Optics · Cross-listed from cs.CV
Image-Derived PM10 Estimation in Cattle Feedlot Using Machine Learning: Addressing Concentration Ranges Beyond Existing Digital Imaging Methods
Sirapoom Peanusaha, Greg B. Ferguson, K. Jack Bush et al. (+2 more)
Affordable dust monitoring remains a pressing need for the cattle feedlot industry, yet camera-based PM estimation, despite its growing body of research in urban air quality settings, has not been evaluated under the extended concentration ranges characteristic of intensive livestock operations. This study developed an image-based approach using contrast panel features and machine learning to estimate PM10 concentrations in a commercial cattle feedlot, where hourly average PM10 ranged from 250 to 1,000 ug/m^-3 and instantaneous concentrations reached 5,000 to 20,000 ug/m^-3. Grayscale images were captured during the evening dust peak period, and features including panel contrast, black and white panel pixel values, and overall image brightness were extracted. The model also incorporated recent past values from preceding images and solar zenith angle as predictors. Among the candidate models evaluated, XGBoost achieved the highest predictive performance, with an R^2 of 0.792 and a median absolute error of 103 ug/m^-3. Feature importance analysis revealed that (a) panels positioned farthest from the camera contributed most strongly to predictions and (b) that black panel pixel values were more sensitive than white panel values to changes in PM10 concentration. Prediction accuracy during the sunset transition, which coincides with the onset of the feedlot evening dust peak, remains an area for further refinement. These findings demonstrate the feasibility of image-based PM10 estimation across PM concentration ranges substantially exceeding those reported in prior urban studies and provide practical guidelines for future deployment in feedlot environments.
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All authors Sirapoom Peanusaha, Greg B. Ferguson, K. Jack Bush, Peiyang Li, Brent W. Auvermann
Computational Physics
Warp-Geo: Differentiable Geometry Representation for Dynamic-Boundary Simulation and Shape Optimization
Weijie Zhang, Xiantao Fan and Jian-Xun Wang
Modern inverse problems and adaptive simulations require efficient and differentiable representations of complex geometries that can be updated frequently for gradient-based optimization. Existing methods cannot simultaneously achieve both. We present Warp-Geo, a differentiable GPU-accelerated framework for reconstructing signed distance fields and evaluating surface normals from point clouds of complex 3D geometries. Warp-Geo combines uniform grids for GPU parallelism with implicit differentiation through the Poisson solve, enabling frequent geometry recomputation while maintaining full differentiability. We demonstrate Warp-Geo on forward SDF reconstruction, dynamic fluid–structure interaction with moving boundaries, and inverse shape optimization via automatic differentiation with gradient validation. GPU scalability analysis confirms practical feasibility for frequent geometry updates. The framework, implemented in Warp and JAX, enables seamless integration with differentiable physics solvers for end-to-end shape optimization.
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Optics
Phase-Flow Topology of Bound States in the Continuum
Nikolai A. Vlasov, Varvara P. Panurchenko, Ravshanjon Kh. Nazarov et al. (+3 more)
Control of topological charges of bound states in the continuum (BICs) is essential for advanced topological photonics. A rigorous theoretical framework for understanding the formation of these charges is therefore necessary for further progress in this field. However, conventional multipolar formalism often fail to predict the topological charge when no single multipole dominates the mode. To address this fundamental issue, we present a rigorous dynamical systems framework for describing formation of topological charges. This attitude provides a direct link between polarization
vortex and the local structure of the vector polarization field. Within the proposed framework, we challenge the established identification of the topological charge with the Hopf index of the dominant multipole and demonstrate the significant role played by derivatives of the multipolar coefficients in determining the charge. Our theoretical framework is validated within semi-analytical multipolar decompositions and full-wave numerical simulations of periodic dielectric metasurfaces. Furthermore, we show how the
symmetry of the unit
cell influences the local structure of the polarization field around BIC and identify the necessary conditions for the formation of high topological charges. Our findings provide a more rigorous basis for analysing BICs' topological properties, paving the way for advanced applications in topological photonics.
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All authors Nikolai A. Vlasov, Varvara P. Panurchenko, Ravshanjon Kh. Nazarov, Stanislav S. Baturin, Ekaterina E. Maslova, Zarina F. Kondratenko
Optics
Physics-aware hyper-reduction for full-vector photonic waveguide mode solvers
Daniel Rodríguez-Guillén and Lorena Velázquez-Ibarra
Photonic waveguide design often requires repeated full-vector Maxwell eigenmode solves over wavelength and geometry, where the subpixel smoothing needed at dielectric interfaces makes operator assembly a substantial part of the cost. We present a physics-aware hyper-reduction framework for accelerating this assembly. The method exploits an exact affine dependence of the discrete operators on the locally sampled inverse-permittivity tensor, and chooses its strategy from how a parameter acts on the interface
cells. We demonstrate two regimes. In wavelength sweeps at fixed geometry, tensor values change on a fixed set of
cells, and proper orthogonal decomposition with
matrix discrete empirical interpolation reproduces the operators from few snapshots. In moving-boundary sweeps, interface information relocates across the grid, and event-driven dynamic local sampling updates only the coefficients a
geometric step changes, reproducing full assembly exactly. These results show that operator assembly can be reduced by more than an order of magnitude without approximating the moving boundary, shifting the cost of a photonic mode sweep to the eigensolver.
