General Physics
Effect of $R^2$ on the stability of de Sitter solution of the generalized Einsteinian cubic gravity
Tuan Q. Do
In this paper, we would like to investigate whether a generalized Einsteinian cubic
gravity, in which three possible cubic interactions ${\cal P}$, ${\cal C}$, and ${\cal C}'$ are treated on an equal footing, admits a de Sitter solution as its stable cosmological solution. As a result, we are able to confirm the existence of the corresponding de Sitter solution for this
gravity by solving analytically its field equations. Remarkably, only the cubic interaction ${\cal P}$ gives rise to the existence of the de Sitter solution. Then, we convert the field equations into the corresponding dynamical system for a stability analysis purpose. A fixed point of this dynamical system is found and shown to be equivalent to the obtained de Sitter solution. However, the perturbed dynamical system turns out to be incomplete, leaving undetermined information of the stability of the fixed point (or equivalently the de Sitter solution). Fortunately, we show that this loophole can be cured once the well-known Starobinsky term $R^2$ is introduced into the action of the generalized Einsteinian cubic
gravity, despite the fact that it contributes nothing to the value of the de Sitter solution.
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Atomic Physics
Self-calibrated multiparameter measurement of three-dimensional microwave fields
Yupeng Wang, Xinghan Wang, Aishik Panja et al. (+3 more)
Rydberg atoms are promising for microwave (MW) sensing and control, but full local MW characterization remains difficult. Existing methods generally do not provide self-calibrated reconstruction of the three-dimensional vector field, which is valuable for both atom-based sensing and in-situ field characterization in complex electromagnetic environments. We propose and implement multi-level, Zeeman-resolved Rydberg electromagnetically induced transparency (EIT) spectroscopy in a
laser-cooled atomic ensemble. We extract the three polarization amplitudes from a single spectrum and show that the MW polarization components give rise to closed interferometric loops within the atoms' internal
Hilbert space, enabling extraction of their relative phases. Moreover, it is self-calibrated and requires no external reference MW fields, with MW parameters largely separable from one another and from other experimental parameters. These features make it broadly applicable to dedicated sensing platforms as well as
quantum optics and
quantum information experiments.
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All authors Yupeng Wang, Xinghan Wang, Aishik Panja, Md. Ehsanuzzaman, Chuan-Hsun Li, Qi-Yu Liang
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.SR
Understanding hydrodynamical wave-driven shear mixing in stellar radiation zones. Looking in the mirror of the dyapicnal oceanic mixing
Stéphane Mathis
Stellar radiation zones play a key role in the long-term magneto-rotational and chemical
evolution of
stars. As parts of the oceans and of the atmosphere of the Earth, their dynamics is controlled by the Archimedean buoyancy force and the Coriolis acceleration. They are the seat of an efficient extraction of angular momentum and of a mild mixing of chemicals. In this context, particle tracing in recent nonlinear hydrodynamical equatorial numerical simulations of
stellar radiation zones where internal
gravity waves (hereafter IGWs) are propagating led to the measurement of an effective diffusivity following the prescriptions derived by Garcia-Lopez & Spruit and by Zahn for the inflectional instability of the vertical shear of low-frequency IGWs. However, the associated instability criteria are not fullfiled. This effective diffusivity is found to scale as the squared velocity of IGWs for every rotation rates. Other dependences have also been derived in the literature, for instance in the case of the Stokes displacement. To interpret these results, we propose to explore the parameterisation for the mixing of particles, which has been proposed for the oceans. A foundation stone in physical oceanography is the so-called Osborn & Cox energetic balance that leads to an effective dyapicnal diffusivity for the transport of matter that scales as the ratio of the dissipation of the fluctuating flows over the squared Brunt-Väisälä stratification frequency. We demonstrate that this diffusivity is equivalent to the eddy diffusivity derived by Zahn for the inflectional instability of the vertical shear applied to low-frequency IGWs. This allows us to characterize the corresponding energetic balance where the power extracted by the waves from the mean flows is balanced by their dissipation and by the power produced by their buoyancy flux, which triggers mixing, for any rotation rate.
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Fluid Dynamics
Accelerating Bayesian inverse design in computational fluid dynamics using neural operators
Bipin Tiwari and Omer San
Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertainty. However, its practical use in computational fluid dynamics (CFD) is severely limited by the cost of repeated high-fidelity simulations required for gradient-based Markov chain Monte Carlo (MCMC) sampling. While surrogate models are commonly proposed to reduce this cost, their effect on posterior geometry and uncertainty, especially for shock-dominated flows, remains poorly understood. In this work, we demonstrate that neural operator surrogates can be embedded directly within the MCMC inference loop while preserving posterior structure. Using a fully
Bayesian inverse formulation of quasi-one-dimensional nozzle flow, we demonstrate that geometry parameterization plays a decisive role in identifiability and posterior conditioning, with cubic B-splines yielding stable and physically meaningful uncertainty estimates. Building on this formulation, a Deep Operator Network trained on CFD-generated data is substituted for the CFD solver within a No-U-Turn Sampler, while keeping the likelihood model, priors, and sampling configuration unchanged. Across sparse to fully observed regimes, surrogate-based inference reproduces the posterior geometry and uncertainty trends of the CFD reference. As a result of surrogate integration, total inference time is reduced to under one second, corresponding to a speedup exceeding three orders of magnitude. In addition, a direct inverse neural operator is examined as a deterministic alternative for inverse design, enabling single-shot geometry reconstruction without posterior sampling. These results demonstrate that neural operator-accelerated
Bayesian inference enables practical, uncertainty-aware inverse design workflows for aerodynamic applications.
