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Physics Physics

physics Watch Physics

Friday 9 January

Fluid Dynamics

On the Reynolds analogy for high-speed rough-wall flows: implications for wall modelling

Michele Cogo, Davide Depieri, Matteo Bernardini et al. (+1 more)

We study the validity of the generalized Reynolds analogy (GRA) in compressible turbulent boundary layers over prism-shaped roughness by mining direct numerical simulation data of Mach 2 and Mach 4 compressible turbulent boundary layers with adiabatic and cooled surfaces. Although the direct influence of roughness strongly disrupts the near-wall coupling between momentum and energy, we show that this breakdown is confined to the roughness sublayer. Above this layer, the enthalpy and velocity fields recover a smooth-wall-like similarity, and the GRA becomes asymptotically valid by naturally accounting for roughness-enhanced wall shear stress and heat flux. Building on these results, we propose a GRA-based wall model for predicting heat transfer over rough surfaces, which is coupled with a drag-predictive physics-based method developed for prism-shaped roughness by means of compressibility transformations.
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All authors Michele Cogo, Davide Depieri, Matteo Bernardini, Francesco Picano

Chemical Physics

Stray Field NMR: a powerful method to measure dynamics at the millisecond scale

Lafon Suzanne, Vedel Jeanne, Teynier Clara et al. (+4 more)

Transport properties in fluids and confined systems play a central role across a wide range of natural and technological contexts, from geology and environmental sciences to biology, energy storage, and membrane-based separation processes. Nuclear Magnetic Resonance (NMR) provides a unique, non-destructive means to probe these properties through species-selective measurements of self-diffusion coefficients. While pulsed field gradient NMR (PFG-NMR) is routinely used, its access to diffusion times is typically limited to values no shorter than about 10 ms, restricting its applicability to systems with fast dynamics and long relaxation times. Diffusion NMR in a permanent magnetic field gradient (STRAFI) offers a complementary, multiscale approach, enabling diffusion measurements over an extended temporal window, from a few hundred microseconds to several tens of seconds. Despite its strong potential, this technique remains rarely implemented due to experimental and methodological challenges. In this work, we present a robust and versatile STRAFI-based methodology, including a specifically designed experimental setup, optimized pulse sequences, and rigorous data analysis, allowing accurate extraction of self-diffusion coefficients for a broad range of nuclei. The capabilities of the approach are illustrated through diverse applications, including the study of concentrated electrolytes using "NMR-exotic" nuclei ($^{35}$Cl, $^{79}$Br/$^{81}$Br, $^{127}$I, $^{17}$O) and the characterization of micrometre-scale porosity in membranes.
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All authors Lafon Suzanne, Vedel Jeanne, Teynier Clara, Raj Mithalal Divyen, Wzietek Pawel, Zeghal Mehdi, Judeinstein Patrick

Accelerator Physics · Cross-listed from nucl-ex

Frascati 22 GeV Workshop Summary

A. Accardi, P. Achenbach, A. Afanasev et al. (+103 more)

This document summarizes the outcomes of the "Science at the Luminosity Frontier: Jefferson Lab at 22 GeV" workshop, held at the INFN Laboratori Nazionali di Frascati in December 2024. The primary goal of the workshop was to critically assess and refine the scientific case for a proposed energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) to 22 GeV. This document intends to capture the progress on developing the scientific case since the publication of a lengthy "White Paper" in summer 2024 signed by about 450 authors.
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All authors A. Accardi, P. Achenbach, A. Afanasev, M. Albrecht, A.C. Alvaro, J. Arrington, H. Avakian, P. Barry, A. Bashir, M. Bashkanov, M. Battaglieri, S.A. Bogacz, M. Boglione, M. Bondi, V.D. Burkert, C.E. Carlson, D.S. Carman, A. Celentano, M. Cerutti, J.-P. Chen, I. Cloet, M. Contalbrigo, A. D'Angelo, L. Darme, E. De Sanctis, R. De Vita, D.J. Dean, F. Delcarro, O. Denisov, A. Deur, P. Di Nezza, C. Dilks, S. Dobbs, R. Dupre, D. Dutta, A. El Alaoui, L. El Fassi, L. Elouadrhiri, A. Filippi, N. Fomin, L. Gan, D. Gaskell, D.I. Glazier, S. Gonzalez-Solis, R.W. Gothe, F.-K. Guo, N. Hammoud, T.B. Hayward, G.M. Huber, N. Husken, N. Kalantarians, W.Y. Ke, C. Keppel, A. Kotzinian, C.H. Leung, S. Li, A. Lung, B. McKinnon, T. Mineeva, M. Mirazita, V.I. Mokeev, G. Montana, R. Montgomery, P. Moran, C. Munoz Camacho, S. Nakamura, G. Niculescu, I. Niculescu, M. Nycz, A. Osmond, M. Ouillon, R. Paremuzyan, B. Parsamyan, L. Pentchev, R. Perrino, A. Pilloni, L. Polizzi, J.W. Qiu, M. Radici, K. Raya, M. Rinaldi, C.D. Roberts, L. Rossi, P. Rossi, N. Santiesteban, M. Sargsian, N. Sato, S. Schadmand, M.R. Shepherd, K. Shirotori, A. Simonelli, M. Spreafico, J.R. Stevens, A. Szczepaniak, H. Szumila-Vance, A. Tadepalli, Y. Tian, A. Tyler, S. Vallarino, T. Vittorini, C. Weiss, D. Winney, Z. Ye, T. Yushkevych, M. Zaccheddu, Y. Zhou