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Atomic Physics
Integration of the NIST Spectroscopic Plate Archive into the Harvard Plate Stacks
Jacob Ward, Thom Burns, Lisa Bravata et al. (+2 more)
For many decades, the National Institute of Standards and Technology (NIST) Atomic Spectroscopy Group housed a growing collection of thousands of photographic glass plate negatives. With the closure of the NIST Atomic Spectroscopy Group in March 2025, the irreplaceable collection needed a new home. Staff from NIST were contacted by staff from the Harvard Plate Stacks at the Center for Astrophysics | Harvard & Smithsonian (CfA). The Harvard Plate Stacks is the largest collection of astronomical glass plates in the world, with over 500,000 glass plate negatives from the 1880s to the 1990s. Given the long-standing history of the Harvard Plate Stacks and the expertise of the staff, it was decided that the NIST archive would move to the CfA. We summarize the contents of this collection regarding the instruments used and the spectra measured, and we present the tools and procedures we use for digitizing the collection. This includes the use of calibrated flatbed scanners for digitizing the plates themselves, as well as the digitization of correlated lab notebooks with important metadata associated with the plates.
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All authors Jacob Ward, Thom Burns, Lisa Bravata, Gillian Nave, Adam Foster
Chemical Physics
RECOB: Reliable Benchmarking of Experimental Optimization in Chemistry and Materials Science
Zikai Xie, Jiaming Wan and Linjiang Chen
Optimization methods for experimental science are often evaluated on synthetic functions that are reproducible but omit important characteristics of real experiments. We introduce RECOB (REliable Chem Optimization
Benchmark, Github repository: \hyperlink{https://github.com/XieZikai/RECOB}{https://github.com/XieZikai/RECOB}), a black-box optimization
benchmark constructed exclusively from data generated through physical experiments in chemistry and materials science. The suite contains 14 single-objective and two multi-objective tasks spanning chemical reactions, material formulations, electrochemical systems, continuous-flow processes, and automated laboratories. Each task provides a machine-readable specification of its decision variables, feasible domain, physical constraints, objective direction, and experimental provenance. Continuously queryable learned oracles are screened using repeated holdout validation and prespecified admission criteria, while measured-table replay enables evaluation using the original experimental responses. Under a common paired evaluation protocol, we compare ten single-objective and eight multi-objective optimization methods. HEBO achieves the best aggregate single-objective rank, while qNEHVI leads the multi-objective comparison. Model-based methods generally outperform non-adaptive baselines, although their computational overhead varies substantially. We further assess
benchmark reliability using independent oracle retraining and measured-table replay. Aggregate optimizer rankings remain highly consistent across retrained oracles, while replay preserves the broad performance hierarchy using only physically measured responses. Together, these results show that RECOB can reproducibly distinguish optimizer performance as an experimentally grounded and reliability-tested
benchmark for black-box optimization in chemistry and materials science.
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Computational Physics
How Does Distribution Shift Shape Pretraining Gains in Neural PDE Surrogates?
Pochinapeddi Sai Bhargav, Nithin Somasekharan, Rohit Sunil Kanchi et al. (+2 more)
Pretraining a neural PDE surrogate can reduce the amount of new CFD data needed when geometry or modeled physics changes. However, it remains unclear how different components of distribution shift affect this benefit. We pretrain a surrogate on 254,909 RANS solutions from one airfoil family and fine-tune it on a new family under two target settings with matched freestream ranges: the same Spalart-Allmaras (SA) modeling and SA with added $e^N$ transition modeling. At $N=1000$, the pretrained model matches the accuracy of a model trained from scratch on $3.25\times$ as many samples for the same-SA target, but $2.58\times$ as many for the transition-modeled target. By $N=5000$, this ordering reverses ($1.56\times$ versus $1.86\times$). At $N=1000$, sampling more distinct airfoils lowers error on both targets, but only for the same-SA target is the gain increase larger than the observed draw-to-draw variation ($3.3\times$ to $4.0\times$). These results show that pretraining value depends jointly on target-data budget, target-data coverage, and whether source and target differ in modeled physics.
