Geophysics
Stochastic Resonance in a Thermally Driven Low-Dimensional Geodynamo Model
Giuseppina Nigro, Edoardo Cascio, Giuseppe Consolini et al. (+1 more)
Geomagnetic field reversal sequences exhibit persistence times spanning a broad range, from a few $10^4$ years to superchrons lasting more than $10^7$ years. Despite extensive observational and theoretical work, the physical mechanisms governing how such reversals occur and how their broad temporal variability is organized are still not fully understood. Here we investigate the temporal variability of geomagnetic polarity in a thermally driven low-dimensional geodynamo model subject to a slow periodic modulation of the control parameter governing the large-scale induction, namely the $\alpha$-effect parameter. We find that the modulation generates a multipeaked probability density function of
magnetic persistence times, with local maxima occurring at approximately integer multiples of the modulation timescale, as expected in a
stochastic-resonance-like regime. The peak positions follow an approximately linear dependence on their index, showing that the characteristic timescales selected by the system are set by the imposed modulation period. These results provide a physically motivated numerical framework in which slow modulation of a geodynamo control parameter can organize reversal statistics through
stochastic-resonance-like dynamics.
Read the full abstract ▾
All authors Giuseppina Nigro, Edoardo Cascio, Giuseppe Consolini, Francesco Berrilli
Applied Physics
Efficiency in a repetitive pulse magnet
Akihiko Ikeda, Yuto Ishii, Yasuhiro H. Matsuda et al. (+3 more)
A repetitive-pulse magnet is a promising tool when combined with repetitive excitations, such as pulsed
lasers. Technically, the repetition and the
magnetic field values in a repetitive-pulse magnet are limited by the Joule heating in the coil. Here, we analytically examine the relationship between the coil's dimensions and its efficiency, assuming negligible heating of the coil, to design an optimized high-repetition, high-
magnetic-field coil. We calculated the dependence of the maximum
magnetic field, energy loss, pulse duration, form factor, impedance, and maximum current on the coil's geometry. We found that the smaller the coil, the more pulses and the more intense the
magnetic fields we can obtain under a given condition. We argue that the obtained trend arises from a complex interplay among various parameters.
Read the full abstract ▾
All authors Akihiko Ikeda, Yuto Ishii, Yasuhiro H. Matsuda, Go Yumoto, Ayumi Abe, Ryusuke Matsunaga
Instrumentation and Detectors
Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring
Nicholas Tan Jerome, Nadia Aouadi, Christoph Koehler et al. (+2 more)
The Karlsruhe Tritium
Neutrino Experiment (KATRIN) aims to measure the absolute
neutrino mass with unprecedented sensitivity, requiring precise monitoring of the windowless gaseous tritium source, where tritium beta decay occurs. To track variations of the source activity, beta-induced X-ray spectroscopy provides real-time diagnostics. However, traditional drift detection methods struggle with the infrequent and transient nature of instability events in gaseous tritium. This study bridges the gap between state-of-the-art time-series
forecasting models and real-world experimental applications by leveraging deep learning to predict the time to stability after instabilities. Unlike standard benchmarking approaches that emphasize algorithmic performance on fixed
datasets, we apply
forecasting models – including LSTM, N-BEATS, TFT, NHITS, DLinear, NLinear, TSMixer, and Chronos-LLM – to complex, large-scale experimental data. Our findings highlight two challenges: learning from sparse instability events and
forecasting long time horizons (i.e., predicting hundreds of future points), both of which are ongoing challenges in time-series
forecasting and remain active areas of research. This prediction task has direct experimental value by enabling better scheduling and maintenance planning. A reliable forecast of stability time allows for more efficient measurement and task management during stabilization periods. Through model selection, we identified N-BEATS as the top performer, excelling in accuracy and repeatability, demonstrating that deep learning can optimize large-scale physics experiments.
Read the full abstract ▾
All authors Nicholas Tan Jerome, Nadia Aouadi, Christoph Koehler, Suren Chilingaryan, Andreas Kopmann
Biological Physics · Cross-listed from cond-mat.stat-mech
Breakdown of Adiabatic Scaling and Noise-induced Functional Synchronization in Deeply Quiescent Excitable Systems
Yefan Wu
Coherence resonance (CR) characterizes noise-induced regularity in excitable systems, yet its evaluation in quiescent biological media is often obscured by flattened energy landscapes and complex nonlinear dynamics. In this study, we investigate the
stochastic dynamics of a 3D Sherman-Rinzel-Keizer (SRK) model driven by multiplicative Feller noise. We show that traditional extremal evaluations of CR encounter a "bathtub effect", a broad resonance valley that can lead to statistical inaccuracies. To address this, we propose a logarithmic centroid extraction method, which filters out
stochastic jitter and recovers the underlying adiabatic Kramers scaling with high linearity. Furthermore, we identify the physical boundary where this adiabatic approximation breaks down under the strong-noise limit. Extending our analysis to gap-junction coupled systems, we observe a noise-induced transition from sub-threshold physiological shivering (characterized by statistical correlation but negligible functional output) to macroscopic functional synchronization. Our results provide a mathematical framework for extracting optimal noise intensities in broad energy valleys and offer insights into how quiescent biological systems utilize
stochastic fluctuations for functional recovery.
