General Physics
Comment on "Quantum mechanics based on real numbers: a consistent description"
S C Tiwari
In this comment on arXiv:2503.17307 (PRL, 136, 240202, 2026), a novel idea using flag states to transform complex state vector and operators to real ones is argued to effectively lead to a
quantum mechanics that is complex in the disguised form of real. The claim that real-valued
quantum mechanics cannot be falsified is conceptually flawed. A new criterion based on the
geometric phases is suggested to compare real and complex versions of
quantum mechanics.
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Applied Physics
Dual mass milligram-scale torsion oscillator for vibration-free optomechanical sensing
Jack Manley, Thomas Bsaibes, Charles A. Condos et al. (+3 more)
Chip-scale optomechanical devices are driving the miniaturization of inertial sensors and next generation fundamental physics experiments. However, precision at the theoretical limit is often unattainable due to extraneous vibrations. One solution is tailoring the device to isolate a degree of freedom from the environment while maintaining coupling to the signal of interest. To this end, we introduce a dual milligram-mass torsion oscillator, formed by mass loading a strained silicon nitride nanoribbon. The antisymmetric torsion mode suppresses vibrations by over an order of magnitude to achieve a thermally limited torque sensitivity of $10^{-18}$ Nm/$\sqrt{\rm Hz}$ while maintaining ultralow loss. We demonstrate the sensing ability by detecting an optical radiation pressure torque of $10^{-16}$ Nm over a 30 Hz bandwidth. We also characterize the device for frequency-based gravimetry, demonstrating $10^{-6}g_0$ ($g_0=9.8$ $\rm m s^{-2}$) precision in 30 seconds with an oscillation amplitude of only 100 $\mu$rad. This device demonstrates a technique for overcoming vibration noise, with broad implications for optomechanical sensing from commercial applications to fundamental physics experiments.
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All authors Jack Manley, Thomas Bsaibes, Charles A. Condos, William A. Terrano, Dalziel J. Wilson, Jon R. Pratt
Optics
Can Second-Order Nonlinearity in Metal-Dielectric Metamaterials Pave a Way Toward Elusive Photonic Time Crystals?
Jacob B Khurgin
While a tremendous amount of theoretical work has been dedicated to time-varying photonics, practical implementation in the optical range has relied almost exclusively on transparent conductive
oxides , which remain severely constrained by being slow and highly lossy. To bypass these limitations, I propose an alternative platform utilizing ultrafast second-order nonlinearities within an epsilon-near-zero
semiconductor-silver metamaterial. Due to the relatively low loss in silver and polarization-selective pumping, ultra-low pump absorption prevents thermal degradation, while absorption of the probe is sufficiently low to simultaneously permit the multi-cycle interaction lengths necessary for signal detection. I find that with pump powers of 100s of GW/cm2 one can open a wide momentum bandgap of tens of percents and achieve net parametric gain in time, offering a robust pathway to realizing photonic time
crystals in optical range.
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Chemical Physics
Fanpy 2.0: Wavefunction Implementation and Analysis Tools for Flexible Ansatz Design
Krisztina Zsigmond, Rugwed Lokhande, Carlos E. V. de Moura et al. (+6 more)
Fanpy is a Python library for developing new wavefunction methods. It enables users to quickly convert mathematical expressions into working code through a modular design based on the Flexible Ansatz for N-electron Configuration Interaction (FANCI) theory. This architecture facilitates a straightforward extension of the codebase. Here we present version 2.0 of the Fanpy package. This release includes several new wavefunction implementations, including coupled- cluster-inspired geminal approaches. A new analysis module enables a more detailed inspection of computational results and lays the groundwork for future features. In addition, the PySCF interface has been redesigned, and an interface to the PyCI package has been introduced to offload computationally expensive components. Finally, we introduce an improved software development environment, including automated testing and issue tracking.
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All authors Krisztina Zsigmond, Rugwed Lokhande, Carlos E. V. de Moura, Pratiksha B. Gaikwad, Marco Martínez-González, Michelle Richer, Daniel F. Calero-Osorio, Paul W. Ayers, Ramón Alain Miranda-Quintana
Computational Physics
Self-consistent orbital-free nuclear density functional theory with a physics-constrained learned nonlocal kinetic energy functional
Fumihiro Imoto
Nonlocal kinetic-energy density functionals (KEDFs) can encode
nuclear shell structure in orbital-free density functional theory (OFDFT), but self-consistency requires accurate functional derivatives and a stable solution of the Euler equation. We construct a density-dependent-kernel KEDF whose correction is learned from Kohn-Sham (KS) reference data. Linearity in the kernel shape yields analytic Euler–Lagrange (EL) responses. The fit uses exact energy-matching equalities and soft quadratic inequality penalties for violations of prescribed tail-response and selected-path energy-rise margins, evaluated by an active-set repeated penalized least-squares iteration. We formulate the radial EL equation as the rearranged one-orbital eigenproblem. In a constant-$k_F$ $^{16}$O
benchmark it converges without density mixing and agrees with imaginary-time
evolution (ITE) to sub-keV energy. Adaptive-step ITE gives final species EL residuals of at most 0.07 MeV in the reported calculations, and rearranged diagonalization reaches the same stationary densities. For spherical $N=Z$ systems without
spin–orbit or Coulomb terms,
nucleus-specific fits for $A=16$ to $140$ reproduce shell patterns and radii. A correction trained on three
nuclei transfers the shell pattern and radius, but not the absolute energy, to a previously unseen $A=140$ system.