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All authors Pochinapeddi Sai Bhargav, Nithin Somasekharan, Rohit Sunil Kanchi, Sicheng He, Shaowu Pan
Classical Physics · Cross-listed from cond-mat.stat-mech
Global Minima of the Thomson Problem in a Disk: A Molecular Dynamics Approach with Fixed Border Charges
Georgiy K. Lavrov and Eduard G. Nikonov
We report improved global-minimum configurations for the classical Thomson problem of $N=60$, $61$, $92$, and $99$ repulsive Coulomb charges confined to a disk. By combining the quenched molecular dynamics (QMD) method with the fixed-border heuristic introduced by Amore and Zarate, we systematically obtain configurations with energies $E_{\mathrm{QMD}}(60)=2159.3584240930$, $E_{\mathrm{QMD}}(61)=2237.19264190$, $E_{\mathrm{QMD}}(92)=5358.35353314$, and $E_{\mathrm{QMD}}(99)=6254.83029083$, which improve upon the previously best-known values. For $N=60$, the Voronoi diagram of our configuration differs from the one reported earlier. The
symmetries for $N=61$ $(C_{2})$ and $N=99$ $(D_{1})$ are confirmed and are consistent with the known
symmetry patterns for these configurations. For $N=92$, whereas the previously reported Voronoi diagram has lower
symmetry, our solution exhibits a clear $D_{1}$ (axial)
symmetry of defects. The results are highly reproducible across multiple independent runs, providing strong evidence that these configurations are robust global-minimum candidates.
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Space Physics
Microphysical Diversity in Two Very Closely Spaced Magnetic Switchbacks Observed by Parker Solar Probe
Dipali Vadher, Ankush Bhaskar, Smitha Thampi et al. (+1 more)
Parker Solar Probe observations near the Sun reveal frequent, sudden reversals of the
magnetic field known as switchbacks (SBs). Despite their ubiquity, the internal
plasma structure and associated heating within SBs remain poorly understood. We present a case study of two closely spaced SBs (referred in text as SB_1 and SB_2) observed on 24 January 2020 using high-cadence
magnetic and
plasma measurements.
Magnetic fluctuations are decomposed into components parallel and perpendicular to the mean field, and their power spectra are analyzed to characterize the turbulent cascade. The Partial Variance of Increments (PVI) method is applied to identify intermittent current-sheet-like features. Both SB intervals exhibit clear Alfvenic behavior and enhanced radial flow; however, their microphysics differ: SB_1 shows a higher
proton temperature, larger fluctuation amplitudes, and a denser population of current sheets compared to SB_2. The two events also differ in spectral index, with SB_1 exhibiting a steeper perpendicular slope than SB_2. The elevated intermittency,
proton temperature, and transient $\beta > 1$ excursion in SB_1 suggest that localized dissipation at small-scale structures is a plausible driver of the observed heating. These findings demonstrate that SBs are not uniform kinematic deflections but dynamically evolving
plasma structures whose internal
turbulence may regulate local energy conversion and contribute to the spatially intermittent heating of the near-Sun solar wind.
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All authors Dipali Vadher, Ankush Bhaskar, Smitha Thampi, Kamlesh Pathak
Physics and Society
Is higher-order physics different?
Pablo Villegas and Sandro Meloni
Group interactions are widespread, and higher-order extensions of familiar models display collective phenomena absent from their pairwise baselines, which are routinely offered as evidence of a distinct higher-order physics. We ask whether that claim survives the test that gives new a precise meaning in statistical physics: that of universality. Revisiting canonical higher-order models, we argue that what classifies collective behavior are the infrared ingredients that survive at long scales. Arity is not a universality label. Beyond universality, we examine two further questions: whether higher-order structure is a fact about the system or a choice of description, and what data can and cannot tell us about interaction order. Higher-order descriptions remain indispensable when they expose the organizing structure, provide a better mechanistic
language, or improve prediction. We close with what should be measured before new phenomena can be claimed, and where higher-order structure already earns its place.
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Atomic Physics
Effective Conservation and Bistability of Atomic Alignment under Strong Spin~Exchange
Anton K. Vershovskii
We present a phenomenological model of anomalous
alignment signals in dense cesium vapor under linearly polarized pumping and fast
spin exchange near zero
magnetic field. Despite the absence of a conservation law for rank-2 angular momentum, our recent experiments reveal anisotropic narrow resonances, hysteresis, and bistability. We attribute these effects to a stretched state forming a collective mode in which orientation and
alignment are bidirectionally coupled. This mode acts as a reservoir, preserving the essential properties of
alignment despite rapid
spin exchange.
