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Physics Physics

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Sunday 25 January

Optics

Partitionable Diffractive Neural Networks for Multifunctional Optical Operations

Yudong Tian, Haifeng Xu, Yuqing Liu et al. (+3 more)

Diffractive neural network (DNN), which can perform machine learning tasks based on the light propagation and diffraction, has recently emerged as a promising optical computing paradigm due to its high parallel processing speed and low power consumption nature. However, existing diffractive network architectures face challenges in implementing functional reconfiguration. Once a diffractive neural network is fabricated, its functionality is fixed. Deploying such systems for different tasks typically requires reconstructing the entire physical setup, which significantly compromises hardware efficiency in practical applications. In this work, we propose the multifunctional partitionable diffractive neural networks (PDNNs) that can generate networks with additional capabilities by stacking multiple sub-modules with independent functions in the horizontal direction. Each submodule functions as an independent diffractive network capable of performing specific imaging or classification tasks. When these submodules are combined, they can form a new network with additional functionalities. Moreover, assembling these submodules in different configurations enables structures with diverse functions. This powerful PDNN framework demonstrates remarkable advantages in flexibility and reconfigurability for multitask operations, opening a new pathway for realizing multifunctional and integrated optical artificial intelligence systems.
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All authors Yudong Tian, Haifeng Xu, Yuqing Liu, Xiangyu Zhao, Jierong Cheng, Chongzhao Wu

Fluid Dynamics

Cavitation Dynamics in Venturi-Type Microchannels: Experimental Observations and Numerical Modeling

Md Naim Hossain, Anish Pal, Sayan Karmakar et al. (+4 more)

Cavitation in fluids can severely hinder the efficiency of the associated flows. This undesired phenomenon is strongly influenced by local flow conditions, flow orientation, proximity to boundaries and liquid/gas properties at saturation. When liquid flow is severely constricted and cavitation occurs, diverging microchannels can suppress vapor formation through effective pressure recovery; however, the behavior of such microchannels under different geometric and operating conditions remains unclear. This work combines experimental data and computational modeling to elucidate the intricate flow dynamics of cavitation-induced phase change of refrigerant (R134a) flow in a Venturi-type microchannel. Full-field numerical simulations are carried out using the interPhaseChangeFoam solver in OpenFOAM, with the model coefficients being validated against experimental data obtained with an identical system. The effects of flow rate, channel opening angle, and inlet cross-sectional geometry are explored with a focus on pressure drop, velocity distribution, and cavitation characteristics, including vapor fraction. The results highlight the roles of flow velocity, orifice-to-channel width ratio, orifice size, and channel divergence angle on overall pressure drop. A channel opening angle of 12 degrees minimizes pressure drop, in contrast with conventional single-phase Venturi geometries where the optimal opening angle typically lies between 5 and 7 degrees. The study underscores that the cavitation number, including its sign, serves as a critical quantitative indicator of cavitation severity where increasingly negative values correspond to intensified vapor generation in microscale constrictions. By identifying the relationships between operating conditions & cavitation phenomena, this work provides a framework for understanding and optimizing microchannel designs under conditions prone to cavitation.
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All authors Md Naim Hossain, Anish Pal, Sayan Karmakar, Alexander D. Snyder, Michael T. Barako, Clayton A. Pullins, Constantine M. Megaridis

Chemical Physics

cuGUGA: Operator-Direct Graphical Unitary Group Approach Accelerated with CUDA

Zihan Pengmei

We present cuGUGA, an operator-direct graphical unitary group approach (GUGA) configuration interaction (CI) solver in a spin-adapted configuration state function (CSF) basis. Dynamic-programming walk counts provide constant-time CSF ranking/unranking, and pretabulated segment factors enable constant-time evaluation of coupling coefficients. Two-electron contributions are organized through an intermediate-weight formulation that separates sparse generator enumeration from integral contraction and supports both dense and density-fitted/Cholesky backends. We further map the same primitives to GPUs by implementing the irregular DRT traversal and accumulation in custom CUDA kernels while delegating contractions to CUDA libraries. The implementation reproduces reference energies at the 10^{-11} Eh level and matches CPU/GPU sigma-vectors to 10^{-14}. On an RTX 4090, the GPU backend provides up to ~10x speedup over the CPU backend for smaller active spaces and multifold speedups on representative CASCI kernels. Speedup decreases as the active space grows because the workload becomes increasingly dominated by FP64 GEMM, which is not strongly accelerated on consumer GPUs. In addition, the cuGUGA CPU backend generally delivers >2x speedup over PySCF's determinant backend and >4x speedup over PySCF CSF backend.
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Data Analysis and Statistics · Cross-listed from quant-ph

Quantum-Inspired Algorithms beyond Unitary Circuits: the Laplace Transform

Noufal Jaseem, Sergi Ramos-Calderer, Gauthameshwar S. et al. (+3 more)