Physics Education

A computational and pedagogical framework for projectile motion using Python visualizations

Leonardi Hernández Sánchez, Francisco Soto Eguibar, Irán Ramos Prieto et al. (+1 more)

Projectile motion is one of the most fundamental problems in introductory physics, offering a clear context to connect algebraic reasoning with conceptual understanding. This work presents a computational and pedagogical framework that combines the analytical formulation of projectile motion with interactive visualizations developed in Python. Using reproducible simulations, the dependence of the maximum height and horizontal range on the launch parameters $(v_0,\theta)$ is examined through trajectory plots, parameter-space maps, and iso-curves. These visual representations reveal non-trivial combinations of initial conditions that yield equivalent outcomes, reinforcing physical intuition and providing an accessible open-source tool for teaching and learning classical mechanics.
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All authors Leonardi Hernández Sánchez, Francisco Soto Eguibar, Irán Ramos Prieto, Héctor Manuel Moya Cessa

Applied Physics · Cross-listed from cond-mat.soft

Reservoir computing from collective dynamics of active colloidal oscillators

Veit-Lorenz Heuthe, Lukas Seemann, Samuel Tovey et al. (+1 more)

Physical reservoir computing is a computational framework that offers an energy- and computation-efficient alternative to conventional training of neural networks. In reservoir computing, input signals are mapped into the high-dimensional dynamics of a nonlinear system, and only a simple readout layer is trained. In most physical implementations, the interactions that give rise to the dynamics cannot be tuned directly and high dimensionality is typically achieved through time-multiplexing, which can limit flexibility and efficiency. Here we introduce a reservoir composed of hundreds of hydrodynamically coupled active colloidal oscillators forming a fully parallel physical reservoir and whose coupling strength and fading-memory time can be tuned in situ. The collective dynamics of the active oscillators allow accurate predictions of chaotic time series from single reservoir readouts without time-multiplexing. We further demonstrate real-time detection of subtle hidden anomalies that preserve all instantaneous statistical properties of the signal yet disrupt its underlying temporal correlations. These results establish interacting active colloids as a reconfigurable platform for physical computation and edge-integrated intelligent sensing for model-free detection of irregularities in complex time signals.
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All authors Veit-Lorenz Heuthe, Lukas Seemann, Samuel Tovey, Clemens Bechinger

Biological Physics

Inferring three-body interactions in cell migration dynamics

Agathe Jouneau, Tom Brandstätter, Bram Hoogland et al. (+2 more)

In active matter and living matter, such as clusters of migrating cells, collective dynamics emerges from the underlying interactions. A common assumption of theoretical descriptions of collective cell migration is that these interactions are pairwise additive. It remains unclear, however, if the dynamics of groups of cells is solely determined by pairwise interactions, or if higher-order interaction terms come into play. To investigate this question, we use time-lapse microscopy to record the dynamics of three cells interacting together in a linear three-site geometry. We collect a large number of cellular trajectories and develop an inference scheme to infer both pairwise and potential three-body cell-cell interactions. Our results reveal evidence of three-body interactions in one of the two cell lines tested. However, these three-body interactions only introduce minor corrections to the overall dynamics. Our work provides a methodology to infer the existence of three-body interactions from trajectory data, and supports the commonly assumed pairwise nature of cell-cell interactions.
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All authors Agathe Jouneau, Tom Brandstätter, Bram Hoogland, Joachim O. Rädler, Chase P. Broedersz