Read the full abstract ▾
Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech
Brain criticality through nonadditive entropic analysis of electroencephalograms
Henrique Santos Lima, Constantino Tsallis and Dimitri M. Abramov
On the grounds of nonadditive
entropies – appropriate for complex systems – we investigate the electroencephalogram amplitudes of typical and ADHD children. The corresponding probability distributions are $q$-Gaussians, i.e., $\rho(x) \propto e_q^{-\beta x^2} \equiv [1+(q-1) \beta x^2]^{1/(1-q)}$, where $(q,\beta)$ are, respectively, the entropic index characterizing complexity and the inverse width. We show that $q$ tends to monotonically vary with $\beta$ for both typical and ADHD subjects, thus revealing critical behavior of the
brain. Moreover, we verify that ADHD subjects have a higher complexity than the typical ones. Consistently, biomarkers for objective phychyatric diagnosis could emerge along this path. We show that $q$ tends to monotonically vary with $\beta$ for both typical and ADHD subjects, thus revealing critical behavior of the
brain. Moreover, we verify that ADHD subjects have a higher complexity than the typical ones. Consistently, biomarkers for objective phychyatric diagnosis could emerge along this path.
Read the full abstract ▾
Plasma Physics · Cross-listed from astro-ph.SR
MHD simulations on the large-scale propagation of high-speed solar wind streams
Stefan J. Hofmeister
We investigate the propagation of high-speed
solar wind streams from their origin near the Sun to 1 AU using three-dimensional magnetohydrodynamic simulations. By tracking both global stream structure and individual
plasma parcels, we assess how local in-situ measurements relate to the underlying
plasma evolution. We find that high-speed streams are not parcel-preserving structures: commonly used diagnostics such as peak velocity, density, or temperature do not trace fixed
plasma elements, and feature-based radial trends can therefore misrepresent the true
evolution. Instead, velocity-based relationships provide a more robust framework for linking
plasma parcels across heliocentric distances. Stream
evolution is dominated by interaction regions, where compression leads to deceleration of fast wind, acceleration of slow wind, and significant heating. A boundary layer forms close to the Sun and can dominate narrow streams, biasing in-situ measurements toward lower apparent velocities. We show that three-dimensional transport, in particular latitudinal flows, redistributes mass and
magnetic flux and reduces center-to-flank contrasts. While radial
magnetic flux is conserved, the total field strength is not in spherical sampling geometries due to non-radial components. Finally, observed stream properties and geoeffectivity depend strongly on sampling location, stream geometry, and latitudinal
magnetic deflection, introducing systematic variability and asymmetries in geomagnetic response.
Read the full abstract ▾
Atmospheric and Oceanic Physics
Cast3: Translating numerical weather prediction principles into data-driven forecasting
Congyi Nai, Baoxiang Pan, Yuan Liang et al. (+1 more)
Data-driven weather models have made rapid advances in recent years, reaching and in some metrics surpassing the large-scale forecast skill of operational numerical weather prediction. This progress, however, has been built almost entirely on the reanalysis data that NWP produced, while the methodological knowledge that the NWP community distilled over decades of multi-scale atmospheric modelling remains largely unused. Here we present Cast3, a generative
forecasting framework that systematically absorbs NWP meta-knowledge to close this gap. Cast3 operates on variable-resolution cubed-sphere grids for scale-aware representation and constructs structurally diverse super-ensembles that sample the complementary biases of different grid discretizations, delivering state-of-the-art ensemble prediction. It further introduces generative nudging, a posterior-sampling strategy that distils the collective information of the full ensemble into a single forecast possessing both the large-scale accuracy of the ensemble mean and the mesoscale realism of a high-resolution member. Evaluated across synoptic-scale skill, spectral fidelity, station-level surface verification, and tropical cyclone prediction, Cast3 outperforms established deterministic and generative baselines across various dimensions. More broadly, these results demonstrate that the design principles embedded in computational atmospheric science offer a rich and largely untapped foundation for the next generation of data-driven Earth system modelling.
Read the full abstract ▾
All authors Congyi Nai, Baoxiang Pan, Yuan Liang, Xi Chen
Plasma Physics
Conductor-Insulator Crossover in the Steady-State Ultracold Plasmas
Yurii V. Dumin and Ludmila M. Svirskaya
We present a theoretical model of the ionization-recombination balance in the ultracold Rydberg gas-
plasma mixture, which is caused by the collective processes rather than by individual interparticle interactions. We consider the electron-ion system where each electron moves for the most part of its time in the effective centrifugal potential formed by the nearest ion and sometimes jumps to the neighboring potential wells due to the perturbations from remote particles. These perturbations are described by a thermal bath with the effective "virial" temperature. Then, the electron with a sufficiently high energy, which can escape from the local potential well, should be considered as free (conducting) one; while the electron with low energy, confined within the well, as forming the Rydberg atom. As follows from our calculations, there is a sharp crossover from the insulating phase (Rydberg gas) to the conducting one (
plasma) with increase in the particle density, which somewhat resembles the Mott transition in condensed-matter physics. This model should be well relevant to the steady-state ultracold
plasmas obtained in the most recent experiments and represents a promising direction for further research.