Quantum-inspired algorithms can deliver substantial speedups over classical state-of-the-art methods by executing quantum algorithms with tensor networks on conventional hardware. Unlike circuit models restricted to unitary gates, tensor networks naturally accommodate non-unitary maps. This flexibility lets us design quantum-inspired methods that start from a quantum algorithmic structure, yet go beyond unitarity to achieve speedups. Here we introduce a tensor-network approach to compute the discrete Laplace transform, a non-unitary, aperiodic transform (in contrast to the Fourier transform). We encode a length-$N$ signal on two paired $n$-qubit registers and decompose the overall map into a non-unitary exponential Damping Transform followed by a Quantum Fourier Transform, both compressed in a single matrix-product operator. This decomposition admits strong MPO compression to low bond dimension resulting in significant acceleration. We demonstrate simulations up to $N=2^{30}$ input data points, with up to $2^{60}$ output data points, and quantify how bond dimension controls runtime and accuracy, including precise and efficient pole identification.
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All authors Noufal Jaseem, Sergi Ramos-Calderer, Gauthameshwar S., Dingzu Wang, José Ignacio Latorre, Dario Poletti

Optics

Topological antilaser

Rui-Chang Shen, Chunquan Peng, Bingbing Wang et al. (+6 more)

Coherent perfect absorption (CPA)-the time-reversed operation of lasing at threshold-relies on finely tuned interference and is intrinsically fragile to disorder and structural imperfections. Whether absorption can be endowed with topological protection, by analogy to topological lasing, has remained an open question. Here, we experimentally demonstrate a topological antilaser: the time-reversed counterpart of a topological laser, in which chiral edge modes of a photonic lattice enable perfect light absorption protected by topology. Using a nonreciprocal microwave network with low intrinsic loss, we show that the topological antilaser preserves near-unity absorption under strong disorder, and, unlike conventional antilasers, remains functional for arbitrary placements of dissipation and input ports, even when the lattice is strongly perturbed. This robustness arises from the disorder-immune propagation and stable spatial profile of the topological edge modes. Our results establish topologically protected absorption as the missing counterpart of topological lasing, opening new directions for studying robust energy dissipation, wave control, and coherent-absorption-based detection technologies.
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All authors Rui-Chang Shen, Chunquan Peng, Bingbing Wang, Wentao Xie, Siyuan Zhang, Peiheng Zhou, Baile Zhang, Y. D. Chong, Haoran Xue

Physics and Society

Synchronization in Traffic Dynamics: Mechanisms of Hysteresis

Jinghui Wang and Wei Lv

Starting from a second-order linear differential equation, we analyze the dynamical mechanisms of no behavior pattern (pure response), reaction and anticipation behaviors in traffic. As an emergence of the underlying dynamical evolution, the periodic evolution trajectories (3D hysteresis) in phase space ($v_i, v_j, d_{ji}$) exhibit fascinating characters. We investigate the emerging Time-Delay ($TD$) phenomena and the resulting analytical hysteresis, an equal frequency sets of Lissajous figures. By quantifying energy dissipation through individual and system perspectives, we demonstrate that $TD$ and Time-To-Collision ($TTC$) are direct metrics of zero-dissipation under equilibrium and synchronization states. Finally, a phase diagram based on $TD$ and $TTC$ is developed to bridge the dynamical behaviors in traffic across $\mathbb{R}^1$ and $\mathbb{R}^2$ spaces. Our results provide a theoretical foundation upon which many obscure mechanisms become self-evident, such as the $TD$-induced flip of the hysteresis (clockwise to counterclockwise in FD) and the crossed hysteresis, etc.
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Optics

Metasurface-assisted balanced-injection synchronization for turbulence-resilient long-haul chaotic free-space link

Yiqun Zhang, Mingfeng Xu, Ning Jiang et al. (+12 more)

Optical chaotic synchronization between coupled nonlinear lasers underpins most chaos-based applications, including complex laser network dynamics, secure communication, key distribution, and reinforcement learning. In free-space links, however, chaotic synchronization is highly vulnerable to stochastic fluctuation induced by atmospheric turbulence, which results in temporal injection imbalances at symmetric receivers and triggers intermittent desynchronization. Here, we introduce a full Poincaré vector beam-enabled balanced-injection synchronization (BIS) mechanism, which passively mitigates coupling fluctuations and preserves injection symmetry through a complementary metasurface pair, without requiring any channel estimation or active control. Over a 3.2 km urban link under moderately strong turbulence, BIS suppresses coupling power fluctuations by a factor of 4.6 (from 0.4511 to 0.0975). It eliminates desynchronization events and increases the high-quality synchronization probability from 58.6% to 91.0%. This enables a record-high bit rate-distance product of 720 Gbps \cdot km, reducing communication interruption probability by up to 77% compared to Gaussian beam transmission. Our innovative strategy bridges the gap between nanophotonics and engineering optics, offering a new insight into advancing next-generation LiDAR, secure communication, and integrated sensing and communication systems in turbulent environments.
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All authors Yiqun Zhang, Mingfeng Xu, Ning Jiang, Mengjie Zhou, Yuhan Zheng, Sichao Chen, Jiazheng Ding, Shuangcheng Chen, Yong Yu, Xianglei Yan, Fei Zhang, Yinghui Guo, Mingbo Pu, Kun Qiu, Xiangang Luo