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

Heterogeneous ice nucleation on model substrates

Miguel Camarillo, Javier Oller-Iscar, María M. Conde et al. (+2 more)

Ice nucleation is greatly important in areas as diverse as climate change, cryobiology, geology or food industry. Predicting the ability of a substrate to induce the nucleation of ice from supercooled water is a difficult problem. Here, we use molecular simulations to analyse how the ice nucleating ability is affected by the substrate lattice structure and orientation. We focus on different model lattices: simple cubic, body centred cubic and face centred cubic, and assess their ability to induce ice nucleation by calculating nucleation rates. Several orientations are studied for the case of the face centred cubic lattice. Curiously, a hexagonal symmetry does not guarantee a better ice nucleating ability. By comparing the body centred cubic and the cubic lattices we determined that there is a significant role of the underlying crystal plane(s) on ice nucleation. The structure of the liquid layer adjacent to the substrate reveals that more efficient nucleants induce a more structured liquid. The most efficient substrates present a strong sensitivity of their ice nucleating ability to the lattice parameters. Introducing a novel methodological approach, we use Classical Nucleation Theory to estimate the contact angle of the ice nucleus on the studied substrates from the calculated nucleation rates. The method also provides the nucleation free energy barrier height, the kinetic pre-factor and the critical cluster size. The latter is in agreement with the nucleus size obtained through a microscopic analysis of the nucleation trajectories, which supports the validity of Classical Nucleation Theory down to small critical clusters.
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All authors Miguel Camarillo, Javier Oller-Iscar, María M. Conde, Jorge Ramírez, Eduardo Sanz

Optics

Spectral Tailoring of Inhomogeneous Optical Response Using Two-Dimensional Coherent Spectroscopy

Pradeep Kumar and Rohan Singh

Controlling the coherent optical response of inhomogeneous ensembles is a key challenge in advancing light-matter interaction engineering. We present a comparative study of two spectral tailoring approaches using two-dimensional coherent spectroscopy (2DCS): the prepulse and double-pulse (DP) methods. In the prepulse scheme, a high-intensity pulse induces Rabi oscillations, modulating the 2D spectral amplitude and lineshape when its spectral bandwidth matches the ensemble full width at half maximum (FWHM). To overcome this limitation, the DP method employs variable inter-pulse delay to generate predetermined periodic spectral modulation without bandwidth constraints. Moreover, tuning the relative phase between DP pulses allows selective switching of frequency components, enabling controlled enhancement or suppression of distinct spectral features. These observations highlight that, while the prepulse approach is constrained by spectral bandwidth, the DP method provides a more versatile and reliable route to manipulate the coherent optical response of inhomogeneous ensembles. We are hoping these findings might stimulate further research in optical switching and coherent storage for quantum memory devices using inhomogeneous ensembles.
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Plasma Physics · Cross-listed from astro-ph.SR

Energy Bounds from Relative Magnetic Helicity in Spherical Shells

Anthony R. Yeates and Gunnar Hornig

Relative magnetic helicity is commonly used in solar physics to avoid the well known gauge ambiguity of standard magnetic helicity in magnetically open domains. But its physical interpretation is difficult owing to the invocation of a reference field. For the specific case of spherical shell domains (with potential reference field), relative helicity may be written intrinsically in terms of the magnetic field alone, without the need to calculate the reference field or its vector potential. We use this intrinsic expression to prove that non-zero relative helicity implies lower bounds for both magnetic energy and free magnetic energy, generalizing the important Arnol'd inequality known for closed-field magnetic helicity. Further, we derive a stronger energy bound by spatially decomposing the relative helicity over a magnetic partition of the domain to obtain a new ideal invariant which we call unsigned helicity. The bounds are illustrated with analytical linear force-free fields (that maximize relative helicity for given boundary conditions) as well as a non-potential data-driven model of the solar corona. These bounds confirm that both relative helicity and the unsigned helicity can influence the dynamics in the solar corona.
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Plasma Physics

Kineclinic magnetogenesis in relativistic collisionless plasmas

Modhuchandra Laishram, Suresh Basnet and Young Dae Yoon

The relativistic momentum equation of a collisionless plasma is reformulated to describe the time evolution of canonical vorticity. Compared to the non-relativistic counterpart, an additional source term for canonical vorticity is identified, which embodies the misalignment between the fluid momentum and fluid velocity gradients. This kineclinic term breaks the frozen-in condition of canonical vorticity, thereby enabling generation or dissipation of magnetic fields and vorticity. We verify the role of this effect through particle-in-cell simulations of a modified Beltrami flow. Kineclinicity should be finite for all relativistic plasma systems due to the general lack of a functional relationship between fluid momentum and fluid velocity.
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Medical Physics