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Medical Physics
Evaluation of reference region modelling techniques in quantitative dynamic contrast-enhanced magnetic resonance imaging of breast cancer
Christian Notte, Zaki Ahmed and Ives R. Levesque
Purpose: To assess the validity of reference region model (RRM) techniques for quantitative perfusion analysis of low temporal resolution DCE-MRI of breast lesions. Methods: Tofts model (TM), extended Tofts model (ETM), RRM, and extended RRM (ERRM) were applied to a
dataset of images from ten patients with confirmed breast cancer collected before and after one cycle of chemotherapy, with known pathologic treatment response. Quantitative maps of estimated perfusion parameters were produced using each model, and changes in median perfusion parameters before and after a cycle of chemotherapy were compared across models. Relative parameters from reference region models were made absolute using the reference region and input function tail (RRIFT) technique. Tumour region voxel count before and after treatment was also recorded. Results: RRMs produced similar absolute quantitative analysis compared to AIF-driven models (TM and ETM), with scaling dicerences in Ktrans parameter estimates and disagreement in vp parameter estimates between ETM and ERRM. RRMs and AIF-driven models were equally capable of separating complete responders based on change in median Ktrans parameter, while change in tumour voxel count was not predictive of treatment response. Both RRMs were also able to fully separate complete responders using relative Ktrans parameter estimates, with no AIF information. Conclusions: RRMs show similar performance as standard models in quantitative analysis of breast lesions, with reduced reliance on the AIF . Results imply that RRMs may be a suitable alternative at low temporal resolutions to provide reliable, patient-specific quantitative analysis.
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Physics and Society · Cross-listed from econ.GN
Ranking-based competitive balance measures in Formula One
Dóra Gréta Petróczy and László Csató
Competitiveness in racing sports can be measured by comparing the start and finish rankings within races, as well as the start and finish rankings across races in a season. Since the importance of position changes is non-uniform and variance at the top of the ranking is more interesting than at the bottom of the ranking, we propose using weighted distances for this purpose. Therefore, two weighting schemes are applied and compared to the standard Kemeny distance to analyse the Formula One between 1950 and 2024. The
evolution of competitive balance is unexpectedly robust to the weights, but competition is more intense if one focuses on the top positions. Competitive balance has been more unfavourable in the last two decades than ever before. Statistical tests uncover three structural breaks in competitive balance that are closely related to regulatory changes, highlighting the role of decision-makers in the
evolution of competitiveness.
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Computational Physics
PI-GINOT: Data-free geometry-informed neural operator learning for finite-strain hyperelasticity on parametric DogBone specimens
Aamir Dean and Betim Bahtiri
Parametric nonlinear solid-mechanics simulations are widely used in virtual testing, optimisation, and uncertainty analysis, but repeated finite-element simulations become costly when geometry changes. This paper presents PI-GINOT, a physics-informed neural operator that predicts finite-strain hyperelastic responses across a four-parameter family of DogBone specimens without using finite-element training data. Each specimen is described by a boundary point cloud, which is encoded into geometry features. A cross-
attention decoder then predicts displacement at arbitrary points. Displacement boundary conditions are enforced exactly, while stresses are computed using automatic differentiation and a compressible Neo-Hookean plane-stress model. Training is guided by
equilibrium, traction-free and
symmetry conditions, deformation stability, and internal force consistency. Abaqus simulations are used only after training for validation. Across eight test geometries, PI-GINOT achieves displacement errors of 2.1%-7.1%, peak von Mises stress errors of 0.9%-13.3%, and section-force errors below 10.3%. Larger errors occur in individual stress components, especially for narrow specimens, mainly because of steep stress gradients near the gauge-to-fillet transition. These results show that PI-GINOT can provide useful geometry-dependent predictions for nonlinear solid mechanics without labelled simulation data, while also revealing where better local stress resolution is still needed.
