Computational Physics
Moment-preserving particle merging via non-negative least squares
Georgii Oblapenko and Manuel Torrilhon
A novel particle merging
algorithm for rarefied gas dynamics simulations is proposed that can conserve arbitrary velocity and spatial moments of the particle distribution via solving a non-negative least squares problem. An extension that preserves both exact and approximate collision rates is also derived. The
algorithm is applied to the simulation of several model rarefied gas dynamics problems, where it exhibits noticeably lower merging-induced error in key macroscopic quantities.
Read the full abstract ▾
Optics
Temporal Weyl Physics and Topological Control of Direction-Selected Radiation in Anisotropic Photonic Time Crystals
Zihao He, Sihao Zhang, Huanan Li et al. (+1 more)
Anisotropic photonic time
crystals, enabled by periodic temporal modulation of a uniform anisotropic medium, exhibit asymmetric momentum-bandgap structures and offer unique control over light-matter interactions. Here, we introduce and construct temporal Weyl points in APTCs within a synthetic three-dimensional space defined by two phase parameters and the quasi-frequency. The temporal response reveals robust
Fermi arcs linking TWPs of opposite topological charge. Unlike spatial counterparts, these
Fermi arcs emerge only after the first temporal supercell comprising multiple periods of APTCs, reflecting causality. We further show that TWPs generate a directional near-zero radiation trajectory in momentum space with tunable radiation from stationary charges embedded in APTCs, while the associated
Fermi arcs robustly suppress radiation at selected directions and frequencies. Our findings establish temporal Weyl physics in photonic time
crystals and uncover new opportunities for topological control of light-matter interactions through the time dimension.
Read the full abstract ▾
All authors Zihao He, Sihao Zhang, Huanan Li, Xiang Ni
Physics and Society
Simple spatial processes can generate heterogeneous contact distributions in face-to-face interactions
Juliette Gambaudo and Mathieu Génois
Face-to-face interactions reveal recurring patterns, suggesting the possibility of shared underlying mechanisms. More specifically, inter-contact durations, contact durations and number of contacts per edge share similar heavy-tail distributions in many empirical settings. A common intuition is that face-to-face interactions may be influenced by spatial constraints, and that the observed complex behaviors could arise from such physical limitations. Our models explore the impact of this constraint by simulating pedestrian dynamics, and studying the generated temporal network of contacts. Previous work showed that the inter-contact duration distribution is recovered with a pedestrian dynamic as simple as the two dimensional random walk, but this approach doesn't allow to recover the distribution of the number of times a pair of individuals has been in contact. One assumption is that the number of contact between individual arises from the social relationship between them, in other words a
memory of past interactions. However, we here present models that are based on solely spatial rules, by adding simple targeting mechanisms to the two-dimensional random walk. We show that these models allow to recover a broad distribution of the number of contacts, revealing the importance of two ingredients: localized phases and controlled population mixing. This suggests that the observed heterogeneity in the contact numbers within the data does not necessarily emerge from underlying social relationships between individuals, since an equivalent distribution may be reproduced using a purely spatially based model, without the need for
memory mechanisms.
Read the full abstract ▾
Plasma Physics
Cyclic reformation of subcritical perpendicular fast magnetosonic shocks due to oblique Whistler waves
ME Dieckmann, L Palodhi, M Francois et al. (+2 more)
The stability of subcritical perpendicular fast magnetosonic shocks, which are propagating at 1.7 times the fast magnetosonic speed, is investigated using two-dimensional PIC simulations. The
plasma, composed of electrons and fully ionized nitrogen, is permeated by a uniform
magnetic field oriented at 45 degrees to the simulation plane normal. This configuration results in a diamagnetic current that sustains the shocks
magnetic ramp and is partially resolved within the simulation plane. The diamagnetic current drives an oblique lower-hybrid gradient drift instability within the ramp. This instability has been observed in
magnetic reconnection experiments and studied in the framework of a Harris-type sheath in previous studies. It arises from a reactive coupling between the oblique Whistler wave, which is propagating backward in the electron rest frame, and the forward-propagating ion acoustic wave. Our simulations show that the
magnetic component of this wave modulates the shocks
magnetic field, while the electrostatic ion density modulation forces the shock to collapse into a
magnetic piston and then reform. The reformation is not forced by an external perturbation as in previous simulations but by the oblique Whistler wave.
