Optics
All-optical analogue of anti-skyrmions and anti-merons in difference-frequency-generation process
Arannya Ghosh, Mukesh K Shukla and Ritwick Das
In the paraxial domain, a difference-frequency generation (DFG) process provides a versatile platform for exploring non-Hermitian dynamics in an all-classical interaction. This letter aims to show the existence of topologically non-trivial optical analogues of anti-Skyrmionic states in an 'anti' parity-time (anti-PT)
symmetry-broken dynamical interaction in a DFG process. A practical DFG set-up, which is constituted by a periodically-poled lithium tantalate (PPLT)
crystal and pumped by a visible (green) pump
laser, is considered for ascertaining the non-Hermitian dynamics. In the anti-PT
symmetry broken phase, a negatively phase-mismatched interaction gives rise to a topologically non-trivial pseudo-magnetization texture that could support anti-skyrmion-like quasi-particles in signal and idler modes. The analysis also reveals the existence of anti-meron like pseudo-magnetization texture for a phase-matched DFG.
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Plasma Physics
Optical Emission Spectroscopy Measurements of keV Apparent Ion Temperatures in Avalanche Energy's Centrifugal Mirror Machine
M. Affolter, E. C. Hayes, A. Helson et al. (+4 more)
Newly formed ions in $E \times B$ devices are rapidly accelerated by strong radial electric fields and execute large cycloidal orbits in the presence of an axial
magnetic field. At locations where these orbits intersect, ions originating from different birth radii arrive with substantially different velocities, producing a non-Maxwellian velocity distribution with a large velocity variance. Through Coulomb collisions and collective interactions, this distribution relaxes toward a drifting Maxwellian in the rotating frame. Here, we present the first optical emission spectroscopy (OES) measurements of the line-of-sight-convolved ion-velocity distribution, from which an apparent ion temperature is determined, in Avalanche Energy's centrifugal mirror machine. High-resolution $H_\alpha$ spectra obtained along five chordal lines-of-sight spanning the
plasma radius are analyzed using two complementary models representing limiting cases of the ion dynamics: a collisionless cycloidal model based on the ion-velocity distribution arising from deterministic single-particle orbits, and a rotating Gaussian model based on collisions and collective processes that fully randomize the cycloidal motion into a drifting Maxwellian in the rotating frame. Combined, these approaches bracket the possible degree of velocity-space relaxation and provide a stringent test of the inferred ion energies. Both models reproduce the measured spectra relatively well and yield density-weighted apparent ion temperatures of $1.40\pm0.43$ keV for the rotating Gaussian model and $1.55\pm0.24$ keV for the cycloidal model. These results provide direct spectroscopic evidence that strong $E \times B$ rotation in a device only a few centimeters in size can generate ion populations with keV energy spreads.
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All authors M. Affolter, E. C. Hayes, A. Helson, E. McKee, A. Gargone, S. Hepner, R. Langtry
Atomic Physics · Cross-listed from quant-ph
Isabella Goetting, Ashish Kalakuntla, Mikhail Shalaev et al. (+6 more)
Distributed
entanglement across multi-node
quantum networks is essential for a wide range of
quantum technologies, including modular
quantum computers, distributed sensing and metrology, and multi-party secure communication protocols. Such large-scale
quantum networks will require photonic interconnects to generate and sustain entangled states across localized nodes. Previously, three-node distributed Greenberger-Horne-Zeilinger (GHZ) states have been generated between solid-state
qubits and atomic ensembles, but not yet in the platform of individual atomic
qubits, which can be replicated, detected, and individually controlled with high fidelity. Here we report the first fully-distributed GHZ state of
qubits across a three-node
quantum network of single atomic
memories, using photonic interconnects. We achieve a bounded fidelity of $0.841(17) \leq \mathcal{F} \leq 0.881(17)$ at an
entanglement generation rate of 0.095(5)/sec and measure a clear violation of Mermin's inequality while closing the detection loophole for the first time in a fully-distributed multipartite entangled state.
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All authors Isabella Goetting, Ashish Kalakuntla, Mikhail Shalaev, Harriet Bufan Shi, Ana Ferrari, Sagnik Saha, George Toh, Saki Male, Christopher Monroe
Fluid Dynamics · Cross-listed from stat.AP
Regularized Machine Learning for System Identification of Ship Free-Running Manoeuvres from CFD-Based Synthetic Data: A Comparative Study
R.F. Suárez, J.C. Berndt and M. Abdel-Maksoud
This study investigates supervised machine learning techniques for identifying ship hydrodynamic coefficients from CFD-generated data from free-running simulations. Specifically, ordinary least squares and regularized regression methods are applied to Abkowitz-type manoeuvring models. Training and validation
datasets are derived from URANS simulations of zig-zag and turning circle manoeuvres, which are validated against experimental
benchmark data. The analysis evaluates the effects of coefficient set size, minimum training length required for predictive model training, and manoeuvre combinations on model performance. Results demonstrate the suitability of large-angle zig-zag manoeuvres for hydrodynamic system identification, provided that multicollinearity is addressed through appropriate coefficient selection, regression models, or input data variability. Larger coefficient sets offer greater model flexibility for variable conditions but are more prone to multicollinearity. Regularized regression techniques effectively mitigate multicollinearity and notably enhance prediction accuracy, as does incorporating more diverse manoeuvring data. Among tested models, Ridge regression provided the best compromise between computational efficiency and prediction accuracy.