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Fluid Dynamics
Solutions of the Navier-Stokes Equation Through Affine Transformations: The Triad Triplet
Ö. D. Gürcan, L. Manfredini and P. Morel
Considering triads in helical decomposition of three dimensional Navier-Stokes
turbulence, three consecutive triads of the same shape and class, where the reference wave-number appears as the smallest, the middle and the largest wave-numbers respectively, constitutes an interesting object called the triad triplet that allows tracing the local transfer in wave-number space due to a given shape and class of triads. It is shown that,
evolution equations for the triad triplet can be obtained from the equations of the three legs of a single triad that are put together via affine transformations involving scaling, rotation and reflection. Known power law solutions corresponding to constant flux of energy and helicity appear as exact solutions of a chain of triad triplets, with particular implications for the triadic phases. More interestingly, an exact, time dependent solution is available on an isolated triad triplet, that can be expressed using what appears to be straightforward generalization of Jacobi elliptic functions. These solutions, constitute novel exact nonlinear solutions of the inviscid limit of the Navier-Stokes equations.
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History and Philosophy of Physics · Cross-listed from hep-th
Young Gerard storming high energy physics
John Iliopoulos
On the occasion of Gerard 't Hooft's 80th birthday, I present my personal recollections of a scientific meeting held in the Orsay Campus of the University of Paris in January 1972. In this meeting Gerard gave for the first time a detailed account of his groundbreaking work on the renormalization properties of Yang-Mills theories, both in the massless phase as well as in the phase with spontaneous
symmetry breaking.
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Classical Physics · Cross-listed from nlin.CD
Experimental detection of energy transfer into the antiphase mode in a branched double pendulum
Yusuke Toda and Takeshi Ooshida
Multiple pendulum with branching is proposed as a convenient platform to study energy transfer between different modes. The antiphase oscillation mode is localized to the "child" links, which makes it easy to prepare initial conditions without exciting the antiphase mode. A manageable expression for the energy transfer is derived theoretically and evaluated with experimental data.
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Optics
Volumetric Evanescent Edge Coupling for Fiber-to-Chip Optical I/O
Hamdy Elshehaby, Omar Bakheet, Mohamed A. Swillam et al. (+1 more)
Scaling optical input/output for co-packaged optics is limited by the fiber-to-chip interface. Conventional edge coupling offers low loss and broad bandwidth but confines channels to a single row along the chip facet. On the other hand, surface couplers are either narrowband or difficult to fabricate with high yield and, importantly, compete with back-end metal routing. In this work, we introduce volumetric edge coupling, in which the chip edge is structured in three dimensions so that optical coupling can occur over a two-dimensional region rather than along a single line, providing a route toward interfacing multiple waveguides with multiple cores of a multicore fiber while preserving optical access from the chip perimeter. We investigate a single-channel realization based on total-internal-reflection (TIR)-mediated evanescent coupling through the 54.7$^\circ$ sidewall of a KOH-etched silicon cavity, with the coupling profile shaped by a wedged buried-oxide ridge. Finite-difference time-domain simulations predict a peak coupling efficiency of 88% (-0.56 dB) at 1550 nm and a 1-dB bandwidth of 86.45 nm spanning the C-band. The design further exhibits a vertical
alignment tolerance of approximately $\pm$2 $\mu$m, weak sensitivity to transverse offsets up to 5 $\mu$m, and a $\pm$0.8$^\circ$ 1-dB angular tolerance. The reflected optical field also provides a potential
alignment signal, offering a path toward reduced active
alignment overhead in future multicore implementations.
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All authors Hamdy Elshehaby, Omar Bakheet, Mohamed A. Swillam, Mohamed Elkabbash
Chemical Physics
Molecular Geometry Understanding Has Unintendedly Emerged in Frontier Large Language Models
Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov et al. (+3 more)
Large
language models (LLMs) have already shown strong capabilities in solving complex chemical problems expressed in natural
language. Yet, many tasks performed by chemists require understanding of 3D structures of chemical compounds and
reasoning about them – abilities, which, to the best of our knowledge, were neither intended by LLM developers nor tested in contemporary models. Such understanding is essential for autonomous molecular discovery and drug development, which is one of the Holy Grails of AI application to chemistry. We tested this capability in modern LLMs on 810 geometries and DFT energies of 27 small organic molecules. Strikingly, while models released before July 2025 struggled to rank conformers by stability, many recent frontier models, including GPT-5.6 Sol, Kimi K3, and Gemini 3.6 Flash achieved competitive accuracy, outperforming the Universal Force Field, and GPT-6 Astra and Claude Opus 5 came very close to a modern GFN-FF force field. Analysis of models' explanations for their rankings suggests that they perform best for molecules with well-defined intramolecular interactions – hydrogen bonds – which are well quantified in scientific
language; at the same time all models except the two newest – GPT-6 Astra and Claude Opus 5 – struggle with molecules governed by loosely defined concepts (e.g., ring strain), where force fields excel, suggesting that the scientific
language itself might impose constraints on LLMs. Notably, a model's ability to rank conformers is strongly associated with its performance on scientific, coding, and abstract-
reasoning benchmarks, suggesting that it emerged unintendedly from models training on linked but conceptually different tasks. Thanks to this generalization of mere scientific knowledge into understanding molecular structures, current frontier models offer a realistic starting point for AI-driven molecular design.
