict is not ours to claim: priority belongs to Reinholdt et al. [JCTC 21, 6811 (2025)], and
polynomial-time classical estimation of the flagship
circuits has reinforced it. We state that verdict at the precision a falsifiable claim requires – it concerns reproducible, same-active-space comparisons on molecular electronic structure – and weigh the claims outside those qualifiers. We show that alpha-string weights are not invariant under rotations inside degenerate orbital shells, so determinant counts are undefined until the orbital gauge is declared. We distil a ten-element benchmarking standard and apply it to our own deposit, which returned defects that changed numbers printed here and retracted one from v1. FCI-exact experiments confirm one prediction and refute another: the single generative advantage we find keeps no consistent sign along the dissociation coordinate at device-calibrated noise and reverses under a symmetric readout model. It does beat a noise-matched classical recovery loop on N2 by a margin five seeds cannot resolve, and loses by over a factor of two to a classical selector that needs no sampler.
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Applied Physics
Reduction of intrinsic losses in nanomechanical silicon nitride resonators through thermal treatment in ultrahigh vacuum
Nicola Cavalleri, Ariane Giesriegl, Robert G. West et al. (+7 more)
Since the discovery of dissipation dilution, silicon nitride (SiN) nanomechanical resonators have set the
benchmark for ultracoherent mechanical systems, with geometry and strain engineering driving remarkable gains in the $f \cdot Q$ product. Surface loss, however, has remained the dominant and largely unaddressed dissipation channel. Here, we demonstrate a geometry-independent approach that directly targets surface loss: thermal treatment in ultrahigh
vacuum. Treatment at 1000$^{\circ}$C enhances the intrinsic quality factor of dissipation-diluted SiN membrane resonators by up to a factor of 20, reduces the surface loss eightfold, and simultaneously increases the tensile stress. Photothermal infrared spectroscopy and $\textit{in situ}$ X-ray photoelectron spectroscopy trace the enhancement to thermally activated silanol condensation - the conversion of surface hydroxyl terminations into siloxane bridges - and the reversibility of both quality factor and stress under controlled humidity confirms the surface-chemical origin. These results establish surface chemistry as a tunable parameter for next-generation ultracoherent nanomechanical resonators.
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All authors Nicola Cavalleri, Ariane Giesriegl, Robert G. West, Kostas Kanellopulos, Saeed Rasouli, Daniele Nazzari, Pedram Sadeghi, Sebastian Alberti, Antonius Armanious, Silvan Schmid
Biological Physics · Cross-listed from cond-mat.stat-mech
Nucleation beyond Equilibrium: Fronts Control Invasion in Bistable Ecosystems
Victor Lequin, Giulio Biroli and Camille Scalliet
Bistability, the existence of two alternative stable states with distinct basins of attraction, is common across ecology and many other biological, chemical, and physical systems. In spatially extended systems, the invasion of one state by the other proceeds through nucleation, the fluctuation-driven growth of a droplet beyond a critical size. Classical Nucleation Theory (CNT) quantifies this process using the energy landscape, but this description relies on detailed balance, the condition of microscopic reversibility that holds at
equilibrium. Ecological dynamics generally violate detailed balance and are described not by a single scalar field but by several coupled, non-conserved abundances–a vector order parameter–for which no general nucleation theory exists. Here we derive such a theory for reaction-
diffusion systems with a non-conserved vector order parameter, valid both close to the binodal, where invasion proceeds through propagating fronts, and close to the spinodal, where the metastable state loses stability. Extensive numerical computations of the quasipotential confirm the theory in both regimes. We show that the mathematical structure of CNT survives out of
equilibrium, once energetic quantities are replaced by dynamical properties of fronts: the front speed plays the role of the bulk free-energy difference between phases, and the diffusivity that of the surface tension. Applied to the two-species Lotka-Volterra model, an archetypal system of bistable ecological antagonism, our theory shows that strong interspecific competition generates a pronounced depletion region within fronts, where the total abundance falls well below carrying capacity. This vectorial structure, invisible to a scalar description based on species frequency alone, makes invasion exponentially harder as competition strengthens at fixed competitive advantage–a prediction testable in microbial systems.