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Chemical Physics · Cross-listed from cs.LG
Noise-Driven Escape from Metastable Phases explains Grokking in Deep Neural Networks
Ibrahim Talha Ersoy and Karoline Wiesner
Deep neural networks (DNNs) exhibit first order phase transitions under variations of the L2 regularization strength, with each transition marking the onset of a new learnable feature. Below a critical regularization strength, all features are in principle learnable, but coexisting metastable states, separated by energy barriers, can trap the network and impede convergence. A strength of DNNs is their ability to generalize. But many open questions remain, among them the origin of so called grokking: the abrupt, delayed onset of generalization after prolonged apparent overfitting. We show for linear DNNs that grokking is consistent with hysteresis in first-order L2 phase transitions: using L2 regularization to engineer deliberate trapping, we demonstrate that a model in a low-accuracy metastable state escapes only when SGD noise drives it across an energy barrier, with escape times following Arrhenius scaling. We reproduce grokking-like delayed convergence across two orders of magnitude in escape time by deliberately trapping models in metastable phases. Using sparse sub-sampling we also reproduce the canonical grokking curve where test error eventually approaches the final training error. Our work suggests that the number of metastable states equals the number of learnable features – one per singular value of the data covariance – the potential for hysteresis grows naturally with task complexity. We provide evidence that the same mechanism likely operates in general nonlinear DNNs. Our results provide routes toward more efficient learning schemes.
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Biological Physics · Cross-listed from cond-mat.stat-mech
Milestoning Markov-jump dynamics: Stationary properties, thermodynamic consistency, kinetic hysteresis, and fluctuation symmetries
Tassilo Schwarz, David Hartich and Aljaž Godec
We derive an exact coarse graining of generic Markov-jump processes into observable semi-Markov dynamics. Exact results for waiting-time distributions for jumps between observable states are derived and proved that these decompose into conditionally independent dwell and transition times. Dwell times are proved to be a local property of mesostates - they depend on the initial but not final state. Conversely, transition-path times depend on both states, trigger kinetic hysteresis, and, under suitable conditions on the hidden sub-network, are shown to obey a reflection
symmetry. We characterize the stationary properties of the milestoned dynamics, prove its thermodynamic consistency, and demonstrate robustness to milestone positioning. Surprisingly, even in the limit of a time-scale separation rendering the observed dynamics approximately Markovian, the effect of kinetic hysteresis on the dissipation persists. A minimal example shows how the results lay the foundation for inferring affinities of hidden dissipative cycles from observations of transition-path times.
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Applied Physics · Cross-listed from cond-mat.stat-mech
Distributed Acoustic Sensing for Urban Monitoring: Coverage Thresholds and Percolation
Khen Cohen and Ariel Lellouch
Distributed Acoustic Sensing (DAS) enables the repurposing of existing fiber-optic networks as ultra-dense, long-range seismic arrays for urban monitoring. However, constraints imposed by real-world fiber infrastructure
topology and components limit its use for city-scale applications. Recent technological developments have paved the way for short-range, on-chip DAS. Assuming their availability, and based on a Graph Theory framework, we show that monitoring applications fall along a coverage spectrum with two critical thresholds that define three distinct regimes. Low coverage (<10%) can, with optimal design, resolve earthquake early warning, groundwater monitoring, geological mapping, and urban activity tracking. A percolation transition occurs at 51.6% coverage, beyond which the city effectively becomes fully covered and statistical traffic monitoring is possible. Only for effectively complete coverage, infrastructure monitoring, individual vehicle tracking, and pedestrian movement analysis become possible. Thus,
privacy-related risks remain very low. We show and exemplify how, for metropolises around the world, an optimal sensing network can be designed for earthquake early warning, traffic monitoring, and urban activity tracking. This framework provides a near-future roadmap for deploying urban DAS networks as a backbone of smart city sensing.
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Chemical Physics
Hydrogen Chemisorption and Current-Induced Spin Polarization on NbP
Luis Martinez-Gomez and Raphael F. Ribeiro
Topological semimetals have been proposed as electrocatalytic platforms because their surface states can connect adsorbate bonding with interfacial charge and
spin responses. Here we investigate hydrogen chemisorption on NbP(001) using density functional theory and Wannier-based analyses of surface spectra, chemical bonding, and current-induced
spin polarization. Comparing calculations with and without
spin–orbit coupling allows us to examine nodal-line-derived surface states and Weyl
Fermi arcs on the same surface.
Spin–orbit coupling leaves the adsorption thermodynamics essentially unchanged but reorganizes how hydrogen bonds with the Nb- and P-derived surface orbitals. The characteristic
Fermi-arc branches persist after adsorption, with H-derived spectral weight appearing within the same near-
Fermi-level surface
manifold. Together, these results show that chemisorbed hydrogen participates in the
spin-textured surface electronic response. The accompanying adsorbate-local current-induced
spin polarization points toward opportunities to connect surface chemistry with electrically controlled
spin phenomena in topological semimetals.