Read the full abstract ▾
Optics
Strong light-matter interactions in hybrid polaritonic systems
Ben Johns, Andrea Schirato, Federico Toffoletti et al. (+12 more)
Strong light-matter coupling gives rise to polaritons - hybrid excitations whose mixed photonic and matter character enables control over optical, electronic and chemical properties. This Feature Article surveys the main architectures supporting polariton formation, including photonic microcavities, plasmonic nanostructures, open cavities and metasurfaces, and outlines how inorganic
semiconductors, organic aggregates and hybrid systems access strong and ultrastrong coupling. Key phenomena such as coherent dynamics, vibronic interactions, dark-state reservoirs and polariton-mediated energy and electron transport are discussed, together with the experimental and theoretical tools used to study them. We highlight examples where strong coupling modifies charge transport, energy flow and chemical reactivity, and we summarize emerging regimes, including intermediate and dark-strong coupling, that broaden the landscape of hybrid light-matter physics.
Read the full abstract ▾
All authors Ben Johns, Andrea Schirato, Federico Toffoletti, Tommaso Giovannini, Mirko Vanzan, Margherita Marsili, Giovanni Parolin, Giulia Dall'Osto, Ajay Kumar Poonia, Chiara Cappelli, Francesca Baletto, Stefano Corni, Elisabetta Collini, Margherita Maiuri, Nicolò Maccaferri
Physics and Society · Cross-listed from econ.EM
Hall-Like Transversal Stress and Sandpile Criticality on Real Production Networks
Diego Vallarino
This paper develops a
Hall-Sandpile model of economic instability that combines a
Hall-like transversal stress mechanism with sandpile threshold dynamics on a real production-network substrate. In analogy with the physical
Hall effect, where exposed flows under an external field generate stress in a transversal direction, we model economic shocks as fields that act on flow-intensive, low-redundancy, low-capacity nodes and produce systemic stress through a multiplicative conversion function. The accumulated stress drives a discrete toppling rule and an avalanche dynamics whose effective activation threshold declines with transversal exposure. The model is calibrated on annual World Input–Output Database (WIOD) production networks for 2000–2014 and simulated on the 2014 substrate (2{,}283 country–sector nodes) under three alternative propagation normalisations to avoid mechanical near-criticality from row-
stochastic operators. Controlled Monte Carlo experiments over external field intensity and redundancy stress generate four ordered regimes: stable absorption, latent fragility, critical transition, and avalanche regime. Mean avalanche size and the probabilities of finite-size systemic events $\Pr(S\!\geq\!5)$, $\Pr(S\!\geq\!10)$ and $\Pr(S\!\geq\!20)$ rise jointly with field intensity and redundancy stress. Tail diagnostics show regime-dependent thickening of the avalanche distribution, but the estimated tail indices remain too high to interpret as evidence of universal power-law criticality. The contribution is therefore a finite-size, real-network description of how transversal stress activates structural fragility, not a claim of self-organised criticality in the global economy.
Read the full abstract ▾
Physics Education
Smartphone-based measurement of magnetic force and demonstration of Newton third law of motion
Sanjoy Kumar Pal, Soumen Sarkar and Pradipta Panchadhyayee
A fascinating approach to teaching Newton's Third Law using readily available technology is presented in this article.
Magnetic forces are measured by using a smartphone's pressure sensor, two ring magnets, and common household items. Students can measure the magnitudes of forces, gain a more tangible understanding of the law, and see how 'action' and 'reaction' are quantitatively equal and opposite.
Read the full abstract ▾
Chemical Physics · Cross-listed from quant-ph
Cavity-mediated localization and collective electron correlation phases
Dominik Sidler, Francesco Greco, Michael Ruggenthaler et al. (+1 more)
Collective strong coupling of molecular ensembles to optical cavities opens a route to modifying matter through genuinely collective electronic correlations. Yet even in the absence of a cavity, Coulomb correlations are notoriously difficult to describe, and cavity coupling adds transverse correlation channels extending over the entire molecular ensemble. Here we show that this seemingly intractable problem admits a controlled description by
spin glass theory, i.e., by the analytically solvable spherical Sherrington-Kirkpatrick model. Our results predict two collective correlation phases, a paracorrelated phase and a
spin-glass correlation phase, beyond the conventional uncorrelated molecular regime. These phases reveal an
entropy-driven localization-delocalization mechanism that transfers molecular electronic states into collectively degenerate cavity-dressed states. Analytic calculation suggest that this collective correlated state can either remain insulating or even turn metallic if the entropic occupations favor a fractional filling. Our work reveals cavity-mediated electron correlations as a microscopic mechanism for emergent phases in strongly coupled molecular ensembles.