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Physics and Society
Archetypometrics of 'Friends'
Shun Zhang, Tabia Tanzin Prama, Christopher M. Danforth et al. (+1 more)
Storytelling inherently revolves around characters. Using the television sitcom `Friends' as a case study, we investigate how well archetype vectors capture both individual characterization and the relational structure of a specific ensemble. Our work is based on the archetypometrics framework, which locates 2,000 fictional characters from 341 stories in a continuous space derived from 464 bipolar traits. We proceed in three stages: interpreting each character's archetypal profile against narrative evidence, projecting the ensemble onto ousiograms of the six essential dimensions, and measuring pairwise similarity with vector inner products. We show that the six characters of `Friends' occupy distinct archetypal positions that accord with their established identities, while the projections expose ensemble structure invisible in individual profiles, including the collapse of the Angel–Demon dimension, a signature of the sitcom's uniformly sympathetic cast. Based on inner products, we construct a similarity
matrix that resolves three main kinds of relational structure:
alignment (e.g., Phoebe–Joey), contrast (e.g., Phoebe–Ross), and orthogonality (e.g., Rachel–Ross and Monica–Chandler). The orthogonality of the romantic pairings affords a detailed view of relationships built on complementary rather than overlapping character traits. Overall, our case study suggests that for ensemble-based stories the archetypometric geometry is fully interpretable in narrative terms, from individual identities to the structure of the group's relationships.
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All authors Shun Zhang, Tabia Tanzin Prama, Christopher M. Danforth, Peter Sheridan Dodds
Optics
Approximate reservoir computing with a semiconductor laser for reducing energy consumption
Tatsuki Ito, Kazutaka Kanno, Satoshi Kawakami et al. (+1 more)
Photonic reservoir computing is a promising physical machine-learning approach for time-series prediction. Quantization of the reservoir response signal is an important consideration in the implementation of photonic reservoir computing, and the number of quantization bits and sampling frequency need to be optimized to achieve high prediction performance and low energy consumption. However, few studies have investigated the effects of the number of quantization bits and sampling frequency on both prediction performance and energy consumption. In this study, we introduce the concept of approximate reservoir computing with a
semiconductor laser by quantizing the reservoir node states and output weights. We numerically evaluate the performance of a chaotic time-series prediction task and estimate the energy consumption per sample using a component-level model. By optimizing the number of quantization bits, the sampling frequency, and the injection current of the
semiconductor laser, we achieve a significant reduction in the estimated energy consumption per sample while maintaining prediction performance. We also obtain model-based estimates of the throughput, energy efficiency, and energy per operation of the proposed reservoir-computing system.
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All authors Tatsuki Ito, Kazutaka Kanno, Satoshi Kawakami, Atsushi Uchida
Optics
Photonic reservoir computing with complex networks
Sion Park, Kohei Watabe, Satoshi Sunada et al. (+2 more)
Photonic reservoir computing has attracted increasing
attention as a fast and low-cost approach for time-series prediction. Photonic reservoir computing utilizes the high speed, broad bandwidth, and spatial parallelism of light. However, the effect of the internal connection structure (network
topology) on the computing performance has not been investigated for large-scale photonic reservoirs. In this study, we experimentally and numerically demonstrate photonic reservoir computing using a spatial light modulator to systematically evaluate the relationship between the network
topology and the performance of reservoir computing. We introduce complex network structures such as small-world and scale-free network
topologies of the internal nodes in the reservoir. We perform the
memory capacity measurement and the one-step-ahead prediction task of the chaotic time series to compare the performance. We found that the small-world network exhibits the maximum
memory capacity and the best prediction performance. Our numerical calculations reveal that the performance of the time-series prediction can be optimized by changing the rewiring probability of the network and the leak rate of the reservoir. We also implement photonic human
brain network as a reservoir, which is designed by the connectomes of human
brain activities. We found that the network
topology strongly affects the performance of reservoir computing, and the small-world network structure outperforms the other configurations.
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All authors Sion Park, Kohei Watabe, Satoshi Sunada, Tomoki Yamagami, Atsushi Uchida
Optics
Three-dimensional confinement of light in photonic crystals without bandgaps
Manxi Shi, Sachin Vaidya, Ali Ghorashi et al. (+2 more)
We demonstrate that confinement of light in three dimensions is possible in photonic
crystals without a complete photonic bandgap. Our approach exploits
symmetry-protected quadratic degeneracies in the bulk band structure, where the photonic density of states vanishes at an isolated frequency. By introducing a point defect, we create a localized mode whose
symmetry representation is incompatible with that of the surrounding bulk modes, suppressing coupling to propagating channels. The combination of vanishing density of states and a
symmetry mismatch yields bound defect modes despite the absence of a spectral gap, as confirmed by time- and frequency-domain numerical simulations. This approach highlights the role of engineering the photonic environment around the defect to enable confinement, potentially providing a new route for designing optical cavities in three-dimensional photonic
crystals.