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Optics
Deterministic single-photon sources in hexagonal boron nitride with electron-dose-tuned purity and reversible thermal quenching
Amrita Majumder, Janhavi Khunte, Ikshvaku Shyam et al. (+2 more)
Electron-beam irradiation is an established route to create site-controlled, room-temperature single-
photon emitters (SPEs) in hexagonal boron nitride (hBN), but two aspects remain underexplored: how the electron dose governs the properties of the resulting single emitters, and how the emission behaves when the host is heated above room temperature. Here, we create emitters deterministically with a focused electron beam and confirm single-
photon emission across three independent flakes, with $g^{(2)}(0)=0.09$, $0.12$, and $0.16$. We map the single-emitter response (yield, spectrum, lifetime, and
photon purity) as a function of electron dose, identifying an optimal window for high-purity single emitters. Consistent with recent cryogenic studies, we assign the bright room-temperature feature near 575 n to the
phonon sideband (PSB) of a green–yellow emitter whose zero-
phonon line (ZPL) lies near 548 nm. Temperature-dependent photoluminescence measured in situ under real-time from room temperature to 300 degrees C reveals a thermal quenching that is fully reversible upon cooling, in contrast to the irreversible annealing-induced degradation reported elsewhere, indicating that transient heating does not permanently damage the centers. These results add quantitative dose control and above-room-temperature operation to the toolbox for deterministic hBN
quantum-light sources.
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All authors Amrita Majumder, Janhavi Khunte, Ikshvaku Shyam, Rohit Kumar, Anshuman Kumar
Atomic Physics
Coherent control of orthogonal continuum states in XUV photoionization
Neha Kukreti and Amol R. Holkundkar
We demonstrate coherent control of orthogonal continuum electron states in the XUV photoionization of atomic hydrogen using polarization-tailored driving fields. By combining polarization mixing with carrier-envelope-phase (CEP) control, we generate a pair of orthogonal momentum-space basis states whose populations and relative phase can be independently tuned via the polarization-mixing parameter and CEP, respectively. The resulting photoelectron wave packets are prepared as coherent superpositions within this effective two-dimensional subspace, with
quantum coherence confirmed by interference visibilities reaching 99% in the photoelectron momentum distributions (PMDs). We further demonstrate that a bichromatic driving-field configuration extends this framework to a four-dimensional continuum
manifold, with basis states distinguished by both angular emission patterns and radial momentum distributions, and with independent amplitude and phase control preserved across the higher-dimensional subspace. These results establish that polarization-tailored XUV fields provide a flexible route to independent amplitude and phase control within low-dimensional subspaces of the photoelectron continuum, with the engineered dynamics directly observable in momentum-resolved spectra.
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Optics
Transverse Modulation of Continuous Electron Beams by a Structured Optical Cavity
Marius Constantin Chirita Mihaila, Julie Skýpalová and Martin Kozák
Compact aberration correction at large semi-angles remains a central challenge in electron microscopy. Here we propose a phase plate for continuous electron beams based on the ponderomotive interaction with an intracavity standing wave in a near-concentric Fabry–Pérot resonator. For electrons propagating along the cavity axis, a standing-wave with Laguerre-Gaussian mode of topological charge $l=1$ imprints an annular phase shift that is opposite in sign to the third-order spherical aberration of a conventional electron lens. For a \(5~\mathrm{keV}\) beam with a convergence semi-angle of \(25~\mathrm{mrad}\), we fully compensate the third-order spherical aberration of an objective lens with \(C_\mathrm{s}=1~\mathrm{mm}\), yielding the transverse width of the corrected probe in the focus of \(\sigma_\mathrm{p}=1.4~\mathrm{\AA}\). The accessible transverse electron phases are set by the supported resonator modal basis and its coherent superpositions.
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Instrumentation and Detectors
Latent space mapping of interpretable structural coordinates from stochastic single-molecule signals
Matteo Cartiglia, Sandro Kuppel, Wouter Botermans Wannes Peeters et al. (+7 more)
Nanopores are versatile single-molecular sensors, but their utility is fundamentally constrained by
stochastic translocation dynamics warping any encoded information. We resolve it by shifting from time-domain analysis to a learned latent-space mapping via a contrastive encoder trained exclusively on simulated signals from a physics-informed model. This encoder maps solid-state nanopore signals of engineered DNA barcodes into an interpretable molecular coordinate system. The learned representation is responsive to structural barcode parameters while remaining invariant to acquisition conditions and translocation conformation, allowing data pooling across devices. Molecule identification requires a single pass through the encoder, reducing computational cost by three orders of magnitude relative to
alignment-based methods. We experimentally validate through mixture quantification, rare-variant detection, consensus barcode reconstruction, and real-time signal acquisition. This shift from temporal analysis to mapping structural coordinates into a latent space changes the paradigm behind analyzing
stochastic sensor signals by linking classification to interpretable encoded molecular information.