Optics

Residual neural-field ptychography for dose-efficient electron, X-ray, and optical nanoscopy

Qianhao Zhao, Zhixuan Hong, Ruihai Wang et al. (+7 more)

Ptychography spans from sub-angstrom to meter scales yet suffers from convergence instability and excessive data redundancy. Here we introduce self-correcting residual neural fields as a dose-efficient framework for electron, X-ray, and optical ptychography. Unlike approaches that split complex fields, our complex-valued architecture employs holomorphic phasor activation e^i{\omega}z to preserve intrinsic phase-amplitude coupling. We reformulate reconstruction as residual learning, where the network learns only corrections to physical priors rather than complete wavefields. By embedding the physical model as a differentiable layer within the network, we enable end-to-end automatic differentiation where experimental parameters are jointly corrected alongside the neural fields. We validate our scheme across conventional, near-field, coded, and Fourier ptychography and achieve record-breaking lensless resolution of 244-nm linewidth with visible light. Extending to electron wavelengths, we reveal synaptic connectivity in brain sections with superior performance over conventional approaches. Our framework provides a solution for high-throughput, dose-efficient nanoscopy across the electromagnetic spectrum.
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All authors Qianhao Zhao, Zhixuan Hong, Ruihai Wang, Tianbo Wang, Lingzhi Jiang, Qiong Ma, Peng-Han Lu, Rafal E. Dunin-Borkowski, Andrew Maiden, Guoan Zheng

Fluid Dynamics

DDFKs: Fluid Simulation with Dynamic Divergence-Free Kernels

Jingrui Xing, Yizao Tang, Mengyu Chu et al. (+1 more)

Fluid simulations based on memory-efficient spatial representations like implicit neural spatial representations (INSRs) and Gaussian spatial representation (GSR), where the velocity fields are parameterized by neural networks or weighted Gaussian functions, has been an emerging research area. Though advantages over traditional discretizations like spatial adaptivity and continuous differentiability of these spatial representations are leveraged by fluid solvers, solving the time-dependent PDEs that governs the fluid dynamics remain challenging, especially in incompressible fluids where the divergence-free constraint is enforced. In this paper, we propose a grid-free solver Dynamic Divergence-Free Kernels (DDFKs) for incompressible flows based on divergence-free kernels (DFKs). Each DFK is incorporated with a matrix-valued radial basis function and a vector-valued weight, yielding a divergence-free vector field. We model the continuous flow velocity as the sum of multiple DFKs, thus enforcing incompressibility while being able to preserve different level of details. Quantitative and qualitative results show that our method achieves comparable accuracy, robustness, ability to preserve vortices, time and memory efficiency and generality across diverse phenomena to state-of-the-art methods using memory-efficient spatial representations, while excels at maintaining incompressibility. Though our first-order solver are slower than fluid solvers with traditional discretizations, our approach exhibits significantly lower numerical dissipation due to reduced discretization error. We demonstrate our method on diverse incompressible flow examples with rich vortices and various solid boundary conditions.
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All authors Jingrui Xing, Yizao Tang, Mengyu Chu, Baoquan Chen

Fluid Dynamics

Flocking and mesoscale turbulence in three-dimensional active fluids

Prasad Perlekar

We numerically study the three-dimensional turbulence in a minimal model of an active fluid–the Toner-Tu-Swift-Hohenburg equation. For small activity, we observe bacterial turbulence, while for large activity, we uncover hitherto unexplored regime of a turbulent flock where a global order coexists with turbulence. We present a simple closure model that predicts the turbulent flock and also qualitatively explains the transition to the bacterial turbulence regime via a transcritical bifurcation.
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Optics

Dual Flat-Bands of Bound State in the Continuum and Radiative Mode via TE-TM Coupling

Jiayao Liu, Zimeng Zeng, Zhuoyang Li et al. (+2 more)

A general symmetry-controlled mechanism is proposed for realizing dual flat-bands of bound state in the continuum (BIC) and its radiative counterpart in photonic crystal slabs. By breaking the vertical mirror symmetry of slab, inter-polarization coupling between TE-like and TM-like modes is activated, while intra-polarization coupling among modes within the same polarization class is simultaneously preserved. The cooperative action of these two coupling channels gives rise to the concurrent flattening of both the BIC-hosting band and the radiative band, resulting in a dual flat-band system with strongly contrasting quality (Q) factors. An effective two-step coupling model is constructed to capture the essential physics and show that the emergence of the flat bands is governed by geometric tuning rather than accidental degeneracies. The mechanism is shown to be generic with respect to polarization and material platform, enabling dual flat-band states in both low- and high-index systems, with substantially enhanced angular bandwidths in the latter. These finding establish a unified route for flat-band photonic engineering and provide a robust platform for angle-tolerant resonant photonic functionalities.
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All authors Jiayao Liu, Zimeng Zeng, Zhuoyang Li, Zelong He, Zhaona Wang