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All authors Manxi Shi, Sachin Vaidya, Ali Ghorashi, Steven G. Johnson, Marin Soljačić
Computational Physics
Curl-based Electric-Field Boundary Condition for the Accurate and Stable Electromagnetic Scattering Analysis
Eduard Ubeda, Alex Heldring, Juan M. Rius et al. (+1 more)
We introduce a curl-based Electric-Field Integral Equation (Curl-EFIE) for the electromagnetic scattering analysis from perfect electric conductors. The formulation is derived by enforcing a vanishing curl on the EFIE over the boundary
manifold, achieved by testing the internal electric field with orthogonal tangent solenoidal disks. We demonstrate that a Method-of-Moments (MoM) discretization of the Curl-EFIE converges to a Galerkin-discretized MFIE as the testing disk dimensions vanish, yielding stable, breakdown-free impedance
matrices for low frequencies and dense grids. Unlike the strongly singular kernels of the MFIE, the Curl-EFIE utilizes weakly singular kernels, significantly simplifying source integral evaluations. As a first-kind integral equation, it bypasses the MFIE's Gram
matrix requirement, facilitating the analysis of non-matching triangulations. Furthermore, a linear combination of the Curl-EFIE and the conventional EFIE provides an interior-resonance-free formulation analogous to the Combined Field Integral Equation (CFIE). Finally, the Curl-EFIE performs particularly well at capturing the scattering behavior of sharp- edged and cornered geometries.
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All authors Eduard Ubeda, Alex Heldring, Juan M. Rius, Vladimir Okhmatovski
Optics
Non-volatile integrated photonics on lithium tantalate-on-insulator
Yuhang Li, Miao Deng, Xun Zhang et al. (+11 more)
Scalable reconfigurable photonic integrated
circuits require low-loss, high-speed optical control without continuous holding power. Yet widely used thermo-optic tuning and continuously biased electro-optic tuning consume static power and introduce thermal crosstalk or bias drift. Here we demonstrate a monolithic non-volatile photonics platform on lithium tantalate-on-
insulator (LTOI). In congruent x-cut lithium tantalate, the switched ferroelectric-domain configuration is retained after the write field is removed. On the same LTOI platform, we demonstrate a waveguide propagation loss of approximately 0.05-0.06 dB/cm and multilevel non-volatile phase tuning in separate devices. The programmed states remain distinguishable through $10^{6}$ write cycles. Weighted segmented electrodes resolve 137 phase positions across a $\pi$ range, corresponding to an analogue phase-setting resolution of approximately $0.007\pi$. We further combine non-volatile phase control with high-speed electro-optic modulation to achieve zero-static-power bias control of a >110 GHz modulator and a 59.3 dB extinction ratio after non-volatile trimming. At the system level, an image-edge-detection chip achieves a measured energy efficiency of 3.48 TOPS/W. These results establish LTOI as an integrated photonics platform that combines persistent optical reconfigurability with low-loss routing and high-speed electro-optic modulation.
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All authors Yuhang Li, Miao Deng, Xun Zhang, Cheng Zeng, Chijun Li, Yiqi Dai, Yuankang Huang, Siyu Lu, Zhenwu Mo, Xiao Wu, Peng Tan, Yong Zhang, Yikai Su, Jinsong Xia
Fluid Dynamics · Cross-listed from cond-mat.stat-mech
Kinetic and Hydrodynamic Theories of Chiral Intruder Dynamics in Nonequilibrium Baths
Raphaël Maire and Ignacio Pagonabarraga
We study the
chiral dynamics of an intruder immersed in a nonequilibrium bath in two complementary limits: the dilute kinetic regime and the dense hydrodynamic regime. In the dilute limit, starting from a Boltzmann-Lorentz description, we derive an effective Langevin equation whose coefficients are given explicitly by geometry-dependent boundary integrals. This formulation separates the effects of intruder-shape chirality from those of
chiral intruder-bath interactions. We find that the
chiral interactions generate an odd response and a torque, whereas the chirality of the intruder leads to a ratchet effect. We also show that fluctuation-dissipation-like relations exist and that certain
symmetry-allowed couplings vanish in the dilute regime. In the dense regime, we argue that intruder dynamics are governed primarily by bath hydrodynamics and torque-density-driven edge currents not captured by the previous framework. These currents can generate both an antisymmetric drag and a
curvature-induced torque, leading to an antisymmetric response when inertia is accounted for. Taken together, these results provide a step toward understanding the mechanisms governing the
chiral dynamics of an intruder in a nonequilibrium bath across different scales.