Inclusion of Inter-crystal Scattering in PET: Analytical Models and Dedicated Reconstruction

Jorge Roser, Hong Phuc Vo, Rebecca Kantorek et al. (+2 more)

Inter-crystal scattering (ICS) in Positron Emission Tomography (PET) is commonly regarded as a degradation effect that might compromise the image spatial resolution. In parallel, the inclusion of ICS events has also been recognized as a potential approach to increase PET sensitivity, which could be especially beneficial in scenarios where the latter is a limiting factor, such as very small animal imaging. Several methods for the recovery of ICS events have been proposed, many of which aim to locate the first interaction, i.e., the Compton scattering site, usually limited by their success rate, computational burden or data and training dependency. Conversely, this work proposes a physics-based model for ICS events, leading to analytical expressions of the sensitivity image and the system matrix (required by statistical reconstruction algorithms), without the need to identify the original line of response. After validating the model, the work shows how ICS events can be integrated into a joint image reconstruction algorithm (based on list-mode MLEM) together with conventional PET events, for which dedicated analytical models are also developed. To assess the performance of the proposed approach, Monte-Carlo simulated and experimental data of an image quality phantom were obtained with the MERMAID small-fish PET scanner prototype. Both simulation and experimental results indicate that, while slightly decreasing the recovery coefficient values, the inclusion of ICS clearly reduces statistical noise and improves uniformity.
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All authors Jorge Roser, Hong Phuc Vo, Rebecca Kantorek, Steven Seeger, Magdalena Rafecas

Plasma Physics

Conservative formulation of the drift-reduced fluid plasma model

Brenno De Lucca, Paolo Ricci, Micol Bassanini et al. (+2 more)

A conservative formulation of the drift-reduced fluid plasma model is constructed by analytically inverting the implicit relation defining the polarisation velocity as a function of the time-derivative of the electric field. The obtained model satisfies exact conservation laws for energy, mass, charge and momentum, in arbitrary magnetic geometry, also when electromagnetic fluctuations are included.
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All authors Brenno De Lucca, Paolo Ricci, Micol Bassanini, Sergio García Herreros, Zeno Tecchiolli

Fluid Dynamics

Development and Experimental Validation of Novel Evaluation Criteria for Turbulent Two-Phase VOF Simulations in High-Pressure Die Casting

Mehran Shazedeh, Fabian Teichmann and Sebastian Müller

Air entrapment during mold filling critically affects porosity and overall casting quality in High Pressure Die Casting. This study assesses the feasibility of applying the vof method within OpenFOAM to simulate compressible, turbulent mold filling in a thin-walled geometry. Three-dimensional simulations with the "compressibleInterFoam" solver were carried out under ambient initial cavity conditions, using both laminar flow and the k-e turbulence model. The free surface dynamics were examined across a range of inlet velocities to evaluate their influence on interface morphology, cavity pressurization, and gas entrapment. To quantify these effects, three evaluation criteria were introduced: the TIFSA as a measure of oxidation risk, the TMVF as an indicator of filling continuity and air entrapment, and the TIVF as a proxy for surface loading. Results show that turbulence modeling accelerates pressurization and limits the persistence of entrapped gas, with velocity governing the balance between smooth filling, turbulent breakup, and exposure duration. Comparison with experimental casting trials, including CT based porosity analysis and photogrammetric surface evaluation, validated that the model captures key defect mechanisms and provides quantitative guidance for process optimization.
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Fluid Dynamics

Inferring the Turbulent Breakup of Colloidal Aggregates Using Graph Neural Networks

Michele Buzzicotti, Massimo Cencini, Giulio Cimini et al. (+2 more)

Solid aggregates in turbulent suspensions may break under the action of shear stresses. We explore the use of Graph Neural Networks (GNN) to infer aggregate fragmentation once the aggregate structure and flow velocity gradients are known. We consider two models: the first GNN is a classifier, trained to distinguish aggregates that break from those that do not; the second GNN is a regression model, trained to predict the maximal tensile force within each aggregate in a given flow condition. We show that both models complete their task with a high statistical accuracy, also generalizing to aggregates of different sizes, and generally performing better than the statistical prediction based on mean field quantities. This work paves the way for future use of GNN to quantify aggregate breakup in a large population of aggregates suspended in complex flow configurations, as it takes place in the wet production of fine powders and in the transport of sediments in environmental flows.
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All authors Michele Buzzicotti, Massimo Cencini, Giulio Cimini, Marco Vanni, Alessandra S. Lanotte