Atmospheric and Oceanic Physics

HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts

Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard et al. (+6 more)

AI weather models now rival leading numerical weather prediction (NWP) systems in medium-range skill. However, almost all still rely on NWP data assimilation (DA) to provide initial conditions, tying them to expensive infrastructure and limiting the practical speed and accuracy gains of ML. More recently, ML-based DA systems have been proposed, which are often trained and evaluated end-to-end with a forecast model, making it difficult to assess the quality of their analysis fields. We introduce HealDA, a global ML-based DA system that maps a short window of satellite and conventional observations directly to a 1{\deg} atmospheric state on the HEALPix grid, using a smaller sensor suite than operational NWP. We treat HealDA strictly as a DA module: its analyses are used to initialize off-the-shelf ML forecast models without any fine-tuning of either. For a variety of off-the-shelf ML forecast models, including FourCastNet3 (FCN3), Aurora, and FengWu, HealDA-initialized forecasts lose less than one day of effective lead time when scored against ERA5. HealDA-initialized FCN3 ensembles similarly trail those of the ECMWF IFS ENS system by < 24 h. We find that forecast error growth in these models is unchanged from HealDA initialization, and the skill gap primarily arises from the larger initial error of the HealDA analysis. Spectral analysis reveals that this stems from overfitting to the large scales and upper-tropospheric fields. We also demonstrate that small changes in the verification setup can shift apparent skill by 12–24h, underscoring the need for consistent scoring. Taken together, these results clarify the current performance of ML-based DA systems and show that a relatively simple, direct observation-to-state network can already provide initial conditions that are usable by state-of-the-art ML forecast models with only modest loss in medium-range skill.
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All authors Aayush Gupta, Akshay Subramaniam, Michael S. Pritchard, Karthik Kashinath, Sergey Frolov, Kelsey Lieberman, Christopher Miller, Nicholas Silverman, Noah D. Brenowitz

Saturday 24 January

General Physics

Dynamical Oscillations in Dark Energy: Joint Constraints on the $w_{sin}$CDM Model from DESI, OHD, and Supernova Samples

Manish Yadav, Archana Dixit, M. S. Barak et al. (+1 more)

In this study, we investigate the oscillatory dark energy model $w_{\sin}\mathrm{CDM}$ based on the DESI BAO data together with OHD, Pantheon Plus, and SH0ES measurements. We examine how the DESI data influence the dark energy equation-of-state plane $(w_0, w_a)$ within cosmological models that are free from Hubble tension and employ a Monte Carlo Markov Chain (MCMC) approach. Our findings indicate that although the parameter space still favors $w_a < 0$ and $w_0 > -1$ , the cosmological constant remains consistent with the DESI+OHD+PP combination at the $2\sigma$ level. We also observe that the best-fit Hubble constant $H_0$ is higher for the DESI+OHD+PP+SH0ES data combination, leading to a residual Hubble tension of less than $1\sigma$ to remain consistent with the SH0ES measurement. These results suggest that attempts to address the Hubble tension tend to reduce indication of DESI for the oscillatory dark energy model. Therefore, claims that the cosmological constant should be approached with greater caution, considering both the latest observational datasets and the existing cosmological tensions. We also obtained the present deceleration parameter and the effective equation-of-state value as $q_0 = -0.36$ and $w_{\mathrm{eff}} = -0.57$, respectively, for the DESI+OHD+PP+SH0ES dataset combination. Further analysis indicated a strong departure of $w_0$ from $w=-1$ at the $4\sigma$ level for the DR2+OHD+DES-5yr data combination. The inferred $\Omega_{m}$ tended to shift toward higher values when supernova samples were included, indicating a systematic preference for larger $\Omega_{m}$ in combinations involving supernova data.
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All authors Manish Yadav, Archana Dixit, M. S. Barak, Anirudh Pradhan

Chemical Physics

Universal rapid machine learning models for predicting unconvoluted and convoluted X-ray Absorption Spectra

Fei Zhan and Zhi Geng

X-ray absorption near edge structure (XANES) is an essential tool for elucidating the atomic-scale, local three-dimensional (3D) structure of given materials and molecules. The rapid computation of XANES based on molecular 3D structures constitutes a vital element of quantitative XANES analysis. Here, we present an XANES prediction model. It takes 3D structures as input and generates either unconvoluted XANES or convoluted spectra as output, demonstrating excellent generalizability across diverse instrumental broadening. This model has validated its predictive capability for both hard X-ray XAS (exemplified by K-edges of 3d 4d metals and lanthanides) and soft X-ray XAS (using S K-edge as examples). Adopting the model, XANES spectra of multiple elements can be predicted using a single unified model. A highly efficient 3D structure fitting algorithm based on this unconvoluted XANES prediction model, aiming to serve as an online data analysis method suitable for XAS beamlines.
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Physics and Society