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Fluid Dynamics · Cross-listed from cond-mat.stat-mech
Chiral Dynamics of an Intruder across Dilute and Hydrodynamic Regimes
Raphaël Maire and Ignacio Pagonabarraga
We introduce and simulate an analytically tractable model for an intruder of arbitrary shape in a nonequilibrium bath, with chirality originating from the bath, the intruder, or their coupling. In the dilute regime, a Langevin description derived from a Boltzmann-Lorentz equation shows how intruder geometry governs ratchet effects and odd response. In the dense regime, the dynamics of the intruder are instead governed by the hydrodynamic modes of the bath and edge currents, which are described by a Stokes equation including a
chiral torque density. Our results link shape to
chiral transport and show that odd response arises from distinct mechanisms in the dilute and dense limits.
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Plasma Physics
Radiation Pressure Effect on the degenerate Electron-Positron Plasma Dynamics
Irakli Ugulava and Vazha Berezhiani
We investigate the influence of radiation pressure on the collective dynamics of a degenerate electron–positron
plasma interacting with an intense radiation flux. Assuming the
Fermi energy substantially exceeds the thermal energy, we apply general covariant hydrodynamic equations to demonstrate that in a sufficiently dense
plasma, radiation pressure outpaces the increase of relativistic inertia, yielding an enhanced terminal bulk Lorentz factor. Furthermore, this process is accompanied by dynamic
plasma cooling.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Stoichiometric cluster learning for few-shot property prediction of multi-ionic integrated energetic materials
Ming-Yu Guo, Wei-Jia Zou, Yu Shang et al. (+1 more)
Multi-ionic materials pose a distinct representational challenge in machine learning-driven materials design. Different from single-molecule or composition-based materials, their properties arise from how charged building blocks aggregate into specific assemblies. Here, we show how pretrained machine-learned interatomic potentials (MLIPs) can bypass full
crystal-structure prediction and support pre-synthesis screening from stoichiometric ionic clusters using multi-ionic integrated explosives (MIXs) as a synthesis-facing example. This strategy combines a stoichiometric ionic-cluster representation, which represents each candidate material by a non-periodic, stoichiometry-preserved formula-unit cluster, with multi-task fine-tuning (MT-FT), which adapts a pretrained atomistic backbone while retaining the energy–force objective as physical regularization for the sparse detonation-velocity labels. With the pretrained backbone regularized by MT-FT, this surrogate provides a cross-validated screen across only 25 structurally curated perovskite-type energetic materials (PEMs) with experimentally derived Kamlet–Jacobs (K–J) detonation velocities. Representation probes show that the learned descriptors implicitly retain site-aware ionic organization, density information, and coarse packing compatibility, implying why non-periodic clusters can remain predictive before full
crystal structures are known. The surrogate extends known PEMs chemistry to three newly synthesized ABX$_4$ materials with both unseen ABX$_4$ stoichiometry and an unseen ethylenediammonium B-site cation, yielding three-point concordance with K–J reference velocities and a mean absolute error (MAE) of 92~m$\cdot$s$^{-1}$ without retraining. Together, these results establish stoichiometry-preserved cluster learning as a synthesis-facing screening strategy for data-scarce multi-ionic materials.
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All authors Ming-Yu Guo, Wei-Jia Zou, Yu Shang, Wei-Xiong Zhang
Fluid Dynamics
Characterisation of a multistable turbulent wake: application of an improved regime identification with analytical model training
Ariane Barlet, Pierre Bragança, Christophe Cuvier et al. (+1 more)
This article presents the experimental study and the modelling of the multistable jet in the wake of two side by side square bars separated by a distance $G$ at Reynolds number $R=U_\infty H/\nu=10000$ (with $U_\infty$ the velocity of the incoming flow, $H$ the bar side and $\nu$ the kinematic viscosity). The velocity field downstream of the bars is measured by means of two dimensional two components Particle Image Velocimetry (PIV). We use the weighted transverse position of the jet $Y_m$ and the jet width $w$ to characterise the regimes of multistability as the gap ratio $G/H$ is increased. Three main regimes of multistability are possible: tristability, bistability, and monostability. In our wind tunnel, tristability is observed for $G/H\in [1.15,1.25]$, bistability is observed for $G/H\in [1.5,2.65]$ and monostability is observed for $G/H\in [3.0,3.5]$. Within the first two ranges of $G/H$, there exists gap ratios for which multistability is more complex. In order to analyse the simple and complex multistability regimes as well as the transition from bistable to monostable, we construct an analytical
stochastic differential equation (SDE) modelling $Y_m$ for each gap ratio. These SDEs are written with
polynomial drift and
diffusion. For this matter we use a data based method that finds an trade–off between simplicity of the model (smaller number of monomials) and precision. A first key advantage of the use of the data-based model fitting method is that when the flow is tristable, bistable or monostable, we recover the drifts expected from the theory of bifurcations, but we are now able to correct it with the right multiplicative noise expressed by the
diffusion. The second key advantage is that we can also fit atypical drift expressions when the multistability regime is complex that help us make sense of the jet behaviour.