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Optics
Absolute Experimental Measurement of Intrinsic Helicity in Elementary Plasmonic Nanostructures
Malo Bézard, Simon Garrigou, Jérémie Béal et al. (+5 more)
Helicity is a universal quantity describing the internal rotation of an object or a field. Whether it characterizes fundamental properties such as the
spin of elementary particles or leads to practical consequences such as the toxicity or harmlessness of
chiral molecules, defining and measuring it is essential. However, this remains ambiguous in the case of
chiral photonic systems such as plasmonic nanoparticles or photonic metasurfaces, where contrary to common knowledge, the observation of circularly polarized dependent optical properties is not a relevant measure for the helicity. We demonstrate experimentally and theoretically that the helicity can be rigorously defined and measured in a nanoplasmonics system emitting circularly polarized light after excitation in the near-field by a focused electron beam. In the case of a model system composed of two plasmonic dipoles (Born-Kuhn systems), we show that the helicity of photonic modes takes on a very intuitive form and can be simply measured by symmetrizing the geometry of excitation and detection. The method could be extended to a variety of
chiral photonic systems, whose locally enhanced properties make them promising for the engineering of local chirality.
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All authors Malo Bézard, Simon Garrigou, Jérémie Béal, Andreas Horrer, Yves Auad, Hugo Lourenço-Martins, Davy Gérard, Mathieu Kociak
Computational Physics
Uncovering multi-channel magnetic hopfion annihilation via a single-node, billion-spin-scale atomistic framework
Qichen Xu and Anna Delin
Modern atomistic
spin simulations combine long
stochastic trajectories, thermodynamic sampling, static optimization and multi-image transition-path workflows, all of which rely on repeated evaluation of
spin Hamiltonians and become computationally prohibitive on the large
lattices required for three-dimensional
magnetic textures. We introduce SpinX, a GPU-native atomistic
spin simulation framework built around a unified
Hamiltonian interface and multiple user-selectable computational backends. Its core is a crystallographic sublattice decomposition that reformulates translationally invariant
spin interactions as multi-channel tensor convolutions, enabling dense, sparse and FFT-based convolution backends, while irregular systems are handled by pair-list evaluation and long-range dipolar fields by reciprocal-space FFT. Implemented in JAX, SpinX supports deterministic and
stochastic Landau-Lifshitz-Gilbert dynamics, Monte Carlo sampling, static optimization, dynamical spectroscopy and string and geodesic nudged elastic band transition-path calculations on heterogeneous accelerator platforms. A validated mixed-precision mode combines fp32 field evaluation with fp64
spin-state propagation. We validate SpinX against analytical single-
spin dynamics, finite-size thermodynamics of bcc Fe and transverse dynamic structure factors. Performance
benchmarks show peak throughput exceeding 10 billion
spin-site operations per second on a single accelerator and aggregate single-node workloads of over 1 billion atomic
spins. Applying this framework to an exchange-stabilized
magnetic hopfion, we uncover two competing annihilation channels on a million-
spin atomistic
lattice: a previously reported axial-collapse pathway and a distinct lateral-rupture pathway with a different transition morphology and activation barrier.(Due to arXiv's limit, the abstract shown here is a shortened version)
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Biological Physics · Cross-listed from cond-mat.soft
Beyond Gaussian Statistics in Polymer Melts: Statistical Masking of Persistent Local Constraints
José A. Martins
Short
polymer chains exhibit clear deviations from Gaussian end-to-end distance statistics, yet the molecular mechanism by which Gaussian behavior is recovered in long chains remains unestablished. Atomistic molecular dynamics simulations of polyethylene melts reveal that conformational heterogeneity persists at the Kuhn scale across all chain lengths, consisting of a mosaic of slow-relaxing, extended aligned chain segments (ACS) and coiled segments – random conformational sequences (RCS) and chain ends (CE). We show that the end-to-end distance distributions for both unentangled and entangled chains are accurately described by a $q$-Gaussian function, with the entropic index $q$ increasing systematically from $0.67$ (C50) to $0.99$ (C500). This
evolution tracks the emergence and accumulation of RCS segments, which are absent in short chains, establishing $q$ as a quantitative “heterogeneity index”. The $q < 1$ values are a signature of non-extensive statistics, with the ratio of Tsallis to Boltzmann-Gibbs
entropy ($S_q/S_1$), computed directly from simulation data without fitting, decreasing from $1.80$ (C50) to $1.03$ (C500). Crucially, we demonstrate that Gaussian recovery does not result from the erasure of Kuhn-scale heterogeneities, as ACS domains persist in all chain lengths above the critical mass ($\approx 35\%$). Instead, the transition to Gaussian statistics is a statistical masking effect, where the accumulation of independent RCS segments progressively obscures the non-Gaussian signatures of the persistent ACS domains.