Applied Physics

A Novel Stability Improvement Method of S-Band Magnetron Systems Based on Its Anode Current Feature

S Wang, Y Zhao, X Chen et al. (+1 more)

Magnetrons are widely used as a high-efficiency, large-power, and low-cost microwave source. This article proposes a method to improve the magnetron output characteristics based on regulating its anode current by a novel active distortion eliminator. It takes advantage of the constant current state of MOSFETs to suppress the anode current ripple and eliminate the magnetron output ripples. An improved switch-mode power supply is applied to a 1-kW commercial S -band magnetron in the experiment. The peak-to-peak anode current ripple is reduced from 31 to 7 mA. The spectral width of the free-running magnetron is decreased from 246 to 87 kHz. In the injection locking state, the spur noise is significantly reduced with a maximum suppression strength of 23 dB at a 100-Hz offset. The peak-to-peak value of the magnetron magnitude and phase jitter are suppressed to 15% and 16% of their original values at a power injection ratio of 0.13. This method provides a novel approach for stabilizing the output phase of an injection-locked magnetron, making it promising for applications in phased arrays, wireless power transfer, particle accelerators, large-power communication, and other scenarios that require high phase stability.
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All authors S Wang, Y Zhao, X Chen, C Liu

Optics · Cross-listed from cs.IT

Nonlinearity Mitigation for Coherent Ground-to-Satellite Optical Links

Stella Civelli, Marco Secondini and Luca Potì

We propose digital signal processing techniques for nonlinearity mitigation in high power optical amplifiers used in satellite communications. The acceptable link loss increases by 6dB with negligible complexity.

Optics

Quantum Dynamics of Enantiomers in Chiral Optical Cavities

Yang-Cheng Ye, Panpan Zhang, Ajay Jha et al. (+2 more)

Chirality, the absence of mirror symmetry, is a fundamental molecular property with far-reaching consequences from chemistry to biology. Yet enantiosensitive optical responses are very weak. Here, we introduce a theoretical framework in which a chiral optical cavity under strong coupling directly lifts the degeneracy of opposite enantiomers at the electronic-dipole level. The cavity's parity-breaking field inside the cavity induces distinct site-energy shifts for left- versus right-handed molecules, producing robust enantioselective polariton states that overcome the weakness of traditional chiroptical effects. Using cavity quantum electrodynamics simulations, we show that strong light-matter coupling reshapes the polaritonic energy landscape and leads to enantiomer-specific coherence lifetimes and relaxation pathways. To reveal these dynamics, we propose ultrafast two-dimensional electronic spectroscopy (2DES) as a probe, capable of resolving polaritonic splittings on femtosecond timescales. Simulated 2DES spectra exhibit unambiguous enantioselective signatures of the cavity-induced asymmetry. These findings establish that chiral cavities provide a powerful platform for detecting and controlling molecular handedness beyond the limits of conventional optical methods.
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All authors Yang-Cheng Ye, Panpan Zhang, Ajay Jha, Fulu Zheng, Hong-Guang Duan

Atmospheric and Oceanic Physics

Coupling between sub-mesoscale eddies, internal waves, and turbulence in the deep Mediterranean: A spectral investigation

Hans van Haren

Interaction between energy-abundant (sub-)mesoscale eddies and internal waves can lead to turbulence generation and may prove important for replenishment of nutrients for deep-sea life and circulation. However, observational evidence of such interaction is scarce and precise energy transfer is unknown. In this paper, an extensive spectral study is reported using mooring data from nearly 3000 high-resolution temperature sensors in about half-a-cubic hectometer of seawater above a deep flat Northwestern-Mediterranean seafloor. The number of independent data records partially improves statistics for better determination of spectral slopes, which however do not show a roll-off to the viscous dissipation range of turbulence. The spectra hardly show power-laws omega^p having exponent p = -5/3 representing an inertial subrange that evidences shear-induced isotropic turbulence. Instead, they are dominated by p = -7/5 representing a buoyancy subrange, which evidences convection-induced anisotropic turbulence. In contrast with p=-5/3 that indicates a downgradient cascade of energy, p=-7/5 characterizes by an ambiguous cascade direction. At height h<50 m above seafloor, p=-7/5 is found adjacent to instrumental noise. The p=-7/5 is also found in the sub-mesoscale/internal wave band that is elevated in variance by one order of magnitude. It is reasoned that this sub-inertial range cannot represent isotropic motions, hence p .ne. -5/3 at all heights, and a new deep-sea energy cascade is proposed between mesoscales and turbulence dissipation. Only higher up in more stratified waters an inertial subrange is formed. The transition from internal waves into large-scale turbulence follows p = -2, while a higher-frequency transition from 0 to pi phase change reflects overturns of slanted convection or standing-wave breaking leading to isotropic turbulence.
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Biological Physics · Cross-listed from cond-mat.soft