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All authors Matteo Cartiglia, Sandro Kuppel, Wouter Botermans Wannes Peeters, Natan Biesmans, Liam Vandekerckhove, Eric Beamish, Koen Ongena, Wouter Renckens, Pol Van Dorpe, Sanjin Marion
Applied Physics · Cross-listed from quant-ph
Counterdiabatic Raman Atom Optics for Compact High-Sensitivity Gravimetry
Asad Ali, Hamid Arian Zad, Saif Al-Kuwari et al. (+6 more)
Large-momentum-transfer (LMT) atom interferometry provides a route toward enhanced inertial sensitivity in compact
quantum sensors, but its scalability is limited by the accumulation of pulse-transfer errors across long Raman pulse sequences. We investigate theoretically the use of stimulated Raman shortcut-to-adiabatic passage (STIRSAP) for high-fidelity LMT atom optics in a Mach–Zehnder interferometer geometry. The counterdiabatic correction is encoded directly into the Raman pulse envelopes, eliminating the need for auxiliary microwave or
radio-frequency control fields. Numerical simulations based on an effective Raman model show that $1~\mu\mathrm{s}$ STIRSAP pulses achieve single-pulse transfer fidelities of $F_\pi = 0.99902$ while maintaining negligible pulse-time overhead even at high momentum order. We analyze the resulting tradeoff between interferometric phase enhancement and compound contrast decay and identify an unconstrained shot-noise optimum near $n\approx270$. The analysis further shows that practical operation at extreme LMT order is constrained by wave-packet separation, vibration noise, Doppler detuning, and accumulated systematic effects rather than by pulse duration itself. These results establish superadiabatic Raman control as a promising approach for scalable high-fidelity atom optics and clarify the physical limitations governing compact high-order atom interferometers.
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All authors Asad Ali, Hamid Arian Zad, Saif Al-Kuwari, Muhammad Irtiza Hussain, Muhammad Talha Rahim, Hashir Kuniyil, Tim Byrnes, James Q. Quach, Saeed Haddadi
Optics
Edge-enhanced diffractive neural networks based on spin-multiplexed nonlocal metasurfaces
Qianqian He, Kenan Guo, Jumin Qiu et al. (+3 more)
Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and DNN-based classification on a single chip remains a challenge. Here, we propose an integrated nonlocal meta-platform that sequentially achieves all-optical edge detection and classification via
spin-multiplexing. By exploiting the dispersion properties of the nonlocal Huygens' metasurface, the co-polarized component in the output light performs momentum-space filtering for real-time edge detection. Subsequently, the cross-polarized component undergoes
geometric phase modulation to execute image classification within the DNN. We couple quasi-bound states in the continuum and
magnetic dipole resonances in crescent-shaped nanopillars, achieving a high polarization conversion efficiency of approximately $55\%$. This edge-enhanced DNN architecture significantly reduces data redundancy, elevating the classification accuracy of the single-layer network on the MNIST
dataset from $64.2\%$ to $80.7\%$. Our work provides a compact, high-efficiency solution for integrated all-optical machine
vision and intelligent photonic computing.
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All authors Qianqian He, Kenan Guo, Jumin Qiu, Huifu Qiu, Shuyuan Xiao, Tingting Liu
Optics · Cross-listed from quant-ph
Experimental quantum state learning with pairs of photons
C. Pria Dobney, Johan Henaff, Allen Kasum et al. (+5 more)
Tomography allows one to estimate the density
matrix describing the state an ensemble of
quantum systems are prepared in (for example, polarization
tomography determines the polarization state of a beam of identically prepared
photons). In general, it is not possible to uniquely decompose the density
matrix into its pure state components. Agarwal et al. proposed a protocol which, for a mixture composed of any two pure states of a
qubit (with arbitrary probabilities), allows an observer to infer not only the density
matrix but the identity of those specific pure states and their weights - the additional requirement being that the
qubits arrive in pairs, where both
qubits in each pair are in the same state. We experimentally demonstrate this learning-from-pairs concept using
photons in the polarization degree of freedom. We use
tomography to measure a sequence of single
photons and make use of their time-of-arrival information to 'pair up' the
photons after the measurement. From here we are able to infer the
photons' polarization states and their respective probabilities, and we demonstrate this for various different choices of polarization states and ratios. Finally, we investigate our ability to discriminate between two equal mixtures of distinct pairs of orthogonal polarization states. We find that on the order of approx. 10e4
photons is typically enough to achieve
tomography fidelities of approximately 0.9999. This is sufficient to discriminate between two different preparations of the same mixed state, differing by angles of less than 5 degrees between the pure states used in the two preparations.