Read the full abstract ▾
All authors Dominik Sidler, Francesco Greco, Michael Ruggenthaler, Angel Rubio
Computational Physics · Cross-listed from eess.SP
Physics-Guided Deep Learning For High Resolution X-ray Imaging
Shao Xian Lee, Aashwin Ananda Mishra, Ariel Arnott et al. (+21 more)
Imperfections in X-ray imaging systems can limit their performance, especially in High Energy Density (HED) or Inertial Fusion Energy (IFE)-relevant experiments that are typically single shot, by introducing structured, non-stationary features that overlap with the signal of interest. When the X-ray transmission is reconstructed by typical flat-field normalization, even small shot-to-shot drift of structured features imprints residual patterns onto transmission maps, degrading signal visibility and biasing measurements such as electron density, velocity and feature sizes. We investigate this limitation by modeling the artifacts as a separable feature layer and training a U-Net architecture to estimate and infer them directly from the experimental data. We compare our method against
Fourier filtering and more advanced procedures like Dynamic Flat-Field Normalization (DFFN) to evaluate artifact suppression capability and signal preservation in the reconstructed transmission maps. In multiple synthetic injection tests, our Physics-Guided Deep Learning approach is able to obtain an improvement in mean Structural Similarity Index (SSIM) from 0.345 to 0.906 and from 0.0679 to 0.945, while better preserving filament profiles and reducing degradation of the filament signal during artifact suppression. Additionally, we utilize deep ensembles to obtain predictive epistemic uncertainty estimates for the U-Net based reconstruction, to ensure Out Of Distribution (OOD) robustness for this procedure.
Read the full abstract ▾
All authors Shao Xian Lee, Aashwin Ananda Mishra, Ariel Arnott, Meriame Berboucha, Nina Boiadjieva, Gourab Chatterjee, Eric Cunningham, Nick Czapla, Gilliss Dyer, Jonathan Ehni, Robert Ettelbrick, Anna Grassi, Mickael Grech, Philip Hart, Dimitri Khaghani, Hae Ja Lee, Peregrine McGehee, Bob Nagler, Paul Neumayer, Caterina Riconda, Marc Welch, Andrea Zabala, Eric Galtier, Quynh L. Nguyen
Computational Physics · Cross-listed from cs.LG
Mesh Based Simulations with Spatial and Temporal awareness
Paul Garnier, Vincent Lannelongue and Elie Hachem
Machine Learning surrogates for Computational Fluid Dynamics (CFD), particularly Graph Neural Networks (GNNs) and
Transformers, have become a new important approach for accelerating physics simulations. However, we identify a critical bottleneck in the field: while architectures have advanced significantly, the common underlying training paradigms remain bound to naive assumptions, such as node-wise supervision and explicit Euler time-stepping. These legacy choices ignore the stiff dynamics and local flux continuity inherent to numerous partial differential equations resolution methods, such as Finite Element, Difference, or Volume (FEM). In this work, we propose a unified framework to bridge the gap between
geometric deep learning and rigorous numerical analysis. We introduce three key innovations: (1) Multi Node Prediction, a stencil-level objective that predicts field values for a node's full local
topology, enforcing spatial derivative consistency; (2) Temporal Correction, replacing unstable explicit schemes with a predictor-corrector via temporal Cross-
Attention; and (3)
Geometric Inductive Biases, leveraging 3D Rotary Positional
Embeddings (RoPE) to robustly capture rotational
symmetries in unstructured meshes. We evaluate this framework across three architectures (MeshGraphNet, Transolver, and a
Transformer) on diverse physics
datasets. Our approach yields consistent improvements in accuracy and stability, particularly in long-horizon rollouts, while producing latent representations that generalize to unseen subtasks such as Wall Shear Stress or Pressure prediction. Code is available at https://github.com/DonsetPG/graph-physics.
Read the full abstract ▾
Medical Physics
Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates
Paul Xing, Antoine Malescot, Eric Martineau et al. (+4 more)
Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP)
brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic quantification. Extending this approach to 3D transcranial ULM imaging in NHPs, we show efficient correction of skull induced aberrations and removal of artifacts, such as vessel duplications. By providing a fully automated and generalizable solution for aberration correction, this work lowers a major technical barrier to transcranial ultrasound imaging, enabling broader adoption of non-invasive, super-resolution and functional neuroimaging across laboratories and across species.