Read the full abstract ▾
All authors ME Dieckmann, L Palodhi, M Francois, D Folini, R Walder
Plasma Physics
Generating intense attosecond pulses and vectorizing polarization states from laser-plasma interactions
Panfei Geng, Yipeng Wu, Zhixin Fan et al. (+7 more)
Vector beams with spatially structured polarization and intertwined
spin-orbital angular momentum (SAM-OAM) provide powerful degrees of freedom for tailoring light-matter interactions. While such structured beams are well established in the visible and infrared regimes, extending them to the extreme-ultraviolet (EUV) and soft X-ray (SXR) domains at relativistic intensities remains a major challenge. Here, we investigate the generation of higher-order harmonic vector beams driven by relativistic
laser-
plasma interactions. Combining theoretical analysis with three-dimensional particle-in-
cell simulations, we elucidate the underlying physical mechanisms governing the transfer and conversion of polarization and orbital angular momentum during harmonic generation. We demonstrate that both the polarization
topology and OAM of the emitted harmonics can be deterministically controlled by the topological charges of the driving field. Owing to the intrinsic properties of vector beams, either few-cycle driving pulses or vector polarization gating applied to multi-cycle pulses enable the production of intense isolated attosecond pulses featuring spiral wavefronts and spatially tailored polarization states. These results establish a pathway toward high-intensity structured light sources in the EUV and SXR regimes and open new opportunities for ultrafast and strong-field light-matter interaction studies with engineered angular momentum.
Read the full abstract ▾
All authors Panfei Geng, Yipeng Wu, Zhixin Fan, Min Chen, Longqing Yi, Xiaohui Yuan, Zhengming Sheng, Warren B. Mori, Chan Joshi, Jie Zhang
Fluid Dynamics · Cross-listed from quant-ph
Quantum machine learning for the quantum lattice Boltzmann method: Trainability of variational quantum circuits for the nonlinear collision operator across multiple time steps
Antonio David Bastida Zamora, Ljubomir Budinski, Pierre Sagaut et al. (+1 more)
This study investigates the application of
quantum machine learning (QML) to approximate the nonlinear component of the collision operator within the
quantum lattice Boltzmann method (QLBM). To achieve this, we train a variational
quantum circuit (VQC) to construct an operator $U$. When applied to the post-linear-collision
quantum state $\ket{\Psi_i}$, this operator yields a final state $\ket{\Psi_f} = U\ket{\Psi_i}$ that successfully replicates the nonlinear collision dynamics derived from the Bhatnagar-Gross-Krook (BGK) approximation. Within this framework, we present two distinct architectures: the R1 model and the R2 model. The R1 model is designed for
quantum simulations that involve multiple time steps without intermediate measurements, focusing on accurately capturing nonlinear dynamics in continuous
evolution. In contrast, the R2 model is tailored to achieve the high-precision reconstruction of the nonlinear operator for a single time step with an
unitary operator.
Read the full abstract ▾
All authors Antonio David Bastida Zamora, Ljubomir Budinski, Pierre Sagaut, Valtteri Lahtinen
Classical Physics
Efficiency of a smoke curtain in a ventilated tunnel
Alexandre Narcisse, Olivier Vauquelin, Éric Casalé et al. (+1 more)
Smoke curtains are typically used in public-access buildings in connexion with ventilation effects without considering a crossed design. This paper aims to understand the fire smoke behaviour in the context of the interaction between mechanical ventilation and smoke curtains. This interaction is analysed here in the configuration of a tunnel with using numerical simulations. Initially, without the presence of fire smoke, the length of the
vortex induced downstream of the curtain is determined as a function of the longitudinal velocity and the size of the curtain. It can be observed that for a sufficiently high velocity (i.e. a sufficiently high Reynolds number), the size of this
vortex depends only on the height of the smoke curtain. Then, in the presence of a moderate fire heat release rate, we determined the air velocity required to prevent smoke from rising beyond a curtain measuring 1/5 the height of the tunnel. A significant reduction in this longitudinal velocity was observed in comparison to the velocity required to achieve the same level of containment without the presence of a curtain. The
vortex generated by the curtain nevertheless interacts with the smoke layer, locally increasing its thickness. Lastly, this configuration is tested in the case of a medium and high fire heat release rate in a road tunnel with transverse ventilation. The work carried out suggests that the installation of smoke curtains of an appropriate size, combined with control of ventilation effects, is likely to reduce the need for them.