Reconsidering Global Comparisons of Scientist Trustworthiness

Yuke Yang and Zhihao Ma

In a reanalysis of perceived scientist trustworthiness across 68 countries/regions, measurement-aligned indicated limitations in the cross-national comparability of the original measurement structure. Some country/region rankings and predictor associations varied across alternative scoring approaches, suggesting the value of considering measurement invariance when interpreting cross-national findings. Future research could use diverse methods to strengthen cross-cultural measurement validity.
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Fluid Dynamics

Gravity Wave Interactions in the Stratocumulus-Topped Boundary Layer

Arun Balakrishna, Hao Fu, Parviz Moin et al. (+1 more)

This work studies the breakup propensity of the stratocumulus-topped boundary layer (STBL) interacting with gravity waves using large-eddy simulation with a uniform vertical grid of $5$ m and horizontal spacing of $30$ m. A radiative-convective equilibrium (RCE) state is constructed to enforce stationarity in the STBL, and the gravity waves are introduced via a vertical momentum forcing mimicking a packet of plane waves. A nondimensionalization involving the inversion height and mean horizontal base wind as length and velocity scales is proposed to provide a framework to analyze the forcing parameter space. The magnitude of the scaled forcing amplitude ($\mathcal{A}$) is critical in understanding various STBL breakup conditions. Classification of breakup was based on the reduction of the liquid water path for each forced STBL case. We found that breakup did not occur for $\mathcal{A}<1$ and observed modest reductions in cloud for $1<\mathcal{A}<2$, but the deck recovered to the stationary state slowly after the single-period forcing ceased. Fixing $\mathcal{A}\sim 2$ showed that forcings with longer duration and wider locality promote breakup. However, when the forcing is a linear combination of waves of two different periods, the percentage of cleared cloud dramatically increases, though recovery of RCE is still observed in some cases. $\mathcal{A}\geq2.5$ marks a critical threshold by which the STBL breaks up entirely and remains patchy. We further explore the connection between these bulk breakup results and the turbulent state by examining energy budgets and the anisotropy induced by the forcing.
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All authors Arun Balakrishna, Hao Fu, Parviz Moin, Morgan O'Neill

Data Analysis and Statistics · Cross-listed from stat.ME

Non-parametric finite-sample credible intervals with one-dimensional priors: a middle ground between Bayesian and frequentist intervals

Tim Ritmeester

We present a method of constructing statistical intervals that obtain a natural middle ground between Bayesian and frequentist statistical intervals, previously unexplored in literature: To a p% Bayesian credible interval we should assign a p% belief after observing both the dataset and the interval, to p% frequentist intervals we can generally only assign a p% belief before observing either the data or the interval, while to the intervals proposed here we can assign a p% belief after observing the interval, but not necessarily after inspecting the full dataset ourselves. Even in fully non-parametric problems this only requires a prior over the parameter(s) of interest, not a high-dimensional prior over the full distribution, while maintaining many of the practical and philosophical advantages of Bayesian methods. We belief these methods may therefore provide significant advances in statistical methodology to a number of fields. This work is meant as a proof of principle: We concretely implement such intervals for two different problems and study the properties of resulting intervals. We discuss promising directions where the proposed type of interval may provide significant advantages.
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Atomic Physics · Cross-listed from quant-ph

Holstein Primakoff spin codes for local and collective noise

Sivaprasad Omanakuttan, Tyler Thurtell, Andrew K. Forbes et al. (+2 more)

Quantum error correction is essential for fault-tolerant quantum computation, yet most existing codes rely on local control and stabilizer measurements that are difficult to implement in systems dominated by collective interactions. Inspired by spin-GKP codes in PhysRevA.108.022428, we develop a general framework for Holstein-Primakoff spin codes, which maps continuous-variable bosonic codes onto permutation-symmetric spin ensembles via the Holstein-Primakoff approximation. We show that HP codes are robust to both collective and local-spin noise and propose an explicit measurement-free local error recovery procedure to map local noise into correctable collective-spin errors.
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All authors Sivaprasad Omanakuttan, Tyler Thurtell, Andrew K. Forbes, Vikas Buchemmavari, Ben Q. Baragiola

Instrumentation and Detectors

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector

CMS Collaboration

The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023$-$2024) conditions, the jet energy resolution improves by 10$-$20% for jets with transverse momentum between 30$-$100 GeV. Inference time is evaluated using simulated multijet events, with a median of 20 ms per event on an Nvidia L4 GPU, compared to approximately 110 ms for the standard CMS PF reconstruction.
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Plasma Physics