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All authors Ariane Barlet, Pierre Bragança, Christophe Cuvier, Joran Rolland
Physics and Society
Individual and collective gains from cooperation and reciprocity in a dynamic-network Prisoner's Dilemma driven by extraversion, openness, and agreeableness
David Abián, Jorge Bernad, Sergio Ilarri et al. (+1 more)
How do stable personality differences shape cooperation when social ties can form and dissolve? We model a repeated Prisoner's Dilemma on an endogenous network in which three continuous Big Five traits map to transparent local mechanisms: Extraversion sets a target number of partners, Openness determines how broadly
agents search beyond friends-of-friends, and Agreeableness sets a baseline willingness to cooperate. At each encounter,
agents combine this baseline with the partner's directly observed history; there are no trait labels, gossip, or global reputations. Ties form when
agents are under-connected and are cut when they become over-connected, with cuts prioritising partners who have defected more often. We vary network size (N=30–200), population composition, and the balance between trait-driven and history-driven behaviour. Three robust patterns emerge. First, cooperate first, then reciprocate—high initial willingness to cooperate combined with history-sensitive response—produces systems that are simultaneously more prosperous, fairer, and safer. Second, personality has predictable conditional effects: Agreeableness helps when history matters but hurts when behaviour is mostly trait-driven; Extraversion amplifies the environment; Openness has little net payoff effect. Third, the network reorganises accordingly: degree assortativity stays near zero, whereas agreeable
agents increasingly connect to one another when cooperation takes hold.
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All authors David Abián, Jorge Bernad, Sergio Ilarri, Raquel Trillo-Lado
Fluid Dynamics · Cross-listed from cond-mat.soft
Active flows drive anchoring of nematics at rigid walls
Michael Fang, Ioannis Hadjifrangiskou, Sumesh P. Thampi et al. (+2 more)
Although confinement strongly influences flows in active materials, it remains unclear how active particles align at rigid boundaries when no thermodynamic anchoring is imposed. We address this question using continuum simulations of active nematics, together with analytical arguments based on a reduced near-wall description. In the flow-tumbling regime, extensile systems align parallel to the boundary, whereas contractile systems align perpendicular to it, consistent with active anchoring observed at active-passive interfaces. In the flow-aligning regime, the preferred orientation depends on the sign of activity and of the flow aligning parameter: either the shear-like flow generated near the wall selects the Leslie angle, or no unique
alignment is established. These results provide a unified framework for activity-induced anchoring at rigid walls, demonstrating that boundary
alignment in dense active matter can emerge solely from the interplay between self-generated flows and orientational dynamics.
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All authors Michael Fang, Ioannis Hadjifrangiskou, Sumesh P. Thampi, Julia M. Yeomans, Jan Rozman
Fluid Dynamics · Cross-listed from math.NA
Physics-informed token transformer methodology for nonlinear balance laws. I. Schwarzschild–Burgers fluid flows
Philippe G. LeFloch and Shuyang Xiang
We introduce a Physics-Informed
Token Transformer (PITT) methodology for nonlinear
hyperbolic balance laws in one space dimension, using piecewise steady-state profiles for the representation of approximate weak solutions. The method combines symbolic equation tokenization, a
Fourier neural operator encoder, an explicit Rankine–Hugoniot law for shock motion, and a learned correction term. For clarity, we present it here for the relativistic Schwarzschild–Burgers equation, a scalar model for spherically symmetric fluid flows on a Schwarzschild background. For this model the steady-state invariant and the generalized Riemann solutions are explicit, and they can therefore be built into the neural
evolution. In particular, the leading discontinuities are advanced by the analytical jump condition, while the learned part reconstructs smooth regions, rarefaction fans,
geometric dependence, and finite-resolution effects. The method is designed to locate wave fronts accurately and to preserve the relevant steady states. We test our PITT method on moving shocks, stationary shocks, rarefaction waves, and compare it with a standard high-order finite-volume approximation. We also analyze the standard Burgers limit (when the Schwarzschild mass tends to zero). The Rankine–Hugoniot prior plays the dominant role in these tests, while equation tokenization gives a systematic additional gain. The method is relevant for problems involving
geometric effects and/or complex shock-wave dynamics, and is used here to study the long-time dynamics of perturbations of steady-state solutions. In particular, we exhibit an asymptotic law of propagation for the shock location of perturbed steady-state flows.