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All authors Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov, Stepan N. Ostarkov, Igor V. Alabugin, Michael G. Medvedev
Space Physics · Cross-listed from gr-qc
Gravity from Invariant Weyl-Integrable space-time (IWIST)
José Edgar Madriz Aguilar, A. Bernal, M. Montes et al. (+1 more)
In this paper we propose a geometrically motivated formulation of
gravity in an invariant Weyl–Integrable space-time (IWIST), in which the background geometry is not imposed a priori but is dynamically determined through the Palatini variational principle. For a scalar field non-minimally coupled to
gravity action, the resulting compatibility condition defines a Weyl–Integrable geometry which is preserved by a local Weyl transformation of the metric and the geo\-me\-tri\-cal scalar field. We construct an action invariant under both diffeomorphisms and Weyl transformations and introduce a Weyl-covariant variational procedure based on an invariant extension of the divergence
theorem. The corresponding field equations are obtained for the metric, the Weyl scalar, and the Weyl gauge field. We further formulate the theory in the Einstein–Riemann frame, where the effective metric is Riemannian and the scalar field is re\-inter\-pre\-ted as a phy\-si\-cal degree of freedom of
geometric origin. Finally, we propose a geometrical mechanism for the explicit breaking of Weyl
symmetry by introducing a coupling between the Weyl gauge field and a current constructed from the scalar sector. The resulting theory remains diffeomorphism covariant while departing from local Weyl invariance. The Einstein–Riemann representation of the broken theory contains an additional generally covariant interaction, making the Weyl-invariant model a particular limit of a more general gravitational theory.
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All authors José Edgar Madriz Aguilar, A. Bernal, M. Montes, J. Zamarripa
Optics
A Simplified Model for Linear Mode Coupling in Multimode Fibers with Experimental Assessment
Paolo Carniello, Filipe M. Ferreira, Fabio A. Barbosa et al. (+2 more)
We propose a novel surrogate model for linear coupling in graded-index multimode fibers that is computationally more efficient than existing models, and can be tuned through a single measurable parameter. The model is benchmarked numerically and experimentally on a 90-polarization-mode fiber.
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All authors Paolo Carniello, Filipe M. Ferreira, Fabio A. Barbosa, Ming-Jun Li, Norbert Hanik
Optics
Quantum Battery Enhancement via Degenerate Optical Parametric Amplifier and Common Reservoirs
Y. Y. Yang, H. N. Liu, Gangcheng Wang et al. (+2 more)
We investigate a nonreciprocal
quantum battery in which the charger and battery share one or two common reservoirs, with the charger driven by a degenerate optical parametric amplifier (DOPA). Reservoir-mediated dissipative interactions enable directional energy transfer, while the DOPA provides additional parametric am plification. We show that, in the stable below-threshold regime, the DOPA can enhance the battery energy and average charging power, with the nonlinear gain and pump phase providing effective control of the charging dynamics. Further optimization of the dissipative-coupling asymmetry improves the stored energy. These fea tures persist for both single- and two-common-reservoir configurations, demonstrating a tunable strategy for enhancing open-system
quantum-battery charging.
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All authors Y. Y. Yang, H. N. Liu, Gangcheng Wang, T. Z. Luan, H. Z. Shen
Biological Physics
The Motile-Units model: Interacting spins model of cell polarization and motility
Jonathan E. Ron and Nir S. Gov
We introduce a coarse-grained interacting-
spin model for two-dimensional
cell motility, in which the
cell perimeter is discretized into
stochastic binary
spins that switch between active and inactive states. Each perimeter
spin represents a "motile-unit" that is a source of protrusive force and retrograde flow when active. Long-range interactions between the motile-units arise through a polarity cue advected by the collective actin retrograde flow, providing a minimal realization of spontaneous
symmetry breaking and self-propulsion. The model exhibits three dynamical phases, a random walk phase, persistent random walk phase, and an intermittent bistable phase characterized by run-and-tumble migration. Additional nearest-neighbor interactions modulate speed and persistence without altering the overall phase structure. Owing to its simplicity, the framework naturally incorporates external cues, reproducing chemotactic migration, steering by localized optogenetic activation, and directional decision-making (
symmetry breaking) under competing stimuli. The model introduces a new class of active-particle model in which both speed and polarity emerge from internal
stochastic spin dynamics, rather than being imposed as particle-level variables, offering a framework for the study of
cell migration and extends the scope of active-matter physics.