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Geophysics
Marchenko Theory, Algorithms, and Applications: A Review
Hammed A. Oyekan
Seismic reflection data collected at the Earth's surface carry information from every depth in the subsurface, including the internal multiples that conventional migration treats as noise or artefacts. The Marchenko method retrieves subsurface Green's functions directly from these single-sided measurements. The only model information it requires is a smooth macro-velocity model, used to estimate the direct-wave traveltime from the surface to a virtual point at depth. The method originates in one-dimensional
quantum mechanical inverse scattering and has been generalised over the past two decades to three-dimensional acoustic media using acoustic reciprocity
theorems, and to elastic and electromagnetic media using the corresponding elastodynamic and Maxwell reciprocity
theorems. This review covers the classical Gelfand–Levitan–Marchenko equation and its derivation through the Schrodinger scattering formalism, the seismic interferometry results that motivated the multidimensional extension, and the coupled Marchenko equations for wave focusing in higher dimensions. On the application side it treats Marchenko redatuming, single- and double-sided imaging conditions, internal multiple elimination, target-oriented processing, and full-waveform inversion. Extensions to dissipative media, elastodynamics, electromagnetic fields, and plane-wave acquisition are also covered, along with practical topics such as least-squares implementations, GPU acceleration, compressive-sensing acquisition, and recent machine-learning approaches to predicting the focusing functions. Field-data results from marine, land, sub-salt, and time-lapse monitoring settings are summarised. The review closes with an assessment of current limitations and open problems, among them elastic multi-component extensions, joint inversion, and neural-network accelerators.
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Fluid Dynamics
The prediction of extreme uncertainty-production events in three-dimensional Navier-Stokes turbulence
Jin Ge, Joran Rolland and John Christos Vassilicos
We investigate the exponential growth of uncertainty energy in 3D Navier-Stokes
turbulence, emphasising the intermittent and highly localized amplification/production of uncertainty, a critical factor in understanding the predictability of turbulent systems. From the Navier-Stokes equations one can identify some key fields contributing to the growth/decay of uncertainty-production term $P_{\Delta}$: strain rate, vorticity, and
vortex deformation. The dynamics of these fields are examined in the $Q-R$ plane, where $Q$ and $R$ are the second and third invariants of the velocity gradient tensor, to understand their role in the
evolution of uncertainty-production term $P_{\Delta}$. We proceed by estimating committor functions across the entire spatiotemporal domain of direct numerical simulations (DNS) of
turbulence in a periodic domain at different Reynolds numbers. Our estimates of the probability of rare extreme events of local uncertainty-production term as a function of uncertainty energy, strain rate, vorticity, and
vortex deformation confirm the role of strain rate in driving uncertainty. Where strain rate and vorticity are too close to their space-average values, stable probabilistic forecasts appear impossible solely on the basis of the fields considered here.
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Atomic Physics · Cross-listed from quant-ph
Fast Quantum Interconnects via Neutral Atom Ensembles
Sina Zeytinoglu, Wenchao Xu and Thomas Pohl
Distributing
entanglement between distant
qubits is a crucial element of scalable
quantum computing. Here, we describe a scalable
quantum interconnect that generates remote
entanglement at rates approaching those compatible with two-
qubit gates of current neutral-atom
quantum processors. The proposed approach exploits the strong dipole-dipole interactions between atomic Rydberg states to generate
entanglement between stationary
qubits and propagating
photons, without the need for an optical cavity. We provide a thorough description of the optimal conditions for the developed
entanglement-generation protocol for realistic experimental parameters and demonstrate that
entanglement-generation rates $\gtrsim 3\times 10^5$ s$^{-1}$ can be achieved using Rydberg states of ytterbium atoms. Given the inherent scalability and design flexibility of the proposed interconnect, our results suggest a promising approach towards distributed networks based on neutral-atom
quantum architectures.