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Physics Education · Cross-listed from cond-mat.stat-mech
Boltzmann Distribution from Invariance of Coarse-Graining-Scale and Energy-Shift
Weicheng Fu, Yisen Wang, Yong Zhang et al. (+1 more)
We present a concise derivation of the Boltzmann form for single-particle energy distributions in classical many-body
Hamiltonian systems. The derivation relies on two physical facts: coarse-graining-scale invariance of the empirical distribution and invariance under a uniform shift of the energy zero. These conditions uniquely yield the Boltzmann factor, whose parameter is fixed by the mean energy per particle. For separable
Hamiltonians, the
equilibrium weight factorizes into kinetic and configurational contributions sharing the same parameter, identified from the kinetic part as the inverse kinetic temperature. The principle extends to any physical quantity with a stationary distribution and translational invariance. It is illustrated in a one-dimensional diatomic hard-core gas and a nonlinear
lattice chain, where it predicts velocity, energy, spacing, collision-time, and pressure-dependent displacement distributions in agreement with simulations. The
lattice model further shows how harmonic elasticity, anharmonic corrections, internal pressure, and thermal expansion emerge from the same exponential
equilibrium weights. Finally, the relationships among different ensembles are briefly discussed.
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All authors Weicheng Fu, Yisen Wang, Yong Zhang, Hong Zhao
Instrumentation and Detectors
DetMesh-Gadep: Triangulated Surface Modeling and GPU-based Monte Carlo Efficiency Calibration of High-Purity Germanium Detectors
Kainan Zhang, Shuchang Yan, Zhen Wu et al. (+3 more)
Sourceless efficiency calibration of high-purity germanium (HPGe) detectors can provide accurate detector-response information without experiments using radioactive calibration sources, offering advantages in both convenience and safety. In many practical implementations, this process is performed using Monte Carlo simulation; however, its performance is constrained by the accuracy of detector modeling, the operational complexity of simulation frameworks, and the computational-resource requirements associated with CPU-based parallelization. In this study, a complete detector modeling and simulation framework is proposed. The detector modeling program DetMesh can generate triangulated surface geometry from parameterized detector models, providing advantages in the representation of complex
geometric boundaries. It incorporates standard
geometric operations and a
geometric library, and is lightweight with strong extensibility. The generated geometry is then input into Gadep, a GPU-based Monte Carlo computational kernel, to enable rapid simulation. For $1\times 10^8$ particles, a single RTX 4090 achieved a speedup factor of 13.53 compared with simultaneous computation using 60 CPU cores. The proposed framework has low implementation cost and broad applicability, providing a complete solution for refined detector modeling and calibration.
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All authors Kainan Zhang, Shuchang Yan, Zhen Wu, Hui Zhang, Rui Qiu, Junli Li
Computational Physics · Cross-listed from cs.LG
Small Models, Strong Priors: Architectural Inductive Bias for Parameter-Efficient Neural PDE Solvers
Shyam Sankaran, Hanwen Wang and Paris Perdikaris
Neural PDE solvers have followed the scaling trajectory of
vision and
language, with recent foundation models reaching billions of parameters. We argue that scale is a poor substitute for architectural inductive bias in this domain: structured priors deliver outsized parameter efficiency, and the pattern of where they succeed and fail is itself informative about what they capture. We instantiate this argument in WaveLiT, an architecture combining a discrete wavelet transform for lossless multi-resolution tokenization, an augmented linear
attention block, a shared-weight multiscale feature pyramid, and a wavelet-domain auxiliary loss. Bespoke 1-10M-parameter WaveLiT models compete with foundation models of 100-1000$\times$ their size across eight TheWell
benchmarks, with the largest gains on wave and acoustic-dominated
benchmarks where the wavelet-multiscale prior fits the dominant dynamical structure and small per-step errors do not compound geometrically under rollout. Trained jointly across all eight
benchmarks, a 10M-parameter foundation variant exhibits a structured, physically interpretable transfer pattern – strongest where the wavelet-multiscale prior matches the dynamics, weakest on chaotic advection-dominated flows. The entire pipeline trains on a single GPU. The results suggest that small-model PDE performance is shaped by architectural inductive bias rather than scale, and that the structure of a prior's failures is a useful empirical signal about its content.