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Applied Physics · Cross-listed from cond-mat.mes-hall
Janus-induced atomic reconstruction amplifies twist-angle modulation of interlayer thermal transport in moiré bilayers
Bin Xu, Rulei Guo, Jie Sun et al. (+8 more)
In two-dimensional moiré
bilayers, atomic reconstruction, the spontaneous structural relaxation toward energy-minimizing stacking registries in the near-commensurate regime, can strongly modify local stacking and interlayer coupling, providing a possibility to significantly control
phonon-mediated properties. Here we show that the twist-angle dependence of interlayer thermal conductance can be modified by introducing Janus-induced mirror-
symmetry breaking into
bilayer MoS2. The intrinsic out-of-plane dipole in MoSSe/MoS2
bilayers leads to frictionless interface, and reduces the
lattice deformation energy, thereby promoting atomic-reconstruction into locally distorted aperiodic moiré patterns. These features weaken interlayer coupling and suppress
phonon transmission across the interface, leading to an anomalously strong twist-angle dependence of thermal conductance, with a pronounced minimum at small twist angles and a reduction rate nearly one order of magnitude larger than that of twisted
bilayer MoS2. Our results demonstrate that interlayer thermal transport is modified by atomic reconstruction, highlighting Janus-induced mirror-
symmetry breaking as an effective way to promoting
phonon engineering in two-dimensional moiré structures.
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All authors Bin Xu, Rulei Guo, Jie Sun, Hiroo Suzuki, Takuma Yoshida, Ichiro Nakaya, Koki Sawasaki, Yasuhiko Hayashi, Shohei Chiashi, Tomoki Machida, Junichiro Shiomi
Atomic Physics
Protocols of coherent motion control for an interaction-driven Rydberg gate
Valentin Magro, Wojciech Adamczyk and Sylvain de Léséleuc
Generating
entanglement between two Rydberg atoms is at the core of neutral-atom
quantum computers. Current two-
qubit gates operate in the Rydberg-blockade regime, in which the full strength of the van der Waals interaction between the two Rydberg atoms is not directly exploited, to avoid sensitivity to the position noise of the tweezer-trapped atoms, at the cost of a longer time spent in the Rydberg state. Here, we propose a set of techniques based on coherent control of the atomic motion obtained by combining optical tweezers and a two-dimensional optical
lattice, and a sequence of multiple on/off pulses. The protocols keep the two-
qubit gate error contribution from position noise below $10^{-4}$, heat the atom by less than~$\Delta n = 0.01$, while being robust to
alignment errors of the potential up to $50$~nm and thermal excitation up to $\bar{n} = 3$. This toolbox opens the path for new two-
qubit Rydberg gates directly, or partially, driven by the interaction, in which the atoms spend only $\sim 10$~ns in the Rydberg state, minimizing the increasingly dominant error source originating from its finite lifetime.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
Output-Stage Design Optimization for High-Sensitivity SiSeRO CCDs and SiSeRO Active Pixel Sensors
Tanmoy Chattopadhyay, Sven Herrmann, Kevan Donlon et al. (+16 more)
The Single electron Sensitive Read Out (SiSeRO) technology is a new device class designed to support the needs of future X-ray and optical astronomical
telescopes that will require fast, low-noise, megapixel spectro-imagers. Developed at MIT Lincoln Laboratory, in collaboration with Stanford University and MIT, the first generation SiSeRO-CCD (charge-coupled device) prototypes achieved a charge/current conversion gain of 700$-$800 pA per electron, an equivalent noise charge (ENC) of around 3.5 electrons root mean square (RMS), and a full width half maximum (FWHM) energy resolution of approximately 130 eV at 5.9 keV at a readout speed of 625 kpix/s. Utilizing Repetitive Non-Destructive Readout (RNDR), these same devices also demonstrated sub-electron noise performance (ENC$<$0.5 electrons RMS) at a readout speed of 10 kpix/s. We present the results of device simulations for next-generation SiSeRO CCD output stages that optimize the sensing transistor and its internal gate geometry to enhance noise and speed performance. Further, the goal is to develop a SiSeRO active pixel sensor (APS) that combines the proven X-ray performance of CCDs with the architectural advantages of an APS. Enabling this requires substantial design updates, for example, incorporating two SiSeRO amplifiers side by side on each pixel and shuffling the charge between them to support RNDR. We discuss our device simulation framework and design parameter optimization in the first-generation SiSeRO devices.
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All authors Tanmoy Chattopadhyay, Sven Herrmann, Kevan Donlon, Ilya Prigozhin, Peter Orel, Steven W. Allen, Marshall W. Bautz, Michael Cooper, Catherine E. Grant, Beverly LaMarr, Christopher Leitz, Eric D. Miller, R. Glenn Morris, Abigail Y. Pan, Tonya L. Peshel, Artem Poliszczuk, Gregory Prigozhin, Haley R. Stueber, Keith Warner
Atomic and Molecular Clusters
Size characterization of neutral rare-gas clusters based on time-resolved polarization anisotropy measurements
Arne Morlok, Grzegorz Kowzan, Yilin Li et al. (+3 more)
The size determination of neutral clusters is experimentally challenging. In particular, weakly-bound rare-gas clusters tend to fragment upon ionization, resulting in systematic errors in cluster size studies. In contrast, characterization of the temporal polarization anisotropy dephasing provides a soft detection scheme for cluster size estimation, which avoids fragmentation of the clusters. Here, we present a systematic experimental study of argon and neon clusters in the size range of 50 to 10.000 atoms using this technique. In order to extract the mean cluster sizes from the data, we present an efficient analytical model of the polarization anisotropy dephasing of an ensemble of doped clusters. The approach shows remarkable sensitivity to small changes in the mean cluster size of just a few tens of atoms and allows us to refine the widely used Hagena scaling law for the estimation of rare-gas cluster sizes.