Read the full abstract ▾
All authors Alexandre Narcisse, Olivier Vauquelin, Éric Casalé, Romain Nottet
Fluid Dynamics
Two-stage dispersion mechanism of clean spherical bubbles rising in a chain
Satoi Suzuki and Toshiyuki Sanada
Wake-induced lift is a key mechanism governing the initial destabilization of bubbles rising in a chain (Atasi et al., 2023). Moore's wake model predicts limited interfacial vorticity and a relatively slender, spatially confined wake for clean spherical bubbles, suggesting that wake-mediated interactions weaken as the inter-bubble spacing increases. However, we observed pronounced large-scale lateral dispersion and strong bubble frequency dependence in controlled experiments where bubble diameter and generation frequency were independently varied, even when the inter-bubble separation exceed the characteristic wake length. A reduced-order model incorporating pairwise wake-induced interactions captured the onset of bubble chain destabilization but systematically underpredicted the subsequent emergence of large-scale dispersion. We demonstrate that bubbles rising in a chain collectively generate a mean upward
liquid flow that modifies the local shear field, enhancing the lateral migration through shear-induced lift. Incorporating this self-induced weak flow into the model quantitatively reproduced both the dispersion magnitude and its frequency dependence. These results suggest that the dispersion of bubbles rising in a chain involves a two-stage mechanism, with initial chain destabilization mediated by wake interactions, followed by flow modification arising from two-way coupling between bubbles and the
liquid. This collective mechanism highlights the importance of self-induced mean flow effects in continuum descriptions of bubble flows.
Read the full abstract ▾
Space Physics · Cross-listed from astro-ph.EP
Misaligned rings around minor planets with moons
Barnabás Deme
Recent observations have confirmed the existence of rings around minor bodies in the outer
Solar System. These objects may possess satellites as well. Here we analytically investigate the interaction between such rings and satellites. We show that the perturbations from the moons may efficiently lead to off-equatorial rings around minor bodies like trans-neptunian objects or centaurs. In particular, we derive criteria for the orbital elements under which such misaligned rings may exist. These considerations will be easily testable with the upcoming deep sky surveys.
Read the full abstract ▾
Biological Physics · Cross-listed from q-bio.BM
The fitness landscape of overlapping genes
Orson Kirsch, Nicole Wood, Steven A Redford et al. (+1 more)
Natural genomes sometimes encode two different
proteins in staggered reading frames of the same DNA sequence. Despite the prevalence of these 'overlapping genes' across the tree of life, it remains unknown whether arbitrary
protein pairs can overlap, to what extent such overlaps are feasible, or what design principles govern them. Here, we study compatibility, frustration, and connectivity in the fitness landscape of overlapping genes. We computationally design sequences de novo that satisfy the dual functional constraints of two distinct
protein families. The joint fitness landscape, inferred via Potts models from multiple sequence
alignments, reveals a fundamental trade-off between the two
proteins and provides a simple criterion for when overlap is feasible. We find widespread compatibility between
protein families, with one class of reading frames markedly more permissible than others. By exploring alternative genetic codes, we find that the natural genetic code is uniquely well-suited to support overlapping genes. Constructing mutational paths between sequences, we find that sequence-diverse overlapped genes can be connected via a network of near-neutral mutations. Overall, our results suggest that
protein fitness landscapes are sufficiently flexible so as to accommodate the stringent, orthogonal requirements of overlapping genes.