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Instrumentation and Detectors · Cross-listed from gr-qc
Correlation-based Modeling of Seismic Newtonian Noise in Half-Space and Full-Space Media
Mohamed Samy, Jan Harms and Tomislav Andric
Seismic Newtonian noise, arising from fluctuating gravitational forces on detector test masses due to ambient seismic activity, represents a fundamental sensitivity limit for low-frequency gravitational-wave observatories such as the Einstein
Telescope. Effective mitigation of Newtonian noise requires detailed knowledge of the statistical correlations between the Newtonian acceleration perturbation at the test mass and the seismic displacement field measured by surrounding sensor arrays. In this work, the gravitoelastic correlation tensors (the cross-correlations between the Newtonian acceleration perturbation and the seismic displacement field) are derived and numerically validated for Rayleigh waves and body waves in half-space and full-space media, considering test masses located above and below ground, with and without a spherical cavity. The analytical solutions provide exact and asymptotic
benchmarks for validating a Cartesian numerical integration framework, which reproduces the corresponding gravitoelastic tensors across Rayleigh-wave and body-wave models, establishing a unified tool for Newtonian-noise modeling and sensor-array design in future gravitational-wave detectors.
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Optics · Cross-listed from eess.IV
AI-based single-shot structured-light depth reconstruction for real-time laparoscopic surgical guidance
Wayne Wonseok Rodgers, Xiangyi Le, Seonghoon Jang et al. (+5 more)
Significance. Accurate intraoperative depth perception is important for autonomous and semi-autonomous robotic laparoscopic surgery. Conventional fringe projection profilometry can achieve millimeter-scale accuracy but often requires multi-shot acquisition, digital-micromirror-device projection, and projector-camera synchronization, complicating integration into compact laparoscopic systems. Aim. To develop a synchronization-free, single-shot depth-sensing platform using a passive LED-illuminated binary mask and a VQ-VAE prior with a custom U-Net depth head. Approach. A compact projection module was coupled to one channel of a dual-channel laparoscope, while the second channel imaged the fringe-illuminated target. A Zivid 3D camera acquired reference depth for 722 paired phantom images. Zivid depth maps were reprojected into the SSLE image frame for supervised training and evaluation. The VQ-VAE encoded each input into a discrete latent representation, and a latent-space U-Net predicted depth without a separate mask-prediction branch. Results. Using a fixed train/validation/test split, the proposed model achieved an MAE of 3.70 mm, AbsRel of 0.0326, delta=1.1 accuracy of 0.962, and delta=1.1^2 accuracy of 0.970. It achieved lower MAE than the dual U-Net MaskNet + DepthNet baseline and outperformed off-the-shelf
monocular depth models in MAE, AbsRel, and threshold accuracy. The pipeline operated at 26.0 Hz over 301 consecutive frames on an NVIDIA A100 GPU. Conclusions. The LED-illuminated binary-pattern platform with latent-space depth reconstruction enables synchronization-free,
video-rate endoscopic depth estimation. Results demonstrate Zivid-referenced phantom reconstruction without an explicit
segmentation stage, while emphasizing the importance of
dataset size and SSLE-Zivid calibration accuracy.
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All authors Wayne Wonseok Rodgers, Xiangyi Le, Seonghoon Jang, Shuwen Wei, Justin Opfermann, Michael Kam, Axel Krieger, Jin U. Kang
Chemical Physics · Cross-listed from math.PR
Cluster-Cluster model in $\mathbb{Z}^d$
Noam Berger, Eviatar B. Procaccia, Dominik Schmid et al. (+1 more)
We consider a
stochastic process on $\mathbb{Z}^d$ for $d \geq 1$. Given a translation invariant and ergodic starting configuration of finite clusters, each cluster $C$ performs a continuous time simple random walk with rate $|C|^{-\alpha}$. If it attempts to move to a vertex occupied by another cluster, it does not move, and instead the two clusters connect via a new edge. In all dimensions, we show that if $\alpha\ge 0$, there is almost surely no spontaneous creation of an infinite cluster within finite time. Moreover, for any $\alpha\le-1-2/d$ there is a finite-time blowup almost surely. In the regime $\alpha\in(-1,0)$ we show that the behavior greatly depends on the initial configuration. In addition, in dimension one, we establish the exact phase diagram.