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Fluid Dynamics · Cross-listed from cond-mat.other
Mutual Friction in Dissipative Gross-Pitaevskii Thermal Counterflow Turbulence
Kyo Yoshida, Hideaki Miura and Yoshiyuki Tsuji
We report numerical simulations of the dissipative Gross-Pitaevskii equation for a bulk region of thermal-counterflow
turbulence. Quasistationary states are obtained over a range of forcing, damping, and healing-length parameters. The mutual-friction acceleration exhibits cubic scaling with the mean relative velocity between the superfluid and normal-fluid components, and the coefficient of this scaling is linked to the phenomenological damping parameter. The intervortex spacing follows the expected dimensional scaling in the weak-forcing regime. Comparison with a straight-
vortex-line model suggests that the
vortex-line orientations are nearly isotropic.
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Optics · Cross-listed from quant-ph
Implementation of distillation protocols using a recirculating bricks mesh network
Jacek Gosciniak
General-purpose programmable photonic processors provide a flexible foundation for integrating various functionalities within a single chip. A two-dimensional bricks waveguide mesh of Mach Zehnder interferometers has been demonstrated to possess considerable potential in the domain of photonic neural networks and
quantum signal processing. In this article, we propose an expansion of the available applications of recirculating bricks mesh architecture to
distillation protocols necessary for
quantum signal processing. These protocols are essential for the heralding of the output of single
photons, which is characterized by a reduced distinguishability error rate. The demonstration will be made of a single programmable optical system's ability to realize various
distillation protocols with reduced computational resource costs. The present study will concentrate on cascaded
quantum interferometers and
Fourier transform-based schemes. It will demonstrate that the bricks mesh can implement such schemes, which are unattainable using feed-forward networks, without the need for complex out-of-plane integration. The propagation of the signal in any direction, along with the utilization of all ports as both input and output, facilitates the execution of such transformations with minimal optical depth of the
circuit and in time scales shorter than the decoherence time.
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Chemical Physics
Excess molar enthalpies of (iodobenzene, or 1-iodonaphthalene + n-alkane) liquid mixtures at T = 298.15 K and p = 93 kPa
Fernando Hevia, Luis Felipe Sanz, Juan Antonio González et al. (+2 more)
Excess molar enthalpies ($H_{\text{m}}^{\text{E}}$) for iodobenzene, or 1-iodonaphthalene + heptane, + decane, + dodecane, or + tetradecane mixtures at 298.15 K and 93 kPa have been measured using a Tian-Calvet micro-calorimeter. The values of $H_{\text{m}}^{\text{E}}$ are positive and indicate that interactions between like molecules are prevalent. In contrast, our previous results on excess molar volumes ($V_{\text{m}}^{\text{E}}$) are negative for the systems C$_6$H$_5$I + heptane, or 1-iodonaphthalene + n-alkane, which reveal the existence of large structural effects in such solutions. This set of measurements has been used to determine isochoric excess molar internal energies ($U_{V\text{m}}^{\text{E}}$). In the range of n-alkanes considered (n is the number of C atoms of the alkane), values of $U_{V\text{m}}^{\text{E}}$ at equimolar composition decrease from n = 7 to n = 10 and then slightly increase for systems with C$_6$H$_5$I, while decrease slowly for mixtures with 1-iodonaphthalene. These trends fit well with the patterns observed for other alkane mixtures containing cyclic molecules. Dispersive interactions are dominant and those between aromatic molecules with a given halogen atom become stronger when the size of this atom increases due to the corresponding increase of molecular polarizability. The mixtures were studied using the DISQUAC and Flory models. The latter was also applied to n-alkane solutions with C$_6$H$_5$F, or 1-methylnaphthalene. Both theories describe accurately the $H_{\text{m}}^{\text{E}}$ data. In terms of the Flory model, this means that the random mixing hypothesis is largely achieved. On the other hand, the theory overestimates the interactional contribution to $V_{\text{m}}^{\text{E}}$, particularly for systems with C$_6$H$_5$X (X = F, I).
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All authors Fernando Hevia, Luis Felipe Sanz, Juan Antonio González, Daniel Lozano-Martín, Susana Villa
Plasma Physics · Cross-listed from math.AP
Quasi-periodic traveling electron layers
Luca Franzoi and Emeric Roulley
We consider the one dimensional space-periodic Vlasov-Poisson equations and construct, close to symmetric flat velocity strips, small amplitude traveling quasi-periodic electron-layers, namely strip-shaped patches of electrons in the phase space. These solutions are found for most values of the strip area. The proof uses a Nash-Moser construction together with reducibility arguments based on pseudo-differential homogeneous expansions and KAM reductions. Thanks to a suitable linear transformation of the unknowns, we reveal a connection between the electron patch problem and the classical, physically relevant, electronic Euler-Poisson system with cubic pressure law. As a direct, non trivial consequence, we obtain small-amplitude quasi-periodic traveling waves for this model as well. To the best of our knowledge, this work provides the first rigorous example of construction of quasi-periodic solutions for kinetic models.