Read the full abstract ▾
All authors Orson Kirsch, Nicole Wood, Steven A Redford, Kabir Husain
Medical Physics
Commissioning and clinical outcome assessment of a novel surface-guided radiation therapy (SGRT) system at a C-Arm linear accelerator
Hui Khee Looe, Niklas Felix Hendrik Bartner, Björn Poppe et al. (+1 more)
Surface-guided radiation therapy (SGRT) is now widely used for radiation-dose-free, marker-free patient positioning in modern radiotherapy. We commissioned and clinically implemented a novel SGRT system, LUNA 3D (LAP, Lueneburg, Germany), featuring browser-based operation, GPU-accelerated surface reconstruction, frame rates above 12 Hz, a large field of view, and virtual
laser projection. Commissioning included tests of temperature drift, reproducibility, translational and rotational shift accuracy, gantry-related camera occlusion, agreement with cone-beam CT (CBCT), and end-to-end dosimetric performance. Results were evaluated using both an SGRT-acquired reference surface and a CT-derived external surface. Temperature drift remained below 0.4 mm on all axes. With the SGRT reference, maximum deviations were at most 0.3 mm translationally and 0.2 degrees rotationally; with the CT-derived reference, translational deviations increased to 0.8 mm, consistent with systematic bias from the reference surface. Agreement between LUNA 3D and CBCT was within 1.0 mm, and end-to-end testing showed CBCT residuals of 0.9-1.3 mm with 1.2% dosimetric deviation. All results satisfied ESTRO-ACROP guideline criteria. Clinical evaluation of 192 breast and 259 pelvic treatment
datasets showed significant improvements after implementation: the breast 3D translational vector decreased by 28.7% from 7.00 +/- 4.35 mm to 4.99 +/- 2.75 mm, and the pelvic 3D rotational vector decreased by 24.0% from 2.31 +/- 0.96 degrees to 1.76 +/- 0.67 degrees (both p < 0.001). These results establish LUNA 3D as a reliable SGRT system that improves routine patient positioning accuracy.
Read the full abstract ▾
All authors Hui Khee Looe, Niklas Felix Hendrik Bartner, Björn Poppe, Kay C. Willborn
Fluid Dynamics
Numerical Bow Shock Instabilities in Inert Polyatomic Gases
G. S. Sidharth and Anubhav Dwivedi
We investigate inviscid numerical instabilities that arise in simulations of axisymmetric flow over a hypersonic sphere in an inert, calorically perfect gas at low specific heat ratio ($\gamma \approx 1.1$–$1.2$). We show that when the density ratio across the bow shock is high and the computational mesh is relatively coarse, numerically induced traveling-wave instabilities of the carbuncle type can develop in the shock layer near stagnation for inert gases. These instabilities, not previously documented in the literature, are noteworthy because bow shock oscillations are also observed experimentally in polyatomic gases exhibiting post-shock thermochemical relaxation. When such gases are modeled as inert with an effectively low $\gamma$, our results emphasize the need for caution to avoid conflating genuine physical instabilities with numerical artifacts in simulations.
Read the full abstract ▾
Computational Physics
Discovery of Symbolic Hamiltonian Expressions with Buckingham-Symplectic Networks
Joe Germany, Joseph Bakarji and Sara Najem
Hamiltonian systems lie at the heart of modeling the physical world. Their defining scalar, the
Hamiltonian, encodes both energy conservation and
symplectic geometry in its phase-space trajectories. Recent deep learning approaches model
Hamiltonian systems by
embedding their properties either in the architecture or in the loss function. However, they typically ignore that: i) a
Hamiltonian carries units of energy and/or ii) that every integrable
Hamiltonian admits a canonical transformation to action-angle coordinates in which the dynamics reduce to a simple rotation on an invariant
torus. We propose BuSyNet, a deep learning architecture that combines these two constraints via a dimensionally-consistent,
symplectic transformation. A
symplectic layer maps input trajectories to lower-dimensional latent action-angle variables, which are then combined with system parameters to discover a symbolic
Hamiltonian expression in units of energy. Evaluated on the harmonic oscillator and the Kepler two-body problem (in 2D and 3D), BuSyNet recovers concise, closed-form
Hamiltonians that outperform state-of-the-art neural architectures in long-term prediction accuracy and stability, while maintaining interpretability.