Read the full abstract ▾
All authors Paul Xing, Antoine Malescot, Eric Martineau, Stephan Quessy, Ravi L. Rungta, Numa Dancause, Jean Provost
Physics Education
LiDAR based determination of spring constant using smartphones
Sanjoy Kumar Pal, Soumen Sarkar and Pradipta Panchadhyayee
A novel use of the
LiDAR sensor of a smartphone in introductory physics experiments is discussed in this article. We have determined the spring constant for various combinations of springs using the
LiDAR sensor of a smartphone through the phyphox application. An electrical heater coil is used as a spring, and the period of oscillation of a vertical spring-mass system is measured using a
LiDAR sensor. The experimental values of spring constants agree with the theoretical values. A high school student can perform this simple experiment in a smart way at home.
Read the full abstract ▾
Applied Physics · Cross-listed from cond-mat.soft
Dimple-Encoded Reprogrammable Origami
Qun Zhang, Weicheng Huang, Amir Hajiyavand et al. (+4 more)
Programmable folding of elastic sheets typically relies on predefined flexible creases or active materials-enabled hinges, which lack intrinsic bistability and limit reprogrammability within a single structure. Here, we present a dimple-encoded origami platform that converts bistable dimple snapping into spatially addressable hinges with prescribed folding angles in a continuous sheet. This interaction-enabled mechanism enables the design of distributed hinge networks through the arrangement and selective inversion of dimples. We establish folding-angle design charts that can be directly used to select local dimple arrangements for target fold angle, forming a practical hinge library without altering the underlying unit geometry. Using this approach, a single dimpled sheet can be reprogrammed to realize multiple distinct configurations, such as triangle, square, and pentagon shapes. We further extend the method to flat-to-3D morphing of polyhedral origami and validate the results through experiments and finite element simulations. As demonstrations, we realize self-supporting cubic shells with enhanced impact resistance and partially deployable cube configurations that remain stable upon opening, highlighting their potential for protective enclosures and deployable architectural structures. The proposed strategy provides a fabrication-friendly route to reprogrammable shape-morphing and adaptive mechanical systems.
Read the full abstract ▾
All authors Qun Zhang, Weicheng Huang, Amir Hajiyavand, Hyunyoung Kim, Claire Dancer, Karl Dearn, Mingchao Liu
Physics Education
Determination of the magnetic moment of a magnet by letting it fall through a conducting pipe
Sanjoy Kumar Pal, Soumen Sarkar and Pradipta Panchadhyayee
A novel method is proposed to determine the
magnetic moment of a magnet by studying its free-falling motion inside a non-
ferromagnetic and conducting pipe. The dynamics of a neodymium magnet falling inside a pipe is tracked by using sound waves of a fixed frequency generated by one smartphone and detecting acoustic resonance in the pipe simultaneously by the other. This tracking technique leads to the measurement of the terminal velocity of the falling magnet, as the interaction between the magnet and the conducting pipe creates viscosity artificially. The result obtained is verified by studying torsional oscillations of the suspended magnet and conforms to the reported value in such a low-cost setup. The experiment is designed with concepts integrating the domains of general physics, electromagnetic induction, and acoustics.
Read the full abstract ▾
Space Physics · Cross-listed from astro-ph.SR
On the Radial Evolution of the Solar Wind : The Source Alignment Method Applied to Parker Solar Probe and Solar Orbiter Observations
Jean-Baptiste Dakeyo, Tamar Ervin, Stuart Bale et al. (+10 more)
The properties of the
solar wind, as measured in situ throughout the heliosphere, depend both on the characteristics of its coronal source and on the intrinsic processes governing its interplanetary
evolution. Recently, radial and Parker spiral
alignment techniques have been applied to Parker
Solar Probe (PSP) and
Solar Orbiter (SO) observations to investigate the radial
evolution of the same
solar wind parcel. These studies have shown that the
solar wind can undergo significant acceleration even beyond its primary acceleration region (i.e., above 15
solar radii). However, such radial and Parker spiral
alignments are rare in practice, which limits the statistical significance and general applicability of the results. We introduce a new source
alignment technique designed to overcome these limitations. Using
magnetic backmapping, we associate similar
solar wind streams observed by the two spacecraft based on the proximity of their photospheric footpoints, combined with additional in-situ stream similarity criteria. Applying the source
alignment method to PSP and SO observations, we identify a total of 548
alignment intervals, each lasting 30 minutes. By constructing statistics over all
alignments, we find that the
solar wind speed increases by an average of 45% per radial decade (approximately 147 km/s) between the two probes. This result demonstrates that
solar wind acceleration in the inner heliosphere remains significant compared to that occurring below 15
solar radii. Among the different studied
plasma parameters, the radial
evolution of the electron temperature and
plasma density, show the strongest anti-correlation with the increase in bulk velocity.