Plasma Decay of Nanosecond Pulsed Laser-Produced Ar and Ar-H2O Sparks at Atmospheric Pressure

Ji Yung Ahn, Jianan Wang, Tasnim Akbar Faruquee et al. (+1 more)

Time-resolved diagnostics were applied to investigate free-electron properties in nanosecond laser-produced discharges sustained at atmospheric pressure in Ar and in Ar with 3% H2O. The discharges were generated using 23 ns, 1064 nm laser pulses. Broadband plasma imaging and laser Thomson scattering were combined with optical emission spectroscopy, with particular emphasis on Stark broadening of the Halpha and Hbeta lines. The plasma exhibited a bright emission that persisted for up to 30–40 us after breakdown, followed by a very weak glow lasting up to 19 ms. Peak electron number density of about 2 x 10^17 cm-3 and electron temperature of about 7 eV were measured. Excellent agreement between both techniques was obtained for absolute electron number densities. The inferred temporal decay of free electrons is consistent with processes dominated by ambipolar expansion and two- and three-body electron-ion recombination. These results provide benchmark data for modeling nanosecond laser discharges and demonstrate the reliability of combining Thomson scattering with Stark broadening in atmospheric laser sparks.
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All authors Ji Yung Ahn, Jianan Wang, Tasnim Akbar Faruquee, Marien Simeni Simeni

Biological Physics · Cross-listed from q-bio.NC

Unsupervised sleep-like intra- and inter-layer plasticity categorizes and improves energy efficiency in a multilayer spiking network

Leonardo Tonielli, Cosimo Lupo, Elena Pastorelli et al. (+4 more)

Sleep is thought to support memory consolidation and the recovery of optimal energetic regime by reorganizing synaptic connectivity, yet how plasticity across hierarchical brain circuits contributes to abstraction and energy efficiency remains unclear. Here we study a spiking multi-layer network alternating wake-like and deep-sleep-like states, with state-dependent dendritic integration and synaptic plasticity in a biologically inspired thalamo-cortical framework. During wakefulness, the model learns from few perceived examples, while during deep sleep it undergoes spontaneous replay driven by slow oscillations. Plasticity enabled not only within intra-layer connections, but also in inter-layer pathways, is critical for memory consolidation and energetic downshift. Compared to restricted plasticity, full inter-layer plasticity yields higher post-sleep visual classification accuracy and promotes the emergence of sharper class-specific associations. Furthermore, we introduce a biophysically grounded estimator of metabolic power expressing network energy consumption in ATP units, partitioned into baseline, synaptic maintenance, action potential, and transmission costs. We find that inter-layer plasticity in sleep leads to a larger reduction in firing rates, synaptic strength and synaptic activity, corresponding to a substantially larger decrease in power consumption. This work suggests promising elements to be integrated in neuromorphic/energy-efficient AI learning systems, supported by brain state-specific apical mechanisms.
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All authors Leonardo Tonielli, Cosimo Lupo, Elena Pastorelli, Giulia De Bonis, Francesco Simula, Alessandro Lonardo, Pier Stanislao Paolucci

Optics

Emission of nitrogen-vacancy centers in diamond shaped by topological photonic waveguide modes

Raman Kumar, Chandan, Gabriel I. López Morales et al. (+6 more)

As the ability to integrate single photon emitters into photonic architectures improves, so does the need to characterize and understand their interaction. Here, we use a scanning diamond nanocrystal to investigate the interplay between the emission of room-temperature nitrogen-vacancy (NV) centers and a proximal topological waveguide. In our experiments, NVs serve as local, spectrally broad light sources which we exploit to characterize the waveguide bandwidth as well as the correspondence between light injection site and directionality of wave propagation. Further, we find that near-field coupling to the waveguide influences the spectral shape and ellipticity of the NV photoluminescence, hence allowing us to reveal nanostructured light fields with a spatial resolution defined by the nanoparticle size. Our results expand on the sensing modalities afforded by color centers, and portend novel opportunities in the development of on-chip, quantum optics devices leveraging topological photonics to best manipulate and readout single-photon emitters.
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All authors Raman Kumar, Chandan, Gabriel I. López Morales, Richard Monge, Anton Vakulenko, Svetlana Kiriushechkina, Alexander B. Khanikaev, Johannes Flick, Carlos A. Meriles

Chemical Physics · Cross-listed from cond-mat.stat-mech

A Local Structural Basis to Resolve Amorphous Ices

Quinn M. Gallagher, Ryan J. Szukalo, Nicolas Giovambattista et al. (+2 more)