Read the full abstract ▾
Popular Physics · Cross-listed from cond-mat.mtrl-sci
Beyond Beryllium: AI-Accelerated Materials Discovery for Interstellar Spacecraft Shielding
Yue Li, Xu Pan and Kaiyuan Guo
Project Daedalus (1973–1978), the most detailed interstellar probe design study ever conducted, specified a 9 mm beryllium erosion shield to protect the spacecraft payload during its 5.9 light-year cruise to Barnard's
Star at 12% of the speed of light. This design, however, predated both the isolation of two-dimensional materials and the development of graph neural network (GNN) property predictors. Here, we systematically screen 20 candidate materials–spanning conventional aerospace metals, transition metal dichalcogenides, and ultra-high-temperature ceramics–using density functional theory (DFT) data from the JARVIS database (76,000 materials) with independent validation by the Atomistic Line Graph Neural Network (ALIGNN). We evaluate candidates across four criteria: specific mechanical stiffness (KV/rho), sputtering resistance, thermal
neutron absorption cross-section, and thermodynamic stability. Our screening identifies hexagonal boron nitride (h-BN) and boron carbide (B4C) as dual-function materials offering simultaneous mechanical protection and
neutron radiation shielding, and we propose a
graphene/h-BN/
polymer layered
heterostructure shield design that achieves an estimated 47% mass reduction relative to the original beryllium specification. These findings will become immediately actionable upon the successful development of fusion pulse propulsion, which we note remains an outstanding engineering challenge.
Read the full abstract ▾
Geophysics
Horizontal-Component Prior-based Framework for Adaptive Shear-wave Leakage Suppression in OBC Data
Zheng Cong, Shiqi Dong, Xintong Dong et al. (+1 more)
Shear-wave leakage in the vertical (Z) component of ocean-bottom cable (OBC) seismic data commonly results from the receiver tilt and poor seafloor coupling, introducing unwanted coherent noise that impacts the subsequent data processing and imaging. Traditional denoising methods are limited by manual parameter tuning and idealized model assumptions, while deep-learning (DL) approaches have shown significant potential in suppressing shear-wave leakage. However, supervised learning requires clean primary waves (P waves) as the label, which is generally impractical to obtain for field data. To address these challenges, we propose a framework based on horizontal-component priors for adaptive shear-wave leakage suppression (HPAS). Instead of relying on clean primary-wave (P-wave) data, HPAS generates input-label pairs directly from raw multi-component field data using an additive-subtractive noise strategy. Specifically, we extract shear-wave (S-wave) noise from the horizontal components and apply a linear transformation to match its first and second order moments with the S-wave leakage in the Z-component, and the statistically matched noise is then added to and subtracted from the original Z-component to create the input and label pairs. By allowing the denoising model to learn the S-wave features present in the differences between the input and the label, the adaptive denoising process approximates supervised learning. Evaluations on both synthetic and field data demonstrate that the proposed HPAS framework effectively and adaptively suppresses S-wave leakage while preserving the amplitude of the P-wave signals in the Z-component, offering a robust solution with strong generalization capabilities.
Read the full abstract ▾
All authors Zheng Cong, Shiqi Dong, Xintong Dong, Xunqian Tong
Chemical Physics
Stochastic GW with the Orthogonalized Projector Augmented Wave Method
Dimitri Bazile, Minh Nguyen, Yuji Kon et al. (+2 more)
We introduce
stochastic GW with the orthogonalized projector augmented-wave method (OPAW-sGW). This implementation enables accurate quasiparticle band gaps on significantly coarser real-space grids than norm-conserving pseudopotential sGW (NCPP-sGW). The orthogonalized PAW representation preserves the formal all-electron character and enables
stochastic sampling of the Green's function and screened Coulomb interaction.
Read the full abstract ▾
All authors Dimitri Bazile, Minh Nguyen, Yuji Kon, Tucker Allen, Daniel Neuhauser
Optics
Ultra-high precision speckle spectrometer enabling radio-frequency scale resolution of atomic spectra
Gabriel Britto Monteiro, Christopher Perrella, Sarah K. Scholten et al. (+4 more)
Laser speckle, the granular intensity pattern arising from random optical interference, provides a high-dimensional encoding of spectral information that can be exploited for precision metrology. Speckle-based spectrometers have advanced rapidly owing to their compact footprint, mechanical robustness and
alignment agnostic nature, yet their spectral resolution has remained limited to the picometre scale. In this work, we break this limit by employing an integrating sphere as a multiply scattering cavity with access to a high range of path lengths to enhance spectral sensitivity. At 780$\,$nm, the resulting device achieves a resolution of 6$\,$fm, corresponding to a resolving power of $1.3\times10^8$, representing an approximately 80-fold improvement over previous implementations. This ultra-high resolution enables clear discrimination of
laser sidebands generated by an electro-optical modulator, with extracted sideband powers agreeing with expected values to within 1%. It further permits the first direct speckle-based measurement of the hyperfine structure of the $\text{D}_{2}$ transition in $^{85}\text{Rb}$, with transmission spectra differing by no more than 3.6% from independent wavemeter-referenced measurements. These results establish speckle as a new platform for ultra-high precision spectroscopy,
radio-frequency spectrometry, and microwave photonics.