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Optics
Additive and Subtractive Color Filters Based on Birefringent Dielectric Metasurfaces
Ondřej Červinka, Filip Ligmajer, Ondřej Brunn et al. (+3 more)
Dielectric metasurfaces offer a path to high-efficiency optical components at the sub-wavelength scale. In this work, we utilize the wavelength-dependent birefringence of TiO2 nanopillars to create transmissive color filters with switchable spectral responses. We demonstrate that the same physical nanostructure array can function as either an additive (band-pass) or a subtractive (band-stop) filter solely by rotating an external polarizer. The metasurface was fabricated via a modified damascene process, enabling high-aspect-ratio structures with spatial resolutions down to 250 nm. The filters achieve transmission efficiencies of 70% tailorable across the entire visible spectrum, avoiding the ohmic losses typical of plasmonic alternatives. This approach provides a tunable, high-efficiency platform offering a versatile and compact solution for next-generation high-resolution imaging systems.
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All authors Ondřej Červinka, Filip Ligmajer, Ondřej Brunn, Miroslav Horáček, Stanislav Krátký, Tomáš Šikola
Physics and Society
The impact of behavioral homophily and conformity on epidemic spreading in networks with large groups
Olivier Ribordy, Clara Granell, Guillaume St-Onge et al. (+3 more)
Understanding how social behavior influences epidemic dynamics has become a central focus in mathematical epidemiology. In particular, behavioral homophily (the tendency of individuals to associate with similar others) and conformity (the adjustment of individual behavior to group norms) are key mechanisms in shaping transmission patterns. In this work, we investigate the combined impact of these behavioral processes on the susceptible-infected-susceptible (SIS) dynamics on networks with large, densely connected groups, modeled as cliques. Each individual has an intrinsic behavioral preference, but their expressed behavior within a group is modulated by its composition, reflecting conformity dynamics. Using the approximate master equations (AME) framework, we characterize the interplay between behavioral heterogeneity, group structure, and epidemic localization. Our results reveal that behavioral homophily amplifies the effects of conformity in large groups, enabling minority behaviors to persist as well as substantially shifting epidemic thresholds and spreading regimes.
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All authors Olivier Ribordy, Clara Granell, Guillaume St-Onge, Laurent Hébert-Dufresne, Alex Arenas, Antoine Allard
Computational Physics · Cross-listed from cond-mat.dis-nn
Cluster moves with an entropic reservoir accelerate low-temperature simulations of three-dimensional spin glasses
Claudio Chilin, Enzo Marinari, Víctor Martín-Mayor et al. (+3 more)
We present an
algorithm for the simulation of three-dimensional
spin glasses deep in the low-temperature phase: Parallel Tempering enhanced with Houdayer moves and with an entropic reservoir (PTHR). Although differences with the standard Houdayer
algorithm are small, PTHR allows us to equilibrate a large number of samples of $L=16$
lattices with Gaussian couplings for temperatures $T\geq 0.2$. We show that the computational complexity displays better size scaling than standard Parallel Tempering. For finite sizes, our method outperforms other cluster
algorithms by a speedup factor of around 64. In close analogy with standard Parallel Tempering, PTHR's computational complexity strongly relates to temperature chaos.
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All authors Claudio Chilin, Enzo Marinari, Víctor Martín-Mayor, Giorgio Parisi, Juan J. Ruiz-Lorenzo, David Yllanes
Classical Physics · Cross-listed from cs.LG
UNATE: UNsupervised ATomic Embedding for crystal structures property prediction
Laura Solà-Garcia, Àlex Solé and Javier Ruiz-Hidalgo
Accurately predicting
crystal properties is critical for accelerating materials discovery, but it is often limited by scarce labeled data and costly theoretical calculations. To alleviate this, we propose UNATE (Unsupervised Atomic
Embedding), a framework that leverages structural information extracted from unlabeled
crystal structures. UNATE integrates an unsupervised denoising autoencoder with self-supervised contrastive learning to learn robust atomic representations, which are then used as input features for downstream property prediction. Experimental results show that replacing raw atomic numbers with UNATE-pretrained node
embeddings yields a 2.7% improvement over the full-data baseline. Notably, the benefits become more pronounced in scenarios with limited labeled data, reaching improvements of up to 10% when only 25% of the labeled data is used.
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Computational Physics
Linac: linear algebra with CUDA over finite fields
Giuseppe De Laurentis and Jack Franklin
Solving linear systems of
polynomial equations is a ubiquitous problem in both mathematics and physics. The standard approach, Gaussian elimination, scales cubically with system size and often constitutes a computational bottleneck. The
algorithm's inherent parallelism makes it well-suited for modern computing architectures, namely graphics processing units (GPUs), which offer significantly higher throughput than CPUs. Additionally, the use of finite fields – integers modulo a
prime – in place of floating-point arithmetic offers a scalable solution to the issue of numerical precision loss, which becomes increasingly problematic at large system sizes. With Linac, we present a high-performance, open-source, parallel implementation of Gaussian elimination over finite fields and floating-point arithmetic. This tool has been developed for applications to analytic reconstruction of scattering amplitudes in
quantum field theory.