Read the full abstract ▾
All authors Jean-Baptiste Dakeyo, Tamar Ervin, Stuart Bale, Pascal Démoulin, Nikos Sioulas, Victor Réville, Mingzhe Liu, Alexis Rouillard, Milan Maksimovic, Davin Larson, Orlando Romeo, Philippe Louarn, Roberto Livi
Physics and Society · Cross-listed from cs.RO
Cut-In Gap Acceptance Toward Autonomous vs. Human-Driven Vehicles: Evidence from the Waymo Open Motion Dataset
Abdulaziz Alhuraish, Yuhang Wang and Hao Zhou
Autonomous vehicles (AVs) are widely known to follow conservative, rule-based motion policies that surrounding drivers can learn to anticipate. A direct consequence is that human drivers may accept shorter longitudinal gaps when cutting in front of an AV than when targeting another human-driven vehicle (HDV). We test this hypothesis using the Waymo Open Motion
Dataset (WOMD), which provides 25,906 real-world highway scenarios at 10 hertz. An eight-criterion lane-change detector extracts 706 HDV-to-AV and 3,172 HDV-to-HDV cut-in events from the same traffic environment. The median accepted gap in front of the Waymo AV is 7.58 meters versus 9.57 meters for HDV targets, a 1.99 meter reduction that is statistically significant (p equals 5.76 times 10 to the negative eighth power, d equals negative 0.224) and persists under speed-matched resampling. Cut-in speeds toward the AV are 37 percent higher (51.7 versus 37.7 kilometers per hour, d equals 0.502), and 68.0 percent of AV-targeted cut-ins occur below the 10 meter gap boundary versus 51.8 percent of HDV-targeted events (chi-squared equals 60.5, p is less than 10 to the negative thirteenth power). These results reveal a systematic and safety-relevant asymmetry in human gap-acceptance behavior that warrants AV-specific calibration of both motion-planning safety envelopes and traffic simulation models.
Read the full abstract ▾
Optics
Label-Free Microrefractometry of Interfacial Processes Using Fluorescent Smart Coverslips
Hodaya Klimovsky, Amitay Ginsberg, Dmytro Ohorodniichuk et al. (+5 more)
Molecular dipoles near interfaces emit highly directional radiation due to near-field interactions, making surface-bound fluorophores sensitive probes of local physicochemical changes. We introduce smart coverslips, stably coated with uniform, brightly fluorescent nanobead films, that exploit refractive-index-dependent emission shifts for sensitive micro-refractometry in small volumes. Supercritical-angle fluorescence refractometry uses single back-focal-plane images to allow us real-time RI sensing and nanometric thin-film height measurements without the need for multi-angle or multi-wavelength acquisition. Our fast, label-free, and non-invasive approach allows measurements of thin-film properties and monitoring of interfacial dynamics on a standard inverted microscope and is broadly applicable to nanobiophotonics, chemical sensing, and in-situ materials analysis.
Read the full abstract ▾
All authors Hodaya Klimovsky, Amitay Ginsberg, Dmytro Ohorodniichuk, Maria Shehadeh, Ilya Olevsko, Gerardo Byk, Martin Oheim, Adi Salomon
Computational Physics · Cross-listed from cond-mat.soft
Colloidal layer deposition with a controllable number of layers and compositional order
Akshaya Kumar Jena, Aashima Aashima, Pritam Kumar Jana et al. (+1 more)
We design a system with a binary suspension of colloids and a surface that triggers the self-assembly of crystallites with a finite thickness. The proposed design allows controlling the number of layers forming the aggregate and constrains the two types of particles to lie on different planes. These functionalities are achieved by decorating the colloids and the surface with multiple DNA oligomers featuring specific interactions. Similarly to what was proposed in previous studies, the surface triggers a chain of reactions between DNA oligomers, leading to localized self-assembly. System parameters control the thickness of the aggregates. In this work, compositional order is achieved by engineering the reaction kinetics between DNA oligomers in a way that limits interactions between colloids of the same type. We validate our design using theory and reaction-
diffusion simulation
algorithms, which capture the multibody nature of the interactions. This work demonstrates how engineering the reaction kinetics provides a new tool for controlling the morphology of aggregates assembled by DNA.
Read the full abstract ▾
All authors Akshaya Kumar Jena, Aashima Aashima, Pritam Kumar Jana, Bortolo Matteo Mognetti
Optics
Real-space imaging reveals symmetry-selected nonlinear energy routing in a mechanical resonator
Ya Zhang, Yuko Terasawa, Qian Liu et al. (+5 more)
Nonlinear energy routing among modes underlies phenomena ranging from internal resonance and wave mixing to frequency-comb generation in micro- and nanoelectromechanical resonators, yet modal interactions are typically inferred from spectra rather than imaged in real space. This leaves unresolved how energy is spatially routed and what determines which pathways are selected. Here, we use phase-locked multi-harmonic stroboscopic interferometry to reconstruct harmonic-resolved differential displacement maps in a nearly mirror-symmetric microelectromechanical resonator. These maps reveal that harmonics generated by a driven mode can be carried by distinct spatial eigenmodes, directly resolving pathways of nonlinear energy transfer. We further show that such mode-selective routing occurs even away from integer frequency matching: generated harmonics are dominated by eigenmodes sharing the driven mode's mirror parity, whereas spectrally closer opposite-parity modes remain strongly suppressed. A nonlinear modal framework links this hierarchy to
symmetry-dependent modal-overlap integrals. These results identify spatial
symmetry as a selection rule for nonlinear energy routing.