Read the full abstract ▾
All authors Gabriel Britto Monteiro, Christopher Perrella, Sarah K. Scholten, Morgan Facchin, Andre N. Luiten, Graham D. Bruce, Kishan Dholakia
Biological Physics · Cross-listed from cond-mat.soft
Organic Electrochemical Transistor Arrays with Integrated Lipid-Sealed Femtolitre Chambers for Simultaneous Electrical and Optical Detection of Membrane Protein Activity
S. Kojima, S. Rawat, M. Sanchez Miranda et al. (+4 more)
We report a scalable method for fabricating arrays of fifty-two ion-sensitive PEDOT:PSS organic electrochemical transistors (OECTs) on glass coverslips, each incorporating an integrated fluoropolymer microwell sealed with a lipid
bilayer for simultaneous electrical and fluorescence microscopy studies of membrane
proteins. The architecture addresses a key limitation of existing OECT arrays by providing every transistor with an independent fluid reservoir sealed by its own isolated segment of lipid
bilayer, thereby confining membrane
protein diffusion to a defined sensing region. Microwells containing a dye-labelled $50$~mM KCl solution were sealed with lipid
bilayers and overlaid with a dye-free $100$~mM KCl solution. Following addition of $\alpha$-hemolysin, formation of heptameric pores enabled K$^{+}$ ions to diffuse into the microwells and dye molecules to diffuse out, producing concurrent decreases in transistor conductance and fluorescence intensity. This integrated platform combines electrical and optical readout within independently addressable sensing sites and provides a versatile, scalable foundation for larger transistor arrays and the investigation of diverse membrane
proteins reconstituted into lipid
bilayers.
Read the full abstract ▾
All authors S. Kojima, S. Rawat, M. Sanchez Miranda, J.G. Gluschke, H. Noji, L.K. Lee, A.P. Micolich
Data Analysis and Statistics · Cross-listed from astro-ph.SR
Global asteroseismology of 19,000 red giants in the TESS Continuous Viewing Zones
K. R. Sreenivas, Timothy R. Bedding, Daniel Huber et al. (+5 more)
TESS (Transiting
Exoplanet Survey Satellite) has produced long-term photometry for millions of
stars across the sky. In this work, we present an asteroseismic catalogue of 19,151 red
giants in the TESS Continuous Viewing Zones using sectors 1–87 (Years 1–7). We visually assessed the power spectra for oscillations, and then applied the computationally efficient nuSYD method to confirm reliability. We identified an increase of 80% in the number of previously known oscillating red
giants at a TESS magnitude $>$ 8. We determined the frequency of maximum power ($\rm \nu_{max}$) and the large frequency separation ($\rm \Delta \nu$) using the pySYD pipeline, achieving typical precisions of 1.5% and 1%, respectively. We classified the
stars into Red
Giant Branch (RGB) and Core Helium Burning (CHeB) classes using a Convolutional Neural Network. Using spectroscopic data for 10,298
stars with reliable asteroseismic measurements, we have been able to measure
stellar mass and radii with precisions of 7.5% and 2.8%, which is comparable to that from 4-yr $Kepler$ data. A comparison of the seismic radii with
Gaia radii shows excellent agreement. With three years of TESS data, the asteroseismic parameters are precise enough to identify the RGB bump and delineate the Zero Age Helium Burning edge. Combined with astrometric data, these parameters reveal established trends across the
Galactic plane, providing a valuable set of uniformly determined asteroseismic parameters for
Galactic Archaeology.