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Geophysics
Multi-Track Time-Series Burst-Overlap Interferometry for Resolving Horizontal Deformation in Earthquake-Cycle Studies
Xing Li, Zhuang Gao, Han Chen et al. (+3 more)
Interferometric Synthetic Aperture Radar (InSAR) is intrinsically insensitive to the north-south component of crustal motion because of its near-polar geometry, which makes accurate quantification of this deformation a persistent limitation of InSAR. We introduce a Multi-track Time-Series Burst-Overlap Interferometry (MTSB) framework that delivers absolute, ITRF-referenced deformation fields across representative interseismic, coseismic, and postseismic applications by combining optimized phase linking, a unified time-series framework for separating deformation and residual misregistration, and block-wise spectral analysis. Case studies spanning co-, inter-, and postseismic phases demonstrate that MTSB accurately resolves along-track horizontal deformation, with primary sensitivity to north-south motion, while substantially reducing orbit-related artifacts. Independent comparisons with GNSS indicate centimeter-level agreement for postseismic and coseismic displacement estimates and millimeter-per-year agreement for interseismic velocities. The resulting deformation fields improve constraints on regional plate kinematics and fault kinematics, facilitate more reliable Euler-pole estimation, and provide by-product orbital corrections for conventional InSAR processing.
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All authors Xing Li, Zhuang Gao, Han Chen, Zhangfeng Ma, Yangkang Chen, Alexandros Savvaidis
Optics
High-order correlations and ultrafast Wigner negativities in bright-squeezed-vacuum-driven high-harmonic generation
Sebastián de-la-Peña, Heiko Appel, Marcelo F. Ciappina et al. (+2 more)
High-harmonic generation (HHG) is a prototypical strong-field process in which intense light drives matter to emit radiation at integer multiples of the driving frequency. Extending HHG into the
quantum-optical regime offers new opportunities to probe and control strongly nonlinear light-matter interactions using nonclassical states of light. Yet describing this regime requires a fully
quantum treatment of the correlated electron-
photon dynamics, which becomes computationally challenging for broadband, strongly squeezed fields. Here we solve the
quantum-electrodynamical dynamics of a two-level system driven by bright squeezed
vacuum in a converged multimode
Hilbert space. Both the driving field and emitted harmonics are fully quantized, with the light-matter interaction treated nonperturbatively. This enables direct access to the multimode
quantum state and its higher-order correlations beyond semiclassical sampling or perturbative descriptions. We show that squeezed-
vacuum driving produces harmonic emission with qualitatively distinct second- and third-order
photon correlations compared with coherent excitation. Moreover, back-action from the driven emitter strongly reshapes the incident squeezed field, generating pronounced Wigner-function negativities that evolve on attosecond timescales. Our results establish a fully
quantum framework for broadband strong-field dynamics with squeezed light and provide a route to predicting and interpreting
quantum-HHG experiments and their extension to more complex emitters.
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All authors Sebastián de-la-Peña, Heiko Appel, Marcelo F. Ciappina, Ofer Neufeld, Angel Rubio
Instrumentation and Detectors
Mineral Detection of Neutrinos and Dark Matter 2026 Proceedings
Alexey Elykov, Patrick Stengel, Natsue Abe et al. (+81 more)
The fourth "Mineral Detection of
Neutrinos and Dark Matter" (MDvDM'26) meeting was held April 14-17, 2026 in Karlsruhe, Germany, hosted by the Institute for Astroparticle Physics (IAP) at Karlsruhe Institute of Technology (KIT). These proceedings detail the contributions that were presented during MDvDM'26, illustrating the unprecedented progress in theoretical, computational and experimental studies towards the realization of the concept of mineral detectors. Mineral detectors represent an emerging particle detection concept that has risen in prominence in recent years due to the advent of modern computational and high-resolution microscopy techniques. Natural and synthetic
crystals are capable of retaining microscopic damage features induced by nuclear recoils, which could be then read out with a variety of micrometer and nanometer resolution microscopy techniques. On laboratory time scales mineral detectors could be employed for reactor
neutrino monitoring and dark matter detection, with the potential to measure the directions as well as the energies of the induced nuclear recoils. Uniquely, ancient natural
crystals (so-called paleo-detectors) that have been recording nuclear recoils over geological timescales could be used for studying astrophysical
neutrinos,
cosmic rays, dark matter and heavy exotic particles, as well as the variation of their fluxes over our
Galaxy's lifetime. In recent years the international MDvDM community has been successfully tackling the challenges associated with realizing the concept of mineral detectors, opening the pathway towards a fully fledged experimental program and potential future discoveries.