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Optics · Cross-listed from quant-ph
Photon position eigenstates in configuration space
Artemio González-López and Luis Martínez Alonso
The expressions of the eigenfunctions of the Hawton
photon position operator in configuration space are derived for several classes of wave function, including the Riemann–Silberstein and Landau–Peierls cases. Although these eigenfunctions have a simple form in momentum space, the explicit characterization of their representations in configuration space is rather more involved. We provide closed expressions of these eigenfunctions in terms of linear combinations of the complete
elliptic integrals $K(\kappa)$ and $E(\kappa)$ with modulus $\kappa$ depending on trigonometric functions of the polar angle. We show that they diverge not only at the value $\mathbf q$ of the position
eigenvalue, but also on a plane containing $\mathbf q$ and that they decay as inverse powers of the distance from $\mathbf q$.
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Optics · Cross-listed from quant-ph
Sideband fingerprints of antibunched light in cascaded quantum wave mixing
R. D. Ivanovskikh, W. V. Pogosov, A. A. Elistratov et al. (+5 more)
Quantum wave mixing on a single superconducting
qubit produces a hierarchy of coherent side peaks associated with elastic multiphoton scattering pathways. In a cascaded source–probe geometry these pathways become sensitive to the
photon statistics of the radiation emitted by the source
qubit. We develop an analytical theory of this effect starting from the cascaded master equation in the weak-driving regime. In the coherent-filtering limit when the decay rate of the probe
qubit is less than that of the source, the standard coherent–coherent wave-mixing hierarchy is recovered. In the opposite limit, side peaks associated with multiphoton absorption from the antibunched source field are parametrically suppressed. Numerical solutions confirm the analytical scaling laws. The resulting sideband hierarchy provides a frequency-domain fingerprint of antibunched itinerant microwave light.
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All authors R. D. Ivanovskikh, W. V. Pogosov, A. A. Elistratov, A. Yu. Dmitriev, T. R. Sabirov, A. V. Vasenin, S. A. Gunin, O. V. Astafiev
Optics
All-optical Edge Computing for Speckle Sensing Interrogation
Tomás Lopes, Joana M. Teixeira, Tiago D. Ferreira et al. (+3 more)
Speckle-based sensing exploits the rich environmental information of its high-dimensional spatial intensity patterns. However, the requirement for camera-based acquisition and subsequent electronic digitization introduces significant latency and bandwidth bottlenecks that forbid real-time operation and higher temporal resolutions. Aiming to bypass this imaging processing pipeline, this manuscript presents an optically reconfigurable edge-computing platform for speckle-based sensors that performs task-specific computation directly in the optical domain. This is achieved by projecting output speckle patterns onto a digital micromirror device, using it as a programmable optical layer whose parameters are trained in situ using an evolutionary optimization strategy solely from detector feedback. We demonstrate the concept with a multi-point optical fiber sensing task, where multiple piezoelectric actuators simultaneously perturb the fiber, modifying the speckle pattern. Optimizing a set of masks to decouple these concurrent signals, the system successfully achieves real-time signal separation, achieving a target signal enhancement exceeding 4 dB while suppressing crosstalk leakage below -10 dB. Operating with bandwidths limited only by the photodetector, this approach paves the way for real-time and ultrafast optical sensing via an all-optical edge computing solution.
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All authors Tomás Lopes, Joana M. Teixeira, Tiago D. Ferreira, Catarina S. Monteiro, Pedro A. S. Jorge, Nuno A. Silva
Atomic and Molecular Clusters
Emerging Amines reshape the paradigm of urban atmospheric particle formation
Yongjian Lian, Xurong Bai, Ruoying Yuan et al. (+4 more)
New particle formation (NPF) contributes to more than half of global aerosol number concentrations, with profound implications for human health and
climate change. Observational studies have shown that the frequency of NPF events in urban Beijing during summer exceeds the global average. The prevailing paradigm attributes urban NPF primarily to sulfuric acid-base nucleation involving dimethylamine (DMA). However, recent field measurements in summer urban Beijing have identified several emerging amines emitted from carbon capture processes, including monoethanolamine (MEA), piperazine (PZ), diethanolamine (DEA) and N-methyldiethanolamine (MDEA), in addition to DMA. Here, we systematically evaluate the contributions of sulfuric acid-amine nucleation pathways to urban NPF. We found that emerging amines particularly DEA and PZ, can dominate nucleation pathways under polluted urban conditions, surpassing the contribution of DMA. These findings suggest that the current universal paradigm of urban nucleation should be revisited to explicitly account for the role of emerging amines. Moreover, emerging amine-mediated NPF will become increasingly important in the context of future co-control policies for air pollution and carbon reduction.