Molecular signatures of pressure-induced phase transitions in a lipid bilayer

Yanna Gautier, Guillaume Stirnemann and Jérôme Hénin

Understanding how lipid bilayers respond to pressure is essential for interpreting the coupling between membrane proteins and their native environments. Here, we use all-atom molecular dynamics to examine the pressure-temperature behavior of model membranes composed of DMPC or $\Delta$9-cis-PC. Within the studied range (288-308 K, 1-2000 bar), DMPC undergoes a liquid–gel transition, while $\Delta$9-cis-PC remains fluid due to unsaturation. The CHARMM36 force field reproduces experimental boundaries with high fidelity: simulated DMPC transitions deviate by only 5-10 K and 100-300 bar, and $\Delta$9-cis-PC exhibits no transition. Hysteresis is modest but most pronounced when starting from low-temperature gels. We identify area per lipid, bilayer thickness, and acyl-chain gauche fraction as sensitive phase markers; among these, the gauche fraction provides the most robust signature. Simulations indicate an interdigitated gel is the equilibrium structure under finite-size conditions. However, at low temperature and high pressure, interdigitation decreases, consistent with the experimental lamellar gel phase. This long-lived interdigitation critically impacts standard order parameters, specifically area per lipid and membrane thickness. These results underscore the accuracy of modern force fields and highlight how simulations mechanistically complement experimental studies of pressure-regulated membranes.
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Atmospheric and Oceanic Physics

ORB: An Open Radio Buoy for Coastal Water Measurements

Lars Willas Dreyer, Andrea Pferscher, Riccardo Sieve et al. (+4 more)

Oceanographic instrumentation technology is currently under rapid transition towards increasingly open-source technology. Open-source buoys compete with commercial and closed-source buoys both in price, functionality and availability. Long-range radio (LoRa) is a communication technology which is inexpensive both in terms of data transfer cost and power without the need for pre-existing infrastructure. In this paper, we present ORB, an open-source drifter buoy using LoRa for coastal water measurements. ORB is designed to be reliable, low-cost, modifiable and power efficient. We present validation experiments demonstrating that ORB can achieve a radio telemetry range of five kilometers, and has an expected battery lifetime of up to seven months, owing largely to a the radio telemetry which operates at the low power consumption of 7 mA. Finally, we discuss the role and contribution of ORB in the space of open-source instrumentation and ocean modeling.
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All authors Lars Willas Dreyer, Andrea Pferscher, Riccardo Sieve, Jean Rabault, Atle Jensen, Einar Broch Johnsen, Gaute Hope

Applied Physics

Mobile Robot Localization Using a Novel Whisker-Like Sensor

Prasanna K. Routray, Basak Sakcak, Steven M. LaValle et al. (+1 more)

Whisker-like touch sensors offer unique advantages for short-range perception in environments where visual and long-range sensing are unreliable, such as confined, cluttered, or low-visibility settings. This paper presents a framework for estimating contact points and robot localization in a known planar environment using a single whisker sensor. We develop a family of virtual sensor models. Each model maps robot configurations to sensor observations and enables structured reasoning through the concept of preimages - the set of robot states consistent with a given observation. The notion of virtual sensor models serves as an abstraction to reason about state uncertainty without dependence on physical implementation. By combining sensor observations with a motion model, we estimate the contact point. Iterative estimation then enables reconstruction of obstacle boundaries. Furthermore, intersecting states inferred from current observations with forward-projected states from previous steps allow accurate robot localization without relying on vision or external systems. The framework supports both deterministic and possibilistic formulations and is validated through simulation and physical experiments using a low-cost, 3D printed, Hall-effect-based whisker sensor. Results demonstrate accurate contact estimation and localization with errors under 7 mm, demonstrating the potential of whisker-based sensing as a lightweight, adaptable complement to vision-based navigation.
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All authors Prasanna K. Routray, Basak Sakcak, Steven M. LaValle, Manivannan M

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

Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

Hai Man, Chaobo Wang, Jia-Rui Li et al. (+2 more)

Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.
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All authors Hai Man, Chaobo Wang, Jia-Rui Li, Yuping Tian, Shu-Gang Chen