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All authors Alexey Elykov, Patrick Stengel, Natsue Abe, Daniel Ang, Lorenzo Apollonio, Levente Balogh, Laura Baudis, Chinmay Bharathulwar, Priyanshu Bhattacharya, Yilda Boukhtouchen, Joseph Bramante, Vincent Breton, Andrew Buchanan, Jens Burkhart, Lorenzo Caccianiga, Andrew Calabrese-Day, Mason Camp, Jordan Chapman, Anson Cook, Reza Ebadi, Denis Erkal, Katherine Freese, Audrey Fung, Shota Futamura, Claudio Galelli, Kevin Gao, Peter W. Graham, Thomas Haddock, Minako Hashiguchi, Alexander Hayes, Adam A. Hecht, Samuel C. Hedges, Shigenobu Hirose, Luisa M. Hötzsch, Patrick Huber, Yohei Igami, Vsevolod Ivanov, Florian Jörg, Ayuki Kamada, Takenori Kato, Yoji Kawamura, Katharina Kehl, Chris Kelso, Holger Kluck, Emilie M. LaVoie-Ingram, Matthew Leybourne, Gavishta H. M. Liyanage, Thalles Lucas, Brenden A. Magill, Paolo Magnani, Jennika McIntosh, Naoki Mizutani, Kohta Murase, Tatsuhiro Naka, Lina Necib, Pranav Parvathaneni, Andre Peterson, Zachary S. C. Picker, Rabeya Rabu, Harikrishnan Ramani, Anupam Ray, Morteza Roostaeinia, Hannah Ross, Issei Saikyo, Lukas Scherne, Maximilian Shen, Aaron Shugar, Joshua Spitz, Kai Sun, Jiashen Tang, Erwin H. Tanin, Dionysios P. Theodosopoulos, Yoichi Usui, Pieter Vermeesch, Aaron Vincent, Ronald Walsworth, Jin-Wei Wang, David Waters, Samuel S. Y. Wong, Audrey Wu, Gregory Wurtz, Wen Yin, Xiuyuan Zhang, Zhexian Zhang
Optics
Path-Integrated Polarization Rotation Signatures in Subsea Networks: Cable Geometry Effects in 2023 Turkey Earthquakes
Mohammad M. Hosseini, Miquel Masanas, Giuseppe Parisi et al. (+5 more)
We analyze polarization data streamed from a commercial coherent transponder, used in MedNautilus link, during 2023 Turkey earthquakes. We show how earthquake features and cable geometry fundamentally govern the optical polarization response, taking the first steps for detailed interpretation in designing fiber-based seismic sensing systems.
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All authors Mohammad M. Hosseini, Miquel Masanas, Giuseppe Parisi, Wesley Braga Melo, Alberto Marullo, Danilo Decaroli, Sergei K. Turitsyn, Antonio Napoli
Optics · Cross-listed from quant-ph
Noise-Robust Quantum State Characterization for Remote State Preparation with Deep Learning
Bo Tang, Zixuan Liao, Hao Li et al. (+9 more)
Quantum communication underpins secure information processing and scalable
quantum networks. In particular, remote state preparation (RSP) enables efficient
quantum state transfer, but accurately estimating target states under complex noise remains challenging. Here, we propose a
Transformer-based
Quantum State Characterizer (TQSC) model for noisy RSP experiments. Our model reconstructs experimentally prepared pure and mixed photonic polarization states from noisy measurements in complex scattering environments, while its
attention patterns provide physically grounded insights into correlations among the measured observables. The method achieves a mean
estimator-target fidelity exceeding 99.999% under complex scattering and dynamic Gaussian noise, while its robustness and generalization are further examined using Qiskit-simulated Bloch-ball states. Furthermore, in a practical MNIST image transmission task with held-out states, the decoded bit error rate is reduced from 50.34% to zero after TQSC post-processing. The TQSC model enables accurate tomographic characterization under dynamic noise and provides physically grounded post-hoc insights, holding promise for intelligent
quantum information processing applications.
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All authors Bo Tang, Zixuan Liao, Hao Li, Yilin Yang, Jiani Lei, Zengya Li, Jing Qiu, Zhaohui Dong, Zhengyang Mao, Yuanhua Li, Yuanlin Zheng, Xianfeng Chen
Chemical Physics
Truncated automatic sparse differentiation for machine learning interatomic potentials
Marcel F. Langer, Adrian Hill and Michele Ceriotti
Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy. The forces, the negative gradient of this energy, drive molecular dynamics and are readily obtained using automatic differentiation. Higher-order derivatives, most notably the Hessian, describe collective motion and allow the direct prediction of experimental observables, but are considered computationally inaccessible for large systems. We suggest a solution: in physical systems, interactions decay with distance, and most MLIPs build on this locality through message passing up to a finite receptive field. This implies both sparsity of higher-order derivatives and their decay with distance. This structure can be exploited using automatic sparse differentiation (ASD). We explain how to compute the sparsity pattern for MLIP derivatives and demonstrate that, for multiple foundation MLIPs, ASD computes full Hessians of large porous materials exactly, but with modest speedups at best. The larger gains come from truncated ASD: discarding small, but nonzero, Hessian entries between distant atoms yields order-of-magnitude speedups with negligible impact on predicted observables.