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All authors C. Pria Dobney, Johan Henaff, Allen Kasum, Rui Jie Tang, Haru Mukumoto, Mark Hillery, Berthold-Georg Englert, Aephraim Steinberg
Optics
A Scalable All-to-All Reconfigurable Ising Solver Using Pulsed Time-Division Multiplexing
Henry Love, Zhehao Yu, Mohammad Alimadadi et al. (+6 more)
Physics-based computing platforms, such as those based on the Ising model, are an important pillar of future hardware systems built for the artificial intelligence (AI) era. Such platforms show promise for solving nondeterministic
polynomial (NP) time problems that are difficult for traditional processing units to solve efficiently as problem size grows. Here, we present a scalable optoelectronic Ising machine architecture, demonstrated with 64 all-to-all connected
spins using pulsed time-division multiplexing. The 65 nm CMOS Ising chip integrates the coupling and nonlinear mechanisms in an active area of 3.1 mm2, eliminating the need for benchtop equipment within the loop. The feedback loop of the Ising machine is closed using a compact high-bandwidth, low-loss optical fiber, seamlessly combining optical scalability with the ultradense reconfigurability of integrated electronics. The chip operates at 1 GHz with 4-bit coupling weights and is benchmarked with NP-complete Boolean satisfiability problems consisting of three literals (3-SAT) and clause-to-variable ratios of 32/32, 40/24, and 48/16. Nanosecond annealing times represent at least a three order-of-magnitude improvement over previously reported all-to-all connected works. Time and energy to solutions for 100% 3-SAT clause accuracy are as low as 7.4 us and 2.9 uJ, respectively, achieving more than an order-of-magnitude decrease in time and energy to solution compared to the state of the art. All-to-all connection is demonstrated using MaxCut problems with 100% graph densities. The chip's ability to effectively solve 2-, 3-SAT, and MaxCut problems highlights its reconfigurability and versatility. Furthermore, combining mature CMOS integration with scalable photonic links allows for significant reduction in computation time and energy, addressing the pressing requirements of AI and future hyperscale datacenters.
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All authors Henry Love, Zhehao Yu, Mohammad Alimadadi, Yan Jin, Nitesh Chauhan, Brian Edwards, Pratik Chaudhari, Scott B. Papp, Firooz Aflatouni
Optics
Coupled Flexural Optomechanical Cavities with Engineered Nanomechanical Interconnects
David Alonso-Tomás, Guillermo Arregui, Bingrui Lu et al. (+3 more)
Integrated nanomechanical
circuits require compact and predictable ways to read out, confine, and connect mechanical motion across multiple nanoscale elements. This challenge is particularly acute for megahertz flexural modes, whose large mechanical response and nonlinear dynamics are attractive for optomechanics, sensing, and signal processing, but whose extended nature makes local confinement and coupling difficult within dense devices. Here we demonstrate an optomechanical nanobeam platform in which optical transduction and mechanical connectivity are both engineered lithographically. Transverse
geometric asymmetry in the photonic-
crystal cavity breaks the cancellation that suppresses dispersive coupling to in-plane flexural motion, making these modes optically bright without ancillary structures. In parallel, serpentine mechanical links engineered through their complex band structure act as compact mirrors and evanescent couplers for MHz flexural waves. In coupled-cavity devices, the normal-mode splitting decays exponentially with the number of serpentine
cells, yielding an experimental attenuation constant in quantitative agreement with full-system simulations. Geometry-dependent measurements further show that the coupling can be tuned by the interconnect design and identify regimes where finite-link modes hybridize with the cavity modes, beyond a simple two-resonator picture. These results establish complex-band-engineered mechanical links as calibrated interconnects for scalable optomechanical nanocircuits based on optically addressable MHz flexural resonators.
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All authors David Alonso-Tomás, Guillermo Arregui, Bingrui Lu, Sergei Lepeshov, Søren Stobbe, Daniel Navarro-Urrios
Physics and Society
The Winner Takes It All
P. L. Krapivsky
The winner-takes-all process takes place on an arbitrary graph. There is an
agent on each vertex of the graph, and active
agents at neighboring vertices play games. In each game, a randomly chosen
agent wins, while the loser is eliminated from subsequent games. The games are played at random times, finishing instantaneously, and ceasing when each active
agent has only losers among its neighbors. On the one-dimensional
lattice, the fraction of winners in the final state is $e^{-1}$; we also determine the fractions $w_j$ of winners who won $j=0, 1, 2$ games. For finite segments, we determine statistics of the total number of winners (the average, the variance, and all higher cumulants), the probabilities of attaining the minimum or maximum possible number of winners, and establish the behavior near the boundaries. For infinite regular trees with vertices of degree $d$, i.e., Bethe
lattices with coordination number $d$, we show that the fraction of winners is $(2/d)^{d/(d-2)}$.
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Computational Physics · Cross-listed from quant-ph
Morphology-resolved scrambling in a chaotic quantum billiard
Pranaya Pratik Das
Chaotic
quantum systems can retain spatial
memory through scarred eigenstates, but whether these static structures control scrambling remains unclear. This work establishes a morphology-resolved connection between scarred eigenstates and eigenstate-resolved OTOCs in a peanut-shaped
quantum billiard. Scalar localisation diagnostics, including differential
entropy and continuum participation ratios, detect anomalous concentration but discard spatial architecture. A scale-normalised density overlap, in contrast, directly compares probability density profiles, revealing families of orthogonal eigenstates with nearly identical spatial morphology. Comparing the complete OTOC time traces of these orthogonal eigenstates reveals that morphological recurrence has dynamical content: moderate density overlap yields no universal prediction, whereas strongly recurring morphologies exhibit nearly identical OTOC growth and saturation. Thus, scarred structures act as spatial templates for operator growth, not merely static violations of ergodicity. This morphology-resolved framework turns eigenstate shape into a quantitative predictor of scrambling and provides a scale-controlled diagnostic of weak ergodicity breaking in
quantum chaos.