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All authors Noam Berger, Eviatar B. Procaccia, Dominik Schmid, Daniel Sharon
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
Dibyajyoti Chakraborty and Romit Maulik
Data assimilation (DA) uses
Bayesian inference to update the state of a numerical forecast model with observed data. In this study, we propose a fundamentally different, unified approach to atmospheric data assimilation. We use latent
video flow-matching to sample temporally consistent trajectories from a prior trained using ERA5 reanalysis (69 variables over an 8-day window). We also use posterior sampling to assimilate real observation sources, such as those from the NOAA Integrated Global Radiosonde Archive and the Integrated Surface Database. Because the prior generates a continuous trajectory, it naturally propagates information between observed and unobserved frames. Therefore, we can perform various DA tasks, such as filtering and smoothing, simply by changing the observed frames. Moreover, we generate full-state ensemble forecasts directly from sparse observations, achieving performance competitive with state-of-the-art observation-to-forecast models.
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Accelerator Physics
Building a Start-to-End Model of the CESR Injector Linac
Ryland Goldman, Adam Bartnik and Jared Maxson
The CESR injector linac supplies positrons to the Cornell Electron Storage Ring in six stages: a thermionic cathode, a 150 kV DC gun, a prebuncher, four electron linac sections, a tungsten positron converter, and four positron linacs. We present a start-to-end model of the full chain, built in Python from open-source codes. WarpX is used for the space-charge-dominated cathode, gun, prebuncher, and electron linacs, Geant4 for positron production, and Impact-T for the positron linacs. The beam reaches the converter at 146 MeV and the recaptured positrons are accelerated to 250 MeV. Stage configurations are written in YAML to allow algorithmic adjustment by Xopt for ML optimization.
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Plasma Physics · Cross-listed from astro-ph.SR
Spherically Polarized Alfvén Waves and the Gosling Boost
Yuliang Ding, Marco Velli, Zesen Huang et al. (+6 more)
Alfvén waves are thought to play critical roles in
solar wind acceleration and
plasma heating in the
solar corona and inner heliosphere. Parker
Solar Probe (PSP) has highlighted the role of large amplitude Spherically Polarized Alfvén Waves (SPAWs), where the locally constant
magnetic field magnitude $|\mathbf{B}|$ together with outward propagation explains the observed one sided radial velocity enhancement - the Gosling boost. Starting from the MHD equations, we derive the modified wave pressure and Poynting flux under the SPAW condition, and demonstrate both are governed solely by the transverse
magnetic fluctuations. Using PSP data from Encounters 6–25, we define an unperturbed velocity baseline from the lower 10th-percentile running average and statistically characterize the radial
evolution of Alfvénic fluctuations. The background
solar wind velocity shows clear radial acceleration, while the velocity perturbation amplitude $\delta v$ decreases with heliocentric distance. This decay is anisotropic between the radial and perpendicular directions, which is a direct consequence of the growing
magnetic deflection angle related to the spherical polarization. Our results demonstrate that radial velocity enhancements in the young
solar wind arise naturally from SPAWs rather than from localized velocity jets, and provide direct observational evidence for the anisotropic radial
evolution of SPAWs in the inner heliosphere.