Read the full abstract ▾
All authors Ya Zhang, Yuko Terasawa, Qian Liu, Shumpei Takenaka, Hua Li, Yutao Xu, Xueyong Wei, Kazuhiko Hirakawa
Optics
FID Magnetometer Based on Paraffin-Coated Planar Reflective Multipass Cells
Xiangyu Li, Tingxuan Guo, Yang Li et al. (+1 more)
We demonstrate a paraffin-coated planar reflective multipass vapor
cell for compact optical atomic magnetometry. The
cell has an internal volume of $12 \times 12 \times 8~\mathrm{mm}^3$ and supports 20 optical passes with a total transmittance exceeding $65\%$, while maintaining a longitudinal
spin-relaxation time of $^{87}\mathrm{Rb}$ longer than $1~\mathrm{s}$. The planar geometry provides spatially separated input and output beams, enabling compact optical integration. A single-
cell free-induction-decay (FID) magnetometer reaches $10~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ in the geomagnetic-field range, presently limited by current-source noise in the field coils. A two-
cell differential configuration achieves a sensitivity of $\sim 28~\mathrm{fT}/\sqrt{\mathrm{Hz}}$ over the $1$–$15~\mathrm{Hz}$ band for bias fields of $0.3$–$0.7~\mathrm{G}$. These results establish that paraffin-coated planar multipass
cells offer high optical depth, long coherence times, and an integration-friendly platform for ultrasensitive magnetometry.
Read the full abstract ▾
All authors Xiangyu Li, Tingxuan Guo, Yang Li, Kaifeng Zhao
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Revealing the kinetics of interfacial surfactant phase transitions through multiscale simulations and in-situ plasmonic sensing
Esmée Berger, Narjes Khosravian, Ferry Anggoro Ardy Nugroho et al. (+3 more)
Surfactant self-assembly at solid-
liquid interfaces governs interfacial stability, transport, and reactivity across many technologies, yet resolving interfacial surfactant phases and their transition kinetics in situ remains challenging. Here, we establish an atomistically grounded plasmonic framework that quantitatively maps interfacial surfactant phases and phase transitions onto optical signatures. Distinct morphologies differ in packing and hydration, modifying the effective permittivity within the optical near field and producing surfactant phase-specific plasmonic extinction peak shifts. Using cetyltrimethylammonium bromide on silica as a prototypical surfactant-surface system, we combine atomistic simulations, electronic-structure calculations, and continuum electrodynamics to translate molecular morphologies into predicted spectral shifts for literature-reported surface phases. We experimentally confirm the predicted ordering and magnitude of steady-state peak shifts during stepwise concentration changes, and extract transition kinetics from exponential relaxations of the time-resolved peak shift. A key mechanistic signature is reversal of the spectral shift direction upon transition from an impermeable
bilayer to a water-accessible, channel-containing phase, consistent with hydration-driven reduction of the local effective permittivity. Because the approach relies on dielectric contrast in the plasmonic near field and works through a dielectric overlayer, it provides a broadly applicable route for real-time identification of interfacial surfactant phases and their kinetics in aqueous conditions.
Read the full abstract ▾
All authors Esmée Berger, Narjes Khosravian, Ferry Anggoro Ardy Nugroho, Joakim Löfgren, Christoph Langhammer, Paul Erhart
Plasma Physics · Cross-listed from astro-ph.HE
Powerful parametric instability of Alfven waves in astrophysical pair plasma
Maxim Lyutikov
We demonstrate that in highly magnetized pair
plasmas, nonlinear Alfven waves with wave-number $k \leq k_0 = \omega_p^2 /(\delta \omega_B)$ ($\delta =( \delta B)/B_0$ are relative fluctuations of the
magnetic field) experience powerful modulational instability. In the two-fluid approximation, we develop an analytic set-up for circularly polarized (CP) Alfven mode in its frame (where the initial configuration is stationary; it is moving with relativistic, amplitude-dependent Alfven velocity $v_A (\sigma, \delta ) $, while both charges experience different, amplitude-dependent, synchrotron gyration). PIC simulations using EPOCH code demonstrate that for Alfven waves with $k$ near $k_0$, large, parametrically-driven density fluctuations develop, and lead to fast modulational instability. Charge separation effects, for a CP wave in magnetized pair
plasma, might be temporarily important; on longer time-scales the density fluctuations are charge neutral and in symmetric pair
plasma quickly grow to large amplitudes. In highly magnetized
plasma, $\sigma \gg 1$, high frequency modes $k / k_0 \sim (2-3 ) \times \sigma \gg 1 $ are quickly generated; for smaller
plasma magnetization, the dominant mode is at the Bragg's condition $k = 2 k_0$. Long term behavior of CP and LP modes is similar. We discuss application of the results to the physics of Fast
Radio Bursts generated/propagating in the magnetospheres of magnetars.