Read the full abstract ▾
All authors K. R. Sreenivas, Timothy R. Bedding, Daniel Huber, Dennis Stello, Marc Hon, Claudia Reyes, Yaguang Li, Daniel Hey
Fluid Dynamics
Turbulent heat transfer enhancement by compliant walls
Morie Koseki and Marco Edoardo Rosti
This study investigates the effect of compliant walls on the turbulent heat transfer in channel flows over viscous-hyperelastic walls. We perform Direct Numerical Simulations, fully resolving the mutual fluid-structure interactions between the turbulent flow and the compliant walls, varying the wall elasticity and the thermal diffusivity in a fully turbulent condition. We show that the compliant wall leads to an increase not only of the momentum transfer but also of the heat transfer. Since the compliant wall can dynamically move, in the near-wall region heat flux is mostly transferred via turbulent convection rather than
diffusion, as typically found with rigid walls. Thus, the heat flux can be controlled not only by varying the thermal diffusivity, but also by changing the transverse modulus of elasticity which governs the wall-normal velocity fluctuations and consequently the temperature ones. Finally, we show that the physical mechanism controlling these modifications are the sweep and ejection events.
Read the full abstract ▾
Biological Physics
Contact-Dependent Ion Gating Explains Directional Asymmetry in the Bacterial Flagellar Motor
Jiading Zhu, Yongnan Hu, Yuhai Tu et al. (+1 more)
The bacterial flagellar motor (BFM) is a rotary molecular machine driven by the ion electrochemical potential across the
cell membrane. Recent cryo-EM structures reveal a cogwheel-like architecture in which multiple stators engage a large rotor. A longstanding puzzle is the directional asymmetry of its torque-speed relation: concave in counterclockwise (CCW) rotation but nearly linear in clockwise (CW) rotation. Here, we develop a
stochastic mechanochemical model that explicitly incorporates rotor-stator coupling and detailed ion translocation kinetics. By integrating physiological torque-speed data with recent measurements of rotor-stator relative motion, we show that under physiological conditions the motor operates in a tight engagement regime, rendering the torque-speed relation largely insensitive to the specific form of mechanical interactions. This finding rules out differences in rotor-stator mechanics as the origin of CW-CCW asymmetry. Guided by cryo-EM structures, we propose a contact-dependent gating mechanism in which the MotA-FliG interaction modulates the ion release rate of the MotB subunit proximal to the FliG ring. Molecular dynamics simulations indicate tighter MotA-FliG contact in the CW motor, implying a reduced ion release rate compared to CCW. Our model demonstrates that differential gating strength accounts for the observed asymmetry: stronger gating in CCW shortens torque-free waiting phases, enhances torque generation, and produces a concave torque-speed curve, whereas weaker gating in CW yields lower torque and a linear relation. This structure-based framework quantitatively links molecular asymmetry to motor function and identifies specific interfaces for targeted perturbation and mutational studies.
Read the full abstract ▾
All authors Jiading Zhu, Yongnan Hu, Yuhai Tu, Yuansheng Cao
Plasma Physics
A machine learning framework for developing quasilinear saturation rules of turbulent transport from linear gyrokinetic data
Preeti Sar, Sebastian De Pascuale, Harry Dudding et al. (+1 more)
A new neural network model for a quasilinear saturation rule has been developed to map linear gyrokinetic data to nonlinear saturated potential magnitudes to predict the total energy and particle fluxes. The training
dataset is taken from the high resolution simulation database generated from nonlinear gyrokinetic
turbulence simulations with the CGYRO code for developing the SAT3 model. This new model, named SAT3-NN, overall is able to capture the 1D saturated potential magnitudes of the
dataset more accurately than SAT3, as depicted by lower percentage errors in the peak locations and peak values of the 1D saturated potentials. The resulting fluxes also had smaller deviations from the nonlinear CGYRO data as compared to previous saturation models such as SAT0 - SAT2. Consistent with SAT3, SAT3-NN is able to recreate the anti-gyroBohm scaling of fluxes seen for the TEM-dominated cases considered.