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Instrumentation and Detectors · Cross-listed from hep-ex
Studies of Neutrino-Nucleus Elastic Scattering with Point-Contact Germanium Detectors at the Kuo-Sheng Reactor Neutrino Laboratory
TEXONO Collaboration: M.K. Singh, S. Karmakar, Greeshma C. et al. (+35 more)
The low energy and intense flux of electron anti-
neutrinos from
nuclear reactors provide the perfect stage to study elastic
neutrino-
nucleus scattering ($\nu A_{el}$) in the fully coherent regime. We report results from the TEXONO experiment using electro-cooled $p$-type point-contact Germanium detectors with masses of 523~g and 1434~g at the Kuo-Sheng Reactor
Neutrino Laboratory. We report improved constraints on the $\nu A_{el}$ cross section with a combined exposure of 404(813.7)~kg-days of Reactor ON(OFF) data at an electron-equivalent threshold of 200~eV$_{ee}$. The Lindhard model, in which the quenching factor is parameterized by a single parameter k, is adopted to describe the suppression of ionization yield. At the
benchmark value of k=0.162, a limit of $\rho<$2.0 at 90% confidence level (CL) is derived, where $\rho$ represents the ratio of the observed to the predicted Standard Model cross section. Moreover the region k$>$0.205 is excluded at 90% CL using the SM-predicted $\nu A_{el}$ rate. A bound on the
neutrino magnetic moment from $\nu A_{el}$ at $\mu_{\nu} {<} 5.9 \times 10^{-10}~\mu_B$ at 90% CL is also derived.
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All authors TEXONO Collaboration: M.K. Singh, S. Karmakar, Greeshma C., H.B. Li, F.K. Lin, V. Sharma, L. Singh, H.T. Wong, L.T. Yang, M. Agartioglu, J.H. Chen, J.W. Chen, C.I. Chiang, M. Deniz, T. Guo, H.C. Hsu, W.H. Kao, S. Karadaǧ, J.B. Legras, C.H. Leung, J. Li, T.Y. Liang, S.T. Lin, S.K. Liu, H. Ma, V. Marian, D. Mishra, M.K. Pandey, K. Rani, K. Saraswat, M.K. Singh, V. Singh, D. Tanabe, J.S. Wang, Y.F. Wang, C.-P. Wu, C.H. Yeh, Q. Yue
Chemical Physics
Transferable machine learning of excited-state dynamics with extremal pooling
Cesare Malosso, Wei Bin How, Gonzalo Díaz Mirón et al. (+2 more)
Photochemical processes govern phenomena ranging from
solar energy conversion and atmospheric chemistry to
vision and photosynthesis. Accurate simulation of these processes requires modeling excited-state potential energy surfaces, often involving chemical reactions, tasks that remain computationally prohibitive for extended systems and long timescales using traditional ab initio methods. Machine learning interatomic potentials have revolutionized ground-state simulations, but their extension to excited states faces fundamental challenges: standard architectures assume energy extensivity, an assumption that fails for excited states. Here, we present a size-intensive machine-learning framework for excited-state dynamics based on extremal pooling of predicted atomic HOMO and LUMO contributions. Trained exclusively on excitations energies and forces, the architecture learns interpretable atomic-level contributions that encode physical information on the extent of electron localization. We demonstrate this framework on the photoexcited solvated electron in
liquid water, a paradigmatic problem in radiation chemistry leading to competing pathways involving both hydrogen-atom dissociation and
proton-coupled electron transfer. The model not only reproduces the relevant chain of reactions and product species that form during excitation, but also allows one to explicitly study the dynamics of the solvated electron in quantitative agreement with previously reported Restricted Open-Shell Kohn-Sham calculations, while enabling excited-state simulations of periodic systems at length and time scales inaccessible to the reference electronic-structure method. This work establishes a general strategy for machine learning-driven excited-state dynamics applicable to diverse photochemical systems, from molecular chromophores in solution to extended condensed-phase systems.
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All authors Cesare Malosso, Wei Bin How, Gonzalo Díaz Mirón, Ali Hassanali, Michele Ceriotti
Optics
Reducing Turbulence-Induced Outages in a Deployed Terrestrial Free-Space Optical Communication Link via Interleaving
Kadir Gümüş, Vincent van Vliet, Menno van den Hout et al. (+3 more)
We present an experimental study of data interleaving for terrestrial free-space optical communication over a 4.6~km urban testbed. Results demonstrate a two-order-of-magnitude reduction in outage probability. A dependency between measured
turbulence strength, interleaver length, and achievable data rate is revealed, enabling robust system design.