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All authors Yuliang Ding, Marco Velli, Zesen Huang, Chen Shi, Lorenzo Matteini, Nikos Sioulas, Stuart D. Bale, Anna Tenerani, Mingzhe Liu
Optics
Mask-free fast patterning of organic light-emitting diode pixels using laser-assisted close-space sublimation
Subhamoy Sahoo, Jain Jose, Mani R et al. (+8 more)
Existing patterning processes for organic light-emitting diode displays offer micrometer-scale precision but are constrained by long processing times for large-area substrates. In this work, we study a fast growth method for patterned organic film deposition, aimed at applications including active-
matrix organic light-emitting diode displays. The approach employs a specially engineered donor substrate in a close-space sublimation configuration combined with
laser heating. The donor substrate incorporates spatially patterned absorber and reflector layers that enable selective, one-step or two-step transfer of organic material onto a receiver substrate. We analyze the optical response and heat-transfer dynamics that govern the selective transfer mechanism and demonstrate precise pixel patterning with micrometer-scale spatial fidelity. The reliability and practical applicability of the method are validated by fabricating light-emitting diode devices by using this rapid transfer process and benchmarking their performance against devices produced via conventional
vacuum thermal evaporation. The resulting devices exhibit comparable optoelectronic performance, confirming the robustness and technological relevance of the proposed strategy for scalable fabrication of patterned organic light-emitting diodes for display application.
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All authors Subhamoy Sahoo, Jain Jose, Mani R, Arghya Saha, Kanimozhi V, Dhruvajyoti Barah, R. Bairava Ganesh, Amitava Majumdar, Jayeeta Bhattacharyya, G Rajeswaran, Debdutta Ray
Applied Physics · Cross-listed from astro-ph.IM
CCAT: Characterization of the first science-grade MKID array for the Prime-Cam 850 GHz module
Anthony I. Huber, Jordan Wheeler, James Burgoyne et al. (+15 more)
The Fred Young Submillimeter
Telescope (FYST) is a 6-meter crossed-Dragone
telescope developed by the CCAT collaboration. Sited at 5600 m on Cerro Chajnantor in the Atacama Plateau of Chile, FYST aims to provide superior atmospheric transmission and a wide field of view for submillimeter observations.
Prime-Cam is a first-generation instrument for FYST designed to house up to seven separate instrument modules. Among these, the 850 GHz module represents the highest-frequency band and is optimized for ultra-sensitive broadband polarimetry and imaging. This module is designed to deploy ~38,000 polarization-sensitive lumped-element titanium-nitride (TiN) microwave kinetic inductance detectors (MKIDs) across three arrays. Thus the 850 GHz module will have the most submillimeter MKIDs in a single instrument module to date. These detectors have adopted a novel two-octave design to maximize the multiplexing achieved using a RFSoC readout system. We review the design parameters and fabrication process for the first 850 GHz science-grade array. The preliminary measurements of the array show a fabrication yield of 99%, highlighting the successful application of the design and fabrication process. We further present the cryogenic characterization of the first full MKID array developed for the
Prime-Cam 850 GHz module. Multiple tests were performed on the devices including resonator frequency mapping, quality factor measurements, optical load sweeps and noise performance. From these measurements, we discuss the measured optical efficiency, sensitivity, and uniformity across the array and the expected on-sky performance of the module. The 850 GHz module will be deployed for observing in 2027.
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All authors Anthony I. Huber, Jordan Wheeler, James Burgoyne, Scott Chapman, Jason Austermann, James Beall, Steve K. Choi, Doug Henke, Johannes Hubmayr, Ben Keller, Matthew Koc, Jeff van Lanen, Tilak Patel, Adrian Sinclair, Anna Vaskuri, Eve M. Vavagiakis, Michael Vissers, Ruixuan Xie
Atomic Physics
Diatomic molecular anions of alkali-metal and alkaline-earth-metal atoms
Sana Akkari, Hela Ladjimi, Wissem Zrafi et al. (+3 more)
Studies of anions are inherently more challenging than investigations of neutrals and cations because of the diffuse and weakly bound character of an anionic electron. Here, we present a comprehensive computational examination of ground-state diatomic molecular anions composed of alkali-metal (Li, Na, K, Rb, Cs, Fr) and alkaline-earth-metal (Be, Mg, Ca, Sr, Ba, Ra) atoms. We study 21 alkali-metal diatomic anions in the X$^{2}\Sigma^{+}$ electronic state and 36 alkali-metal–alkaline-earth-metal diatomic anions in the X$^{1}\Sigma^{+}$ electronic state. The calculations employ a hierarchy of the coupled cluster methods, combined with large Gaussian basis sets and small-core relativistic energy-consistent pseudopotentials for heavier elements. We compute potential energy curves, permanent electric dipole moments, and static polarizabilities, and we assess convergence and uncertainties of our results. Additionally, using the multireference configuration interaction and equation-of-motion electron-attachment coupled cluster methods, we investigate excited electronic states of alkali-metal molecular anions, including valence-bound and dipole-bound states. We predict crossings between ground neutral and excited anionic states, which may enhance resonant electron attachment and subsequent anion dissociation. This finding may be relevant for experiments with mixtures of ultracold ground-state alkali-metal molecules and Rydberg atoms.