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All authors Yongjian Lian, Xurong Bai, Ruoying Yuan, Wenli Xu, Hongjun Mao, Jianfei Peng, Shuai Jiang
Chemical Physics
Mutual information and mutual correlation: their spin-free formulations and comparison
Jiri Pittner
Orbital
entropies, pair
entropies, and mutual information have become popular tools for analysis of strongly correlated wave functions. They can quantitatively measure how strongly an orbital participates in the electron correlation and reveal the correlation pattern between different orbitals. However, this pattern can become rather complicated and sometimes difficult to interpret for large active spaces and is not invariant with respect to the
spin projection ($M_s$) component of the
spin multiplet state. We introduce a modified
spin-free orbital
entropy, pair
entropy, and mutual information, which simplify the correlation analysis and are invariant with respect to $M_s$. By comparison of these quantities with their "original"
spin-including counterparts one can distinguish static correlation due to
spin couplings from the "genuine" strong correlation due to a multiconfigurational character of the wave function. Recently, Evangelista introduced mutual correlation computed from the two-body cumulant as an alternative correlation measure, which is easier to compute for some methods. We present here a
spin-free analogue of this quantity, which is $M_s$ invariant, and perform a comparison of the
entropy-based and cumulant-based correlation measures in both
spin-free and original variants. We illustrate the approaches on iron-sulfur bound complexes with one and two iron atoms.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Alignment-free ultra-broadband parametric frequency conversion in lead-halide perovskites
Abhishek Shiva Kumar, Dusan Lorenc, Ayan A. Zhumekenov et al. (+2 more)
Lead-halide perovskites were demonstrated to exhibit some of the largest known optical nonlinearities, yet their potential for frequency conversion remains largely untapped. Here we demonstrate ultra-broadband four-wave mixing of near- and mid-infrared femtosecond pulses in thick single-
crystal LHPs, generating bright, coherent, and highly collimated emission across an exceptionally wide continuous tuning range without phase-matching engineering, angular
alignment, or dispersion optimization. Time resolved measurements reveal that the emission originates near the
crystal surfaces, where phase-matching constraints are relaxed, while the unusually large intrinsic $\chi^{(3)}$ response preserves efficient and directional frequency conversion despite the strongly localized interaction volume. These results position LHPs as a powerful bulk platform for ultra-broadband nonlinear photonics, opening a pathway toward compact,
alignment-free architectures for ultrafast frequency conversion.
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All authors Abhishek Shiva Kumar, Dusan Lorenc, Ayan A. Zhumekenov, Osman M. Bakr, Zhanybek Alpichshev
Optics
Hybrid-plasticity Photonic Synapses Enabling Hardware-Level Neural Reuse
Chenlei Li, Tao Shu, Cunyu Shi et al. (+12 more)
Biological intelligence is distinguished by neural reuse, the capacity to preserve established learning
memory while repurposing it for new tasks and dynamic environments. Bringing this capability to photonic hardware requires hybrid plasticity, namely the coexistence of long-term synaptic plasticity for persistent weight storage and short-term synaptic plasticity for rapid, reversible adaptation within a single synaptic element; however, current photonic architectures lack such a unified mechanism. Here, we demonstrate a hybrid-plasticity photonic synapse on thin-film lead zirconate titanate (PZT) that couples non-volatile and volatile modes to enable hardware-level neural reuse. Crucially, high-speed refresh operations can be superimposed without perturbing the stored weight. Such a neural-reuse framework yields a convergence speedup of over 20-fold and reduces the weight updates by approximately 30-fold compared with random initialization. These results establish hybrid-plasticity photonic synapses as a pathway toward on-chip learning systems that are both
memory-preserving and rapidly adaptable.
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All authors Chenlei Li, Tao Shu, Cunyu Shi, Wei Wang, Shengjie Tang, Yueyang Zhang, Wei Chen, Jungan Wang, Bin Li, Yu Han, Gong Zhang, Huan Li Yaocheng Shi, Jianwei Wang, Feng Qiu, Daoxin Dai
Chemical Physics
Machine Learning Multiscale Interactions
Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro et al. (+4 more)
Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on a narrow range of interactions. While machine learning force fields (MLFFs) offer near-
quantum accuracy, the ubiquitous message-passing layers miss long-range many-body effects. Here we introduce the Multiscale Structural Ensemble (MuSE), a hierarchical model that uses Soft Coarse-Graining Pooling to construct coarse representations from smooth fractional assignments of atoms to coarse nodes, enabling MLFF modules to operate across multiple scales. MuSE is architecture-agnostic and coupled with SO3krates, MACE, and PaiNN MLFFs for both molecules and materials. We demonstrate the power of MuSE through Hessian-based
benchmarks, folding trajectories for biomolecules, and energy profiles in molecule-
graphene nanostructures, where MuSE accurately captures
quantum-mechanical interactions at relevant scales – unlike other recent long-range ML models.