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Chemical Physics
Collective Ion Dynamics from Finite-Volume Fluctuations in Model Explicit-Solvent Electrolytes
Jeongmin Kim
Understanding how collective ion transport emerges from
equilibrium fluctuations is central to electrolyte statistical mechanics. Finite-volume fluctuations provide an accessible route to this information, but their interpretation is complicated because they mix wave numbers and collective fields. Here, we extend the finite-volume counting framework for inferring collective ion
diffusion by combining the exact window projection with an inertial independent-particle reference, and apply it to a symmetric 1:1 solvent primitive model. The growth of ion-number fluctuations between the ballistic and plateau regimes appears nearly ideal, but this apparent ideality results from compensation between negative structural and positive dynamical excess contributions. The coupled ion-number–solvent relaxation further reveals a signed redistribution between solvent-associated and solvent-orthogonal projections that is largely hidden in the total response. The inferred collective
diffusion depends on observation length and dynamical closure: measured structure alone does not systematically improve the estimate, whereas the coupled-field description reduces the high-concentration discrepancy. These results provide a basis for analyzing more realistic electrolytes with mutually coupled ion-number, charge, and solvent fluctuations.
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Computational Physics
Mechanical resilience of ultra-low-density racing-shoe foams
Jeremy A. McCulloch and Ellen Kuhl
High-performance racing shoes rely on ultra-low-density elastomeric foams that undergo large, repeated deformations during running. Yet little is known about how their mechanical properties vary throughout the shoe or change with repeated use. Here, we characterize the midsole foam in an elite-level racing shoe from the heel, midfoot, and toe of a new shoe and a shoe worn for 300 miles. Microscopy reveals a characteristic pore length scale of 128+/-18 um and supports an approximately isotropic continuum description. We then quantify the mechanical response under tension, compression, and shear. Remarkably, despite 300 miles of real-world use, the foam retains its mechanical response across all three loading modes and shoe regions. Energy return remains largely unchanged, with values of 85-93% in tension and compression and 64-71% in shear. At the same time, we observe strong regional variations, with tensile and compressive stiffnesses 38-51% lower in the toe than in the heel. The foam also exhibits a pronounced tension-compression asymmetry in Poisson's ratio. Together, these findings reveal a spatially structured and mode-dependent mechanical response that remains largely preserved after 300 miles of use. This mechanical resilience may extend the functional lifetime of racing shoes, with implications for runners, replacement recommendations, and sustainability.
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Atomic Physics · Cross-listed from quant-ph
Neutral atom quantum computing for materials science and quantum chemistry
J. D. Pritchard
Neutral atom arrays have emerged as versatile platforms for performing both digital and analogue
quantum computing and simulation, with demonstrations ranging from large-scale programmable
Hamiltonians realising topological phases or weighted graph optimisation to error-corrected logical
qubits with transverse gate operations. This paper provides a broad overview to the neutral atom platform, and the potential applications relevant to materials science and
quantum chemistry.
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Computational Physics
Correlation-Free Transition Path Sampling through Shooting Point Generation Guided by Committor Learning
Maximilian Negedly, Sebastian Falkner, Alessandro Coretti et al. (+1 more)
Studying the dynamical behavior of a system often depends on characterizing how it transitions between long-lived states. Because such transitions are rare, observing them usually requires specialized enhanced sampling techniques. Transition Path Sampling (TPS) is a well-established method for generating reactive trajectories, which is simple to implement and does not require the definition of a preconceived reaction coordinate. However, its efficiency is limited by its sequential nature and the resulting correlations between sampled paths. Previous work addressed this limitation by combining TPS with a sampling scheme based on conditioned Boltzmann Generators, a generative machine learning model capable of sampling a given target probability distribution. This approach produces uncorrelated transition paths but relies on an accurate reaction coordinate, which is rarely known in advance. Building on recent advances in committor learning, specifically on the Artificial Intelligence for Molecular Mechanism Discovery (AIMMD) method, in this work we introduce GenAIMMD, an iterative
algorithm that actively and self-consistently learns the ideal reaction coordinate (the committor) and trains a conditioned Boltzmann Generator to sample from arbitrary bias windows along it. GenAIMMD thereby provides a correlation-free and fully parallelizable path sampling scheme that does not require prior knowledge of the system's transition mechanism. We apply GenAIMMD to a two-dimensional toy model and a higher-dimensional
polymer system. In both cases, GenAIMMD succeeds in training the Boltzmann Generator and learning the committor.
Benchmark results show a substantial increase in performance compared to standard TPS.
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All authors Maximilian Negedly, Sebastian Falkner, Alessandro Coretti, Christoph Dellago