Optics

Sub-cycle time-refraction at optical frequencies

Ohad Segal, Noa Konforty, Oded Schiller et al. (+4 more)

Large and abrupt variations in the electromagnetic properties of materials lead to dramatic effects: even a single step-like change in the refractive index induces striking phenomena, such as time-refraction and time-reflection. When the refractive index varies periodically in time, multiple time-refractions and -reflections interfere, giving rise to photonic time-crystals (PTCs). Importantly, PTCs display momentum bands separated by gaps in which the modes experience exponential amplification, drawing energy from the modulation in a non-resonant fashion. Ordinary nonlinear optics does not operate in this regime: the material response is either very weak or very slow. One of the immediate consequences is that time-reflection of light at optical frequencies has never been observed in experiments. Here, we experimentally realize an order-unity change in the refractive index occurring at sub-cycle rates, and explore the phenomena emerging from it. By varying the duration of the index change from extending over many cycles to being significantly below a single cycle, we observe that the frequency shift of the time-refraction is enhanced as the index variation occurs faster. Our experiment is the gateway for realizing sharp time-interfaces at optical frequencies, which are the key for experimenting with time-reflection, PTCs and new phenomena expected from light-matter interactions in time-varying media.
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All authors Ohad Segal, Noa Konforty, Oded Schiller, Maxwell J. Tolchin, Jon-Paul Maria, Yonatan Plotnik, Mordechai Segev

Atomic Physics

Phase-Controlled Ramsey Interference of XUV Photoelectrons

Neha Kukreti and Amol R Holkundkar

We investigate Ramsey-type quantum interference in photoelectron momentum distributions generated by two time-delayed, linearly polarized extreme-ultraviolet (XUV) laser pulses. The electron dynamics are studied by solving the full-dimensional time-dependent Schrödinger equation within the single-active-electron approximation for neon initially prepared in a current-carrying $2p_+$ state. The coherent superposition of electron wave packets released by the two pulses gives rise to pronounced interference fringes in both energy-resolved spectra and angle-resolved momentum distributions. We demonstrate that the fringe positions are governed by a Ramsey phase accumulated during the interpulse delay, resulting in a linear dependence on the relative carrier-envelope phase and an inverse scaling of the fringe spacing with the delay time. By systematically varying the laser intensity, we establish that the observed modulations originate from temporal quantum interference rather than Autler–Townes splitting. Analysis of the time-resolved bound-state population dynamics reveals that carrier-envelope-phase dependent bound–bound coupling dominated by transient population transfer to the $2s$ state, which controls the interference contrast. The accumulated phase is further interpreted in terms of a dynamic Stark shift of the dressed bound states, which is quantitatively reproduced using a reduced two-level model.
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Accelerator Physics

A Realistic Proportional-Integral RF Feedback Model for Longitudinal Beam Dynamics Simulation

Tianlong He, Wenshu Liang, Jincheng Xiao et al. (+1 more)

Modern fourth-generation storage ring light sources predominantly utilize digital I/Q-based proportional-integral (PI) feedback for their radio-frequency (RF) systems. This paper introduces a dedicated PI feedback model implemented in the STABLE tracking code to enable accurate and fast longitudinal beam dynamics simulations. The model's key innovation lies in its treatment of the continuous generator current, which is discretized into electron-bunch-like charge pulses, while the cavity voltage is refreshed on an RF-cycle basis. This methodology offers a more physically accurate model of the beam-cavity-feedback coupling, providing a versatile tool for precise longitudinal beam dynamics studies in single- and multi-rf configurations.
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All authors Tianlong He, Wenshu Liang, Jincheng Xiao, Xin Huang

Data Analysis and Statistics · Cross-listed from cond-mat.mtrl-sci

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs

Kamyar Barakati, Haochen Zhu, C Charlotte Buchanan et al. (+3 more)

Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of characterization and by the difficulty of extracting actionable structure-property relations from complex characterization data. Here we develop an autonomous scanning probe microscopy (SPM) framework that integrates automated atomic force and magnetic force microscopy (AFM/MFM) to rapidly explore magnetic and structural properties across combinatorial spread libraries. To enable automated exploration of systems without a clear optimization target, we introduce a combination of a static physics-informed bag-of-features (BoF) representation of measured surface morphology and magnetic structure with multi-objective Bayesian optimization (MOBO) to discover the relative significance and robustness of features. The resulting closed-loop workflow selectively samples the compositional gradient and reconstructs feature landscapes consistent with dense grid "ground truth" measurements. The resulting Pareto structure reveals where multiple nanoscale objectives are simultaneously optimized, where trade-offs between roughness, coherence, and magnetic contrast are unavoidable, and how families of compositions cluster into distinct functional regimes, thereby turning multi-feature imaging data into interpretable maps of competing structure-property trends. While demonstrated for Au-Co-Ni and AFM/MFM, the approach is general and can be extended to other combinatorial systems, imaging modalities, and feature sets, illustrating how feature-based MOBO and autonomous SPM can transform microscopy images from static data products into active feedback for real-time, multi-objective materials discovery.
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All authors Kamyar Barakati, Haochen Zhu, C Charlotte Buchanan, Dustin A Gilbert, Philip Rack, Sergei V. Kalinin

Computational Physics · Cross-listed from cs.AI

Explainable AI: Learning from the Learners

Ricardo Vinuesa, Steven L. Brunton and Gianmarco Mengaldo

Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that explainable artificial intelligence (XAI), combined with causal reasoning, enables learning from the learners. Focusing on discovery, optimization and certification, we show how the combination of foundation models and explainability methods allows the extraction of causal mechanisms, guides robust design and control, and supports trust and accountability in high-stakes applications. We discuss challenges in faithfulness, generalization and usability of explanations, and propose XAI as a unifying framework for human-AI collaboration in science and engineering.
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Fluid Dynamics

A Favre-Averaging Shallow Water Framework for Aerated Flows with Friction Factor Decomposition

Matthias Kramer

Accurate prediction of flow resistance in high-Froude-number aerated flows remains challenging due to air entrainment, which causes strong spatial variability in mixture density. Here, we introduce for the first time a density-weighted (Favre) averaging approach within a Shallow Water Equation framework specifically tailored to account for this strong mixture density variability. Within this framework, we present a novel Darcy-Weisbach friction factor formulation that decomposes contributions associated with uniform flow, spatially varying flow, and temporally evolving flow, and incorporates momentum and pressure correction factors reflecting the vertical structure of the mixture. Application to experimental data demonstrates that spatial flow development systematically reduces the effective friction factor relative to the uniform-flow estimate, and that momentum-based and energy-based formulations yield nearly identical results. The framework recovers classical uniform-flow predictions in the quasi-uniform downstream region and reduces to standard single-phase formulations in the absence of aeration. Overall, it provides a physically consistent tool for resistance prediction in high-Froude-number spillways, chutes, and open-channel systems, with a structure compatible with depth-averaged numerical solvers.
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Physics and Society · Cross-listed from cs.MA

How Exploration Breaks Cooperation in Shared-Policy Multi-Agent Reinforcement Learning

Yi-Ning Weng and Hsuan-Wei Lee

Multi-agent reinforcement learning in dynamic social dilemmas commonly relies on parameter sharing to enable scalability. We show that in shared-policy Deep Q-Network learning, standard exploration can induce a robust and systematic collapse of cooperation even in environments where fully cooperative equilibria are stable and payoff dominant. Through controlled experiments, we demonstrate that shared DQN converges to stable but persistently low-cooperation regimes. This collapse is not caused by reward misalignment, noise, or insufficient training, but by a representational failure arising from partial observability combined with parameter coupling across heterogeneous agent states. Exploration-driven updates bias the shared representation toward locally dominant defection responses, which then propagate across agents and suppress cooperative learning. We confirm that the failure persists across network sizes, exploration schedules, and payoff structures, and disappears when parameter sharing is removed or when agents maintain independent representations. These results identify a fundamental failure mode of shared-policy MARL and establish structural conditions under which scalable learning architectures can systematically undermine cooperation. Our findings provide concrete guidance for the design of multi-agent learning systems in social and economic environments where collective behavior is critical.
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Plasma Physics

Similarity Theory and Scaling Networks for Electromagnetic Wave-Driven Plasmas

Hanyang Li, Yulia Sharova, Denis Eremin et al. (+1 more)

We demonstrate the scale-invariant behavior of electromagnetic wave-driven radio-frequency plasmas across different dimensional scales. Using two-dimensional electromagnetic particle-in-cell simulations, we show that plasma uniformity remains the same in similar discharges. Building on the concept of similarity laws, we develop scaling networks that effectively relate plasma parameters across varying operating conditions. These results establish a generalized similarity theory derived from the Boltzmann equation coupled with the full set of Maxwell equations, extending the theoretical framework of similarity laws into electromagnetic regimes.
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All authors Hanyang Li, Yulia Sharova, Denis Eremin, Yangyang Fu