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All authors Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro, Silvia Gómez-Coca, Eliseo Ruiz, Alexandre Tkatchenko, Javier Ruiz-Hidalgo
Chemical Physics
Terahertz-induced local-field dynamics and transient birefringence in aqueous electrolytes
Naveen Kumar Kaliannan, Thomas D. Kühne and Hossam Elgabarty
Terahertz Kerr-effect (TKE) spectroscopy provides a time-domain optical probe of the intermolecular structural dynamics of
liquids, but the measured birefringence can only be interpreted microscopically if transient molecular structure, local electric fields, and nonlinear optical response are treated on the same footing. We combine terahertz-driven non-
equilibrium molecular dynamics with a hyperpolarizability-enhanced dipole-induced-dipole (DID) response model to connect the measured TKE signal of water and aqueous electrolytes to molecular-scale hydration dynamics. Starting from a multipolar expansion of the field-dependent molecular energy, the optical response is written as a differential polarizability in which first and second molecular hyperpolarizabilities are coupled to the instantaneous local field generated by the evolving
liquid structure. For neat water, these local-field terms convert an insufficiently structured intramolecular response into the experimentally observed bipolar TKE line shape. For aqueous MgCl2, polarizable force-field trajectories combined with the extended optical response reproduce the concentration-dependent enhancement of the negative birefringence, whereas non-polarizable trajectories yield the wrong trend. Species-resolved analysis assigns the high-concentration response to strongly field-polarized anions and, more importantly, to water molecules bridging cations and anions. The work identifies TKE birefringence as a sensitive readout of ion-hydration local-field dynamics and shows that induced polarization in both the trajectory and the optical response is required to recover the structural dynamics encoded in electrolyte TKE measurements.
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Plasma Physics
Pulse magnet of 10 T for power laser experiments with x-ray free-electron laser diagnostics
Akihiko Ikeda, Kosuke Noda, Yutaro Yamanaka et al. (+22 more)
The importance of investigating magnetized
plasmas/solids in extreme conditions has grown over the last decades, particularly in the field of high energy density physics (HEDP), such as laboratory astrophysics and inertial confinement fusion. However, up to now, the unique capabilities of an X-ray free-electron
laser (XFEL), such as high brilliance and low divergence have never been exploited for this type of research. In this paper, we present the first platform developed at SACLA, Japan, that combines a high-power optical
laser for generating matter under extreme conditions of pressure and temperature, an XFEL probe, and an external
magnetic field. The high current is produced using a 2 kV, 4.8 kJ pulsed power system giving a maximum current of 10 kA which is synchronized with the optical
laser and XFEL in a
vacuum environment. It flows through a split-pair coil to generate a high
magnetic field (10 T at 6 kA) which has 1 cm access every 45$^{\circ}$ in the equatorial plane and 90$^{\circ}$ in the poloidal one. This platform offers new opportunities to study high-energy-density matter in strong
magnetic fields, including shock propagation, instability growth, and turbulent
plasma dynamics.
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All authors Akihiko Ikeda, Kosuke Noda, Yutaro Yamanaka, Yuma Urabe, Keiichiro Kawai, Yasuhiro H. Matsuda, Hirotaka Nakamura, Ryusuke Yamamoto, Yoshiki Naito, Yasuhiro Kuramitsu, Kai Taketoshi, Naoki Yamagata, Norimasa Ozaki, Tatiana Pikuz, Yoichi Sakawa, Takayoshi Sano, Ryosuke Kodama, Taichi Morita, Tomoya Ogawa, Kohei Miyanishi, Toshinori Yabuuchi, Rigon Gabriel, Bakandreas Stavros, Koenig Michel, Bruno Albertazzi
Fluid Dynamics
Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain
Yujia Zhang, Jiaxi Qi, Ruiyan Chen et al. (+5 more)
Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional computational fluid dynamics (CFD) simulations face two fundamental bottlenecks: expert-intensive mesh generation around irregular topography, and iterative solvers that require hours to days even on high-performance clusters. Recent neural operator approaches accelerate inference, but typically fail to resolve the sharp, localized velocity gradients induced by complex terrain features. Here, we present a
transformer-based dual-
attention neural-operator framework for 3D wind field prediction over complex mountainous terrain, and validate its effectiveness through two instantiations on representative point-based (mesh-free) and graph-based neural-operator architectures, namely Patch-solver and Patch-GTO. Trained on a large CFD-generated
dataset spanning diverse terrain geometries and inflow conditions, the framework enables rapid prediction of steady-state wind field while maintaining competitive accuracy. It also demonstrates robust zero-shot transfer to real-world mountainous sites across several diverse locations, outperforming existing neural operator baselines by 10% in relative error. We further verify that incorporating sparse observational data (1% spatial coverage) reduces prediction error by 16.89% relative to the corresponding model without sparse data input and by 32.75% relative to advanced neural operator baselines on unseen terrains. This framework establishes a generalizable computational paradigm across domains, promising to be a real-time tool for wind resource assessment over complex mountainous terrain and related atmosphere-surface interaction studies.
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All authors Yujia Zhang, Jiaxi Qi, Ruiyan Chen, Yong Liu, Yuzhou Zhang, Lyulin Kuang, Rita Zhang, Shengze Cai