Phases with distinct thermodynamic properties must differ in their underlying microscopic configurations. While ordered phases are readily distinguished by unit cells and space groups, the local structural basis differentiating amorphous phases is less apparent. Here, using a new probabilistic data-driven framework applied to molecular simulations of water, we identify local collective variables that discriminate low-density and high-density amorphous (LDA and HDA) ices and characterize pressure-induced transitions between them. As expected, descriptors related to local density effectively distinguish LDA and HDA; however, phase identity is surprisingly encoded within the first coordination shell. Furthermore, the pressure-induced LDA–HDA transformation proceeds through redistribution between LDA- and HDA-like local environments with no evidence for intermediate structures, consistent with a first-order-like phase transition. This contrasts with the gradual structural evolution observed in other amorphous systems, such as metallic glasses. Critically, local hydrogen density reveals pronounced structural hysteresis between compression and decompression pathways, which is not apparent in orientational order parameters, demonstrating that the microscopic interpretation of amorphous transformations depends fundamentally on descriptor choice. These findings are robust across force fields and provide a general strategy for characterizing disordered phases lacking obvious distinguishing features.
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All authors Quinn M. Gallagher, Ryan J. Szukalo, Nicolas Giovambattista, Pablo G. Debenedetti, Michael A. Webb

Applied Physics · Cross-listed from cond-mat.mtrl-sci

Non-monotonic roughness evolution in film growth on weakly interacting substrates

Dmitry Lapkin, Ismael S. S. Carrasco, Catherine Cruz Luukkonen et al. (+5 more)

Thin film deposition on weakly interacting substrates exhibits a unique growth mode characterized by initially strong island formation and rapidly increasing roughness, which reaches a maximum and subsequently decreases as the film returns to a smooth morphology. Here we show this rough-to-smooth growth mode experimentally for two molecular systems with substantially different geometries, namely, the effectively spherical buckminsterfullerene (C$_{60}$) and the disk-like 1,4,5,8,9,11-hexaazatriphenylenehexacarbonitrile (HATCN). This growth mode is explained by a geometrical model that captures the basic mechanisms of multilayer island growth, island coalescence, and formation of a continuous film. Additionally, kinetic Monte Carlo simulations with minimal ingredients demonstrate that this mode generally occurs for weakly interacting substrates, providing quantitative estimates of parameters that characterize adsorbate-adsorbate and adsorbate-substrate interactions. Both the model and simulations accurately describe the experimental data and highlight the generic nature of the phenomenon, independently of the details of the interactions and the molecular flux, which opens up a path for controlling nanoscale film roughness.
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All authors Dmitry Lapkin, Ismael S. S. Carrasco, Catherine Cruz Luukkonen, Oleg Konovalov, Alexander Hinderhofer, Frank Schreiber, Fábio D. A. Aarão Reis, Martin Oettel

Geophysics

Curvelet-Regularized SPDE Inversion on Piecewise-Planar Fractures with Trace-Graph Coupling

J.J. Segura

We formulate a sparse-to-dense reconstruction layer for fractured media in which sparse point measurements are mapped onto piecewise-planar fracture supports inferred from 3D trace polylines. Each plane is discretized in local coordinates and estimated via a convex objective that combines a grid SPDE/GMRF quadratic prior with an $\ell_1$ penalty on undecimated discrete curvelet coefficients, targeting anisotropic, fracture-aligned structure that is poorly represented by isotropic smoothness alone. We further define an along-fracture distance through trace-network geodesics and express connectivity-driven regularization as a quadratic form $z^\top P^\top L_G P z$, where $L_G$ is a graph Laplacian on the trace network and $P$ maps plane grids to graph nodes; plane intersections are handled by linear consistency constraints sampled along intersection lines. The resulting optimization admits efficient splitting: sparse linear solves for the quadratic block and coefficient-wise shrinkage for the curvelet block, with standard ADMM convergence under convexity. We specify reproducible synthetic benchmarks, baselines, ablations, and sensitivity studies that isolate directional sparsity and connectivity effects, and provide reference code to generate the figures and quantitative tables.
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Computational Physics

Frequency-domain general synthetic iterative scheme for efficient simulation of oscillatory rarefied gas flows

Pengshuo Li and Lei Wu

Oscillatory rarefied gas flows are frequently encountered in MEMS, and their efficient numerical simulation remains a major challenge due to the time dependent nature of the problem and the high dimensionality of the Boltzmann kinetic equation. Here, we address this challenge by focusing on the periodic steady state and solving the resulting problem using the frequency domain general synthetic iterative scheme (GSIS). The key idea of GSIS is to simultaneously solve the mesoscopic kinetic equation and the macroscopic synthetic equation. The kinetic equation provides high-order constitutive relations, beyond those given by the Newton law of viscosity and the Fourier law of heat conduction, to the synthetic equation. In turn, the synthetic equation, which converges to the periodic steady state much faster than the kinetic equation, boosts the evolution of the kinetic equation toward the periodic steady state. As a result, super convergence is achieved, together with an asymptotic preserving property that allows the use of coarse spatial grids. The analytical Fourier stability analysis and the Chapman-Enskog expansion, together with challenging numerical simulations, are employed to demonstrate the fast convergence and asymptotic-preserving properties of GSIS, revealing that it can be three orders of magnitude faster than conventional kinetic schemes in near continuum flow regimes.
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Instrumentation and Detectors