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All authors Arne Morlok, Grzegorz Kowzan, Yilin Li, Ulrich Bangert, Frank Stienkemeier, Lukas Bruder
Space Physics
For SIRs, CIRs, and Beyond: Polarization Ratio to Feature Location
Curt A. de Koning, Dusan Odstrcil, Sarah Gibson et al. (+3 more)
The Polarimeter to UNify the Corona and Heliosphere (PUNCH) mission will remotely observe
solar wind transients with high signal-to-noise ratio, high-cadence, high-resolution polarized white-light images. Using different polarization states, an important PUNCH data product will be polarization ratio images. In the small-Sun limit, and using two simple line-of-sight density distributions with a finite angular width that can approximate a stream or corotating interaction region (SIR/CIR), we analytically investigate how the polarization ratio will provide three-dimensional location information and what the uncertainty in this estimated location is.
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All authors Curt A. de Koning, Dusan Odstrcil, Sarah Gibson, Craig DeForest, Vic Pizzo, Christopher J. Scott
Space Physics · Cross-listed from stat.ML
Covariance-Boosted Gaussian Processes for Spatiotemporal Irregularities
Jeremy Ovadia
Nonstationary Gaussian process (GP) models are powerful tools for capturing input-dependent variability by adapting to observed data. However, with limited sampling and highly parameterized covariance structure, they are often prone to overfitting and overconfident uncertainty estimates, potentially leading to misleading predictions in safety-critical applications. Motivated by ionospheric modeling for satellite-based augmentation systems (SBAS), this paper proposes a Covariance-Boosted Gaussian Process (CBGP) framework centered upon boosting covariance priors to discover nonstationary latent functions for signal and observation variation that capture irregularities in the input domain. An additional layer of GP modeling of "partially-whitened" observations guides latent function relative error estimation that is used to iteratively update weak priors in a gradient descent-like procedure. Following boosting, restrictions are imposed upon prior covariances to prevent overfitting while posterior uncertainties are inflated to prevent model overconfidence. CBGP model efficacy and robustness are demonstrated through out-of-sample testing of both simulated and real-world applications that meet a three-nines integrity standard. The modeling of an extensive ionospheric storm
dataset over South America suggests accurate and reliable means to compute SBAS ionospheric corrections in the most challenging space weather environment using regional models that are more informed and responsive than local fitting performed by currently-operating SBAS.
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Optics
Investigating planar Proca metamaterials in nonlinear (2+1)-Electrodynamics
Widervan de Deus Morais, Stamatios Strikos, Ronaldo Thibes et al. (+1 more)
The present work pursues the investigation of the physics of a planar electromagnetic system incorporating nonlinear effects, as well as de Broglie-Proca and Chern-Simons mass terms in
vacuum, which can be tuned to describe new planar metamaterials (MTMs) potentially suitable for technological applications. As a result, we observe interesting effects generated by a spatially dispersive profile that emerges from non-linearity. This characterizes the three-dimensional QED
vacuum as a nonlocal metamaterial. The constitutive relations are derived, and the dielectric function of the metamaterial (MTM) exhibits spatial dispersion due to the Chern-Simons mass. The dispersion relations are analyzed in terms of applied external electromagnetic fields and both the de Broglie-Proca and Chern-Simons mass parameters. We also notice that, in the special case in which only a background
magnetic field is present, this field gives no contribution neither to the dielectric function nor to the spatial dispersion profile, as is the case when both the electric and
magnetic fields are present.
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All authors Widervan de Deus Morais, Stamatios Strikos, Ronaldo Thibes, José Abdalla Helayël-Neto
Physics and Society
An index to quantify scientific debt
N. Zen
I propose the index $z$, defined as $z=\sum\limits_{i=1}^{n} \mathrm{IF}_{i}$, where $\mathrm{IF}_{i}$ is the impact factor of the journal in the publication year of the $i$-th retracted paper. Whereas the $h$-index is a forward-looking measure of scientific achievement, the $z$-index quantifies scientific debt. The quantity $h-z$ may provide a more balanced assessment of scientific value.
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