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All authors Kadir Gümüş, Vincent van Vliet, Menno van den Hout, Thomas Bradley, Eduward Tangdiongga, Chigo Okonkwo
Optics · Cross-listed from math.NA
Phase-field digital image correlation for integrated displacement and damage measurements
Dingxiang Zhu and Ye Lu
This work presents a novel digital image correlation (DIC) framework for full-field measurements of displacement, strain, and damage, based on a phase field (PF) approach. The idea is to take advantage of the ability of the PF method to track complex crack morphologies and to provide a natural way in DIC to perform damage and crack measurements from experimental speckle images, in addition to displacement and strain fields. Moreover, incorporating the damage variable into DIC can improve the displacement accuracy near the crack tip, and can avoid the need of user-defined masks when dealing with cracked samples, which is advantageous when cracks become complex and the manual application of masks becomes challenging. The theoretical formulation of the proposed framework, namely PF-DIC, was presented in detail in the paper, along with a finite element implementation. Numerical examples have demonstrated the capability of the proposed PF-DIC in terms of capturing different types of cracks while providing similar measurement accuracy to that of masked DIC. Additionally, it is shown that the PF-DIC can be easily adapted to selectively identify critical cracks under specific loading conditions or mechanisms for damage assessment and diagnostic purposes. The proposed DIC framework can be used to characterize material defects, support structural health monitoring, and enable a potential unification of PF simulations and experimental fracture measurements
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Optics
Divergence of Light Wave Amplitudes in an Interface Layer at Critical Conditions
Reinhard Eduard Sigel
The amplitudes of light modes in a homogeneous interface layer are investigated around the critical conditions (CC) in a total reflection geometry. CC occur when the normal wave vector vanishes; the resulting divergence upon variation of the angle of incidence is characterized by a critical exponent -0.5. Absorption replaces the divergence with a finite peak whose width and height are derived analytically. The high relevance of the amplification for Evanescent Wave Dynamic Light Scattering (EWDLS) is demonstrated using published experimental data. The relation to surface plasmon resonance (SPR) is briefly discussed. An outlook connects the amplitude divergence to a critical analysis of the Distorted Wave Born Approximation (DWBA) presented in a companion paper.
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Medical Physics · Cross-listed from eess.IV
Conditioning Deep Anatomical Prior Knowledge for Reconstruction of Multispectral Optoacoustic Tomography Images
Sarah Franceschin, Lukas Imanuel Scheel-Platz, Philipp Haim et al. (+3 more)
Accurately delineating tissues and reconstructing their chromophore compositions from Multispectral Optoacoustic
Tomography (MSOT) images is a key challenge in optoacoustic imaging. The difficulty arises because light fluence distributions within tissue intrinsically depend on spectral optical properties, making the inverse problem inherently ill-posed. Currently, there is a lack of studies leveraging a priori probabilistic anatomical knowledge to guide tissue
segmentation and infer chromophore composition. Moreover, most current studies address these two tasks sequentially, which can result in errors accumulating. through the process. To address these issues, we present Anatomical Priors for Reconstruction of Optoacoustic
Tomography (APRECOT), a method that leverages probabilistic models of anatomical structures and tissue properties, to enable simultaneous
segmentation of tissues and reconstruction of their bulk chromophore compositions. In this proof-of-concept using in-silico data, we show that incorporating probabilistic anatomical context strongly improves the accuracy of bulk chromophore concentration estimation compared to reference methods that do not use any anatomical context or use sequential strategies. This work represents an essential step towards an MSOT imaging mode that directly provides clinically relevant information, such as imaging tissue oxygenation dynamics or disease-related changes in tissue composition.
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All authors Sarah Franceschin, Lukas Imanuel Scheel-Platz, Philipp Haim, Guillaume Zahnd, Vasilis Ntziachristos, Dominik Jüstel
Physics Education
Students' Validation Criteria for AI-Generated Physics Solutions
Álvaro Suárez and Richard González
A physics solution can be clear without being valid. This distinction becomes important when students read AI-generated solutions that appear coherent but still require physical and mathematical checking. We examined how pre-service physics teachers in a calculus-based introductory electromagnetism course evaluated AI-generated solutions to a charged-rod electric-field problem after attempting the problem themselves. Although the AI-generated solutions analyzed were correct, no student accepted a response as valid after a first reading. Students who had solved the problem fully or in part could examine the responses using physical and mathematical criteria, while others withheld acceptance because they could not follow the mathematical development. These results suggest that, in physics instruction, AI-generated solutions should be treated less as answers to be accepted or rejected and more as worked-out responses that students learn to reconstruct, question, and test.