Read the full abstract ▾
Space Physics
Statistical analysis of solar energetic particle rise times using Earth and Mars observations and constraints on particle transport parameters
Yihang Cao, Jingnan Guo, Yuming Wang et al. (+3 more)
The propagation of
solar energetic particles (SEPs) in interplanetary space is modulated by
solar wind
turbulence, which significantly influences particle
diffusion and energy
evolution through scattering processes. Traditional analyses based on absolute flux measurements face inherent difficulties in disentangling source acceleration from subsequent transport, while temporal features such as onset and peak times are less affected and better suited for studying SEP transport. This study establishes a statistical relationship between the rise time of SEP events at different energies using multi-satellite observations at Earth and Mars. We use data from SOHO/ERNE and Tianwen-1/MEPA between November 2020 and March 2025, selecting 75 SEP events at 1 AU and 58 near Mars. For each energy range, onset times are determined by linear fitting, and peak times are extracted via a sliding median filter combined with Savitzky-Golay smoothing; the difference gives the SEP rise time. Comparing with the pure
diffusion equation prediction, we examine the statistical behavior of rise time at Earth and Mars. Despite event selection uncertainties, SEP rise time follows a clear power-law relation with energy. The flatter power-law at Mars indicates weaker energy dependence with increasing
solar distance. Using these empirical relations, we constrain the
rigidity dependence of the parallel mean free path within the parallel
diffusion model. Our results show that
turbulence scattering at Mars approaches a
rigidity-independent regime, reflecting
turbulence evolution toward a dissipation-dominated state from Earth to Mars.
Read the full abstract ▾
All authors Yihang Cao, Jingnan Guo, Yuming Wang, Zhuxuan Zou, Yongjie Zhang, Cunhui Li
Optics
Tracking doublon-holon dynamics in high-harmonic generation from Mott insulators
Tao-Yuan Du, Hui-Ru Li, Bo Li et al. (+1 more)
High-harmonic generation (HHG) in strongly correlated Mott
insulators is investigated using exact diagonalization and time-dependent density-
matrix propagation of a
laser-driven one-dimensional Hubbard chain. By projecting onto
equilibrium Hubbard bands, we use the doublon population and its dynamics as a diagnostic to analyze intraband (
spin-wave-like) and interband (doublon-holon creation) excitation channels. A filling-dependent crossover emerges: Bloch-like intraband response at dilute filling, mixed dynamics at intermediate filling, and interband-dominated HHG with plateau and cutoff near half filling. In the considered parameter range, increasing interaction strength $U$ strongly suppresses interband contributions through the enlarged Mott gap and correlation-induced localization. Intra- and interband current decomposition reveals opposing flows below the Mott gap (\Delta_{\mathrm{Mott}}) and selective dephasing suppression of interband coherence, enhancing net doublon accumulation. Time-frequency analysis uncovers the filling-dependent features of
quantum trajectories, manifesting in distinct below-\Delta_{\mathrm{Mott}} emission. This doublon-based analysis provides a transparent link between
equilibrium spin-charge separation and nonequilibrium strong-field response, and clarifies how dephasing modifies interband coherence and doublon accumulation.
Read the full abstract ▾
All authors Tao-Yuan Du, Hui-Ru Li, Bo Li, Ruifeng Lu
Computational Physics · Cross-listed from cs.LG
Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks
Maniru Ibrahim
Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and physical
equilibrium constraints. We investigate sequential learning in differentiable resistor networks governed by Kirchhoff's laws. Although individual input–output mappings can be learned by gradient-based adjustment of edge conductances, sequential training on conflicting tasks produces catastrophic forgetting. We show that forgetting is controlled by task conflict and by the degree of adaptation to the new task. Uniform anchoring and normalised gradient-weighted anchoring reduce forgetting only by increasing the final loss on the new task, giving a clear forgetting–adaptation trade-off. We also show that forgetting is associated with localised conductance changes on high-current edges, giving a physical interpretation as reconfiguration of dominant transport pathways. Broader random-task ensembles show that the strongest forgetting occurs when the second task reverses the output ordering imposed by the first task. Finally, comparisons across Erdős–Rényi, small-world, scale-free, and random-
geometric graph ensembles show that
topology changes the forgetting–adaptation balance. These results position differentiable resistor networks as compact, physically interpretable testbeds for studying continual learning in tunable matter.
Read the full abstract ▾
Atmospheric and Oceanic Physics
Prediction and Predictability of the Wet-Season Rainfall over Southeast India
Harini S, Devabrat Sharma, Yogenraj Patil et al. (+4 more)
The challenge in predicting sub-regional
climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST
climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.
Read the full abstract ▾
All authors Harini S, Devabrat Sharma, Yogenraj Patil, Gaurav Chopra, Shruti Tandon, B. N. Goswami, R. I. Sujith