Polyurethane-Based Scintillators for Neutron and Gamma Radiation Detection in Medical and Industrial Applications

Olga Maiatska, Torsten Dünnebacke, Martin Kreuels et al. (+3 more)

Organic scintillators using a solid polyurethane (PU) matrix have been introduced to combine the robustness of a construction material with scintillating properties that allow gamma rays and fast neutrons to be detected efficiently and at low cost. This work compares two corresponding materials, the older M600 and the more recent M700, with EJ-276D and EJ-200 representing common plastic scintillators with and without pulse-shape discrimination (PSD) capabilities, respectively. Characterization measurements were performed with small samples of 26 mm diameter and 10 mm height, which were coupled to a photomultiplier tube (PMT) and simultaneously exposed to 252Cf fission neutrons and 137Cs gamma rays. M700 turned out to provide the best PSD performance and about the same light yield as EJ-276D, while its light pulses exhibit a shorter pulse decay. An accelerated ageing process applied in between two test campaigns was too short to trigger distinct performance degradation in any of the materials, though optical degradation was visible in EJ-276D and in EJ-200 but not in the PU-based materials. Nevertheless, the extremely robust polyurethane matrix promises advantages in medical and industrial applications where resilience and long-term stability are of crucial importance.
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All authors Olga Maiatska, Torsten Dünnebacke, Martin Kreuels, Guntram Pausch, Falko Scherwinski, Jürgen R. Stein

Atmospheric and Oceanic Physics

Atmospheric Methane Removal as a Third Climate Intervention: Termination Risks and Air Pollutant Effects

Katsumasa Tanaka, Weiwei Xiong, Didier A. Hauglustaine et al. (+8 more)

Atmospheric Methane Removal (AMR) is a third class of climate intervention, along with Carbon Dioxide Removal (CDR) and Solar Radiation Management (SRM). We show that, unlike CDR, the avoided warming by AMR is not durable due to methane's short atmospheric lifetime, although its temperature rebound upon termination is less abrupt than that of SRM. AMR's impact on tropospheric ozone can be further modulated by background pollutant levels.
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All authors Katsumasa Tanaka, Weiwei Xiong, Didier A. Hauglustaine, Daniel J.A. Johansson, Nico Bauer, Philippe Bousquet, Philippe Ciais, Renaud de Richter, Marianne T. Lund, Ragnhild B. Skeie, Eric Zusman

Computational Physics · Cross-listed from quant-ph

Qhronology: A Python package for studying quantum models of closed timelike curves

Lachlan G. Bishop

Qhronology is a novel scientific-computing package for studying quantum models of closed timelike curves (CTCs) and simulating general quantum information processing and computation. Written in Python, the program provides a comprehensive framework for analyzing quantum theories of antichronological time travel, including functionality to calculate quantum resolutions to temporal paradoxes. It also operates as a complete quantum circuit simulator, enabling the examination of quantum algorithms and protocols in both numerical and symbolic capacities. In this paper, we formally introduce Qhronology, beginning with discussion on aspects of its design philosophy and architecture. An overview of its basic usage is then presented, along with a collection of examples demonstrating its various capabilities within a variety of distinct contexts. Lastly, the performance of the package's circuit simulation component is characterized by way of some simple empirical benchmarking.
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Computational Physics

Conformal Quantile Regression for Neural Probabilistic Constitutive Modeling

Bahador Bahmani

Biological soft tissues exhibit substantial inter-subject variability, making the automation of constitutive material modeling essential for patient-specific analysis and design. Such materials are not only highly nonlinear but also display intrinsic stochasticity arising from their complex and heterogeneous microstructure. Despite recent advances in data-driven constitutive modeling, most existing approaches remain deterministic and fail to quantify predictive uncertainty, thereby limiting their reliability in downstream mechanical analyses. In this work, we propose a probabilistic, data-driven constitutive modeling framework for anisotropic soft materials that explicitly accounts for uncertainty through conformalized quantile regression applied to tensor-valued fields. The proposed framework is built upon a strain-invariant, polyconvex formulation that ensures thermodynamic consistency and promotes robust predictive performance, including in extrapolative regimes. A key advantage of the proposed approach is its simplicity: it can be applied in a plug-and-play manner to endow existing deterministic models with probabilistic predictions, while remaining distribution-free and requiring no assumptions on the underlying data distribution. Moreover, the method is straightforward to train, scalable to models with a large number of parameters, and avoids Monte Carlo sampling at inference, making it computationally efficient and well suited for uncertainty propagation in large-scale mechanical simulations. The proposed method is validated using several benchmark datasets synthesized and collected from the literature.
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