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Physics Education
AI as a Partner in Learning about, Doing, and Engaging with Science: Vigilance as the Key to Productive Augmentation
Marcus Kubsch
AI has become a partner in how people learn about, do, and engage with science, and the partnership takes three forms: a scientist works with a co-scientist whose output must be checked; a member of the public looks something up to decide whether a diet works or whether to fit
solar panels; and a student takes up an inquiry with AI in a science class. Across all three, one thing decides whether the partnership helps or harms: whether the human evaluates what the AI returns or takes it on trust. I argue that this evaluation – epistemic vigilance calibrated to how far a fallible source can be trusted – is, given adequate prior knowledge, the binding constraint on productive augmentation. You can hand the AI a great deal precisely because you stay vigilant; vigilance makes generative partnership safe, so it licenses augmentation rather than restricting it. Vigilance is already invoked in science education but under-specified for the AI case; I specify its components, the mechanism tying it to learning, and a way to measure it without soliciting the evaluation it is meant to detect. What is distinctive is that the machine's fluent, confident prose reads as trustworthy whether or not it is, so its surface works against the human evaluating it. The argument bears hardest on education: the integrated conceptual knowledge instruction aims to foster forms only under deep processing, and vigilance sets how deeply a claim is processed, so it is the precondition for learning with AI. The design factors the field reports matter through whether they engage the learner's evaluation; none works around it. Untested is vigilance as a measured disposition, above all where the AI is confidently wrong. Because it is unevenly distributed, integrating AI uniformly is likely to widen the gap between better- and less-prepared students. I close on how it might be built by fading support as the learner takes over.
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Atomic Physics · Cross-listed from quant-ph
Quantum Nonlocal Games on Graph Ensembles
Joshua Tucker, Chris Weeks, Peter Drmota et al. (+11 more)
Quantum entanglement is one of the most striking discoveries in all of science. This effect allows, for instance, two spatially separated
agents to coordinate their actions, without communication, to an extent that is both counter-intuitive, and provably impossible by any other physical means. A recently discovered example is that of mobile
agents (players) performing spatial coordination tasks such as rendezvous, where the
agents aim to meet on a network without communication. Until now, demonstrations of this advantage have relied on highly idealized conditions:
agents are assumed to have complete knowledge of the topography, and experiments have been restricted to simulations using data generated by
qubits within a single
quantum processor. Here we address both limitations by developing a theory for graph ensembles that capture topographical uncertainty and by experimentally demonstrating the advantage in rendezvous scenarios between physically separated ion-trap systems with access to remote
entanglement. Moreover, we simulate a broader set of problems on superconducting hardware. Surprisingly, when players are given the ability to gather more local information the
quantum advantage increases – a feat impossible by classical means. Our findings establish a concrete route toward practical
quantum advantages in motion coordination problems. More broadly, they point to a new way of using portable
quantum devices to enhance collective decision-making in uncertain environments.
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All authors Joshua Tucker, Chris Weeks, Peter Drmota, Ellis M. Ainley, Ayush Agrawal, Adam R. Martinez, Erin Malinowski, Jacob A. Blackmore, David P. Nadlinger, Gabriel Araneda, David M. Lucas, Carlos A. Perez-Delgado, Paul Strange, Jorge Quintanilla
Physics Education
Defining a physics person: Tensions in the identity development of undergraduate women in physics
Laura M. Akesson, Jessica L. Rosenberg, Nancy Holincheck et al. (+1 more)
This qualitative study explores how undergraduate women define what it means to be a physics person alongside how they describe their own physics identity. Our team analyzed 120 survey responses (97%, or 116 of the 120 respondents, identified as women or non-binary) to understand what factors are associated with an individual's physics identity. Those surveyed were attendees of Conferences for Undergraduate Women in Physics, who attended 88 unique institutions in 30 states. Our participants' conceptions of what it means to be a physics person fell into two themes: (1) people who are skilled academically and in research and (2) those who have an interest in physics ranging from an enjoyable interest to a life-devoting passion for the subject. When asked to explain their own physics identities, students responded through a personal and social lens more frequently than through an academic lens. Our analysis revealed three tensions in the development of undergraduate women's physics identity: (1) the tension between identifying entirely as a physics person and having other identities that are important to who they are, (2) the tension between the need for belonging in the physics community and their view of themselves as a physics person, and (3) the tension between their expectations of a physics person and their beliefs about their own abilities or interest.
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All authors Laura M. Akesson, Jessica L. Rosenberg, Nancy Holincheck, Benjamin W. Dreyfus
Fluid Dynamics · Cross-listed from cs.LG
A Validated LBM Dataset and Pipeline for Surrogate Modeling of Turbulent 3D Obstructed Channel Flows
Lukas Schröder, Shubham Kavane and Harald Köstler
Evaluating neural operators for 3D turbulent flow requires validated
datasets with physical
benchmarks. We present a reproducible pipeline generating training data for 3D channel flows around generated geometries at Re=1,000-10,000. Our
lattice Boltzmann solver with cumulant collision operators is rigorously verified against experimental measurements (Strouhal number, drag coefficients, turbulent fluctuations) with comprehensive grid convergence studies at resolution 1024x512x512. Building upon an established framework, this validated pipeline enables standardized surrogate model comparison. We outline planned systematic evaluation of
Fourier Neural Operator and U-Net variants on
forecasting, super-resolution, and error correction tasks, using physics-informed metrics to assess turbulent energy cascade representation. Future work will compare computational efficiency between numerical solvers and neural surrogates, exploring practical application. We seek community feedback on our validation approach, planned
benchmark methodology, and evaluation priorities for neural operators in turbulent flows.
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