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All authors Sana Akkari, Hela Ladjimi, Wissem Zrafi, Hamid Berriche, Marcin Gronowski, Michał Tomza
Biological Physics · Cross-listed from cond-mat.soft
Scaling behavior in non-reciprocal and odd conserved dynamics near criticality
Martin Kjøllesdal Johnsrud, Giulia Pisegna and Ramin Golestanian
In recent years, non-reciprocity has been explored as a ubiquitous manifestation of non-
equilibrium activity at the microscopic scales for various active matter systems, from mixtures of chemically active enzymes, colloids, and droplets, to engineered light-controlled active colloids and robotic meta-materials. A commonly used minimal model to describe the dynamics of a binary mixture of conserved species with non-reciprocal interactions is the non-reciprocal Cahn-Hilliard (NRCH) model. The model is characterized by a temperature-like tuning parameter, which can trigger phase separation, and a non-reciprocal coupling, which can lead to the formation of spatio-temporal patterns, as it represents an intrinsic source of non-
equilibrium activity and breaks parity and time-reversal
symmetries. Here, we study the scaling behavior of the NRCH model near the critical point using perturbative dynamical renormalization group techniques. We find that structural and dynamical correlations are controlled by different correlation lengths, both of which diverge at the critical point, but governed by different scaling laws. In particular, while the structural correlations are always controlled by temperature and the classical Wilson-Fisher critical exponent, the dynamical correlation length exhibits multiple scaling regimes in which either temperature or non-reciprocal coupling can dominate as the key tuning parameter, with a new critical exponent characterizing the divergence. The critical point corresponds to a conserved
equilibrium-like dynamics with odd mobility, which we denote as the odd Cahn-Hilliard (OCH) model. Our findings may have important implications on how living systems can control phase separation and spatio-temporal pattern formation using the rates of catalytic reactions, and in general metabolism, as control parameters.
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Data Analysis and Statistics
Weak Form Recovery of Heston Type Stochastic Dynamics
Sai Sathvik Gullipalli, Eshwar R A and Gajanan V. Honnavar
Estimating the coupled drift,
diffusion, and leverage structure of a
stochastic-
volatility model directly from a price path is an unresolved inverse problem: Kramers–Moyal increment
estimators amplify sampling noise as the step shrinks, weak-form SINDy has not been extended to coupled two-dimensional
diffusions or to the return–variance cross-variation producing leverage, and Heston calibration typically relies on option-implied surfaces rather than the physical-measure path. We extend the spatial weak-form Galerkin framework to the Heston model: variance increments, squared variance increments, squared price increments, and their cross-product are projected onto shared Gaussian kernels in variance space, giving one LASSO regression that jointly recovers mean reversion $\kappa$, long-run variance $\theta$, vol-of-vol $\xi$, and leverage correlation $\rho$, with a drift-informed bias correction analogous to scalar-SDE
diffusion debiasing. Across 30 daily-observed Heston simulations, $\xi$, $\rho$, and $\rho\xi$ are recovered with median errors under 2%. Applied to S&P 500 data spanning the 2007–2010 crisis, the method recovers negative leverage consistent with the documented equity leverage effect, and a 50-stock Indian panel shows the same sign under several independent variance proxies.
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