Read the full abstract ▾
All authors Preeti Sar, Sebastian De Pascuale, Harry Dudding, Gary Staebler
Optics
Noise-resilient nanophotonic gyroscope with sub-prad phase resolution
Yu Tao, Xinhang Li, Jingzheng Huang et al. (+5 more)
Optical gyroscopes based on the Sagnac effect are the cornerstone of precision orientation and
navigation. However, their bulky form factors prevent deployment in emerging mobile and autonomous systems. On nanophotonic platforms, the Sagnac signal plummets under aggressive miniaturization. Consequently, the signal is easily swamped by refractive-index fluctuations, rendering
navigation-grade sensitivity within just a few square millimeters a notoriously elusive goal. Here, we demonstrate a noise-resilient nanophotonic optical gyroscope by exploiting a two-chain decoupling architecture to effectively isolate the rotation signal from channel noise. Implemented on a 3 mm^2 passive silicon-nitride chip, the proof-of-concept device achieves a bias instability of 0.79 deg/h and an angle random walk of 0.0014 deg/\sqrt{h}, representing improvements of 4 and 6 orders of magnitude, respectively, over a representative nanophotonic gyroscope with the same enclosed sensing area (ref. 1). In the broader context of integrated optical gyroscopes, our approach bridges the long-standing size-performance gap by two to three orders of magnitude, moving chip-scale devices into a previously inaccessible regime and pointing toward
navigation-relevant precision for monolithic microsystems. This architecture further enables an estimated input-equivalent phase resolution of 0.17 prad with general applicability, establishing a foundational framework for the next generation of robust, monolithically integrated photonic sensing systems.
Read the full abstract ▾
All authors Yu Tao, Xinhang Li, Jingzheng Huang, Zidi Lin, Yuyao Guo, Hongjing Li, Linjie Zhou, Guihua Zeng
Biological Physics
In-vivo entropy production of A. subaru
Yu Fu, Emmy Dobson, Benjamin B. Machta et al. (+1 more)
Entropy production is often used as a proxy for energy consumption of a non-
equilibrium system. Lower bounds can be estimated from coarse-grained observations, and this has been done for various biological systems. Here, we apply these tools to a more macroscopic system whose true energy consumption is also known. We find that while
entropy production does give a lower bound, it is some 25 orders of magnitude away from being saturated. To be certain of this result, we survey different methods of estimating irreversibility, and write down a novel kNN
estimator.
Read the full abstract ▾
All authors Yu Fu, Emmy Dobson, Benjamin B. Machta, Michael C. Abbott
Fluid Dynamics
Optimization-Based Discovery of A Non-Attracting Flow State in An Oscillating-Cylinder Wake
Daiwei Dong, Wenbo Cao, Wei Suo et al. (+2 more)
In the flow past a stationary circular cylinder, the classical Karman
vortex street arises from a
Hopf bifurcation of the steady flow at the critical Reynolds number. Although this solution becomes dynamically unstable beyond this point, it remains an exact solution of the governing equations. Motivated by this observation, we investigates whether similar non-attracting flow solutions exist in the flow past a forced oscillating cylinder at supercritical Re. In the present study, while employing PINNs to investigate the flow past a forced oscillating cylinder, we identify a class of flow solutions that are inaccessible through direct time-stepping simulations. The obtained solution remains phase-locked with the cylinder oscillation frequency, despite the corresponding parameters lying outside the lock-in regime. To verify this solution, the obtained PINNs solution is used as the initial guess for an optimization based on the optimizing a discrete loss (ODIL) framework. The results show that the solution can be consistently maintained during the optimization process. This indicates that the solution is self-consistent in the optimization sense, although it does not an attracting state of the original dynamical system. To understand the reason, we compare the numerical
evolution mechanisms of each solvers. The results indicate that, flow states that satisfy the governing equations but are dynamically non-attracting can be identified and maintained as minima of the optimization problem. For the flow past a forced oscillating cylinder, non-attracting periodic solutions that satisfy the governing equations exist in addition to the attracting states obtained by conventional time-stepping simulations. Optimization-based solvers can therefore reveal such flow states that are difficult to obtain through direct time integration, providing a new perspective for understanding complex wake dynamics.
Read the full abstract ▾
All authors Daiwei Dong, Wenbo Cao, Wei Suo, Jiaqing Kou, Weiwei Zhang
Physics Education · Cross-listed from hep-ph
Motivation and design of a yotta-eV $\tau^+\tau^-$ collider
Matt Bellis, Matthew Carberg, Chester Gould et al. (+10 more)
Two significant goals of the particle physics community is the precision study of the
Higgs boson and the search for new particles. The Large
Hadron Collider (LHC) is the current high-energy
collider, soon to be superseded by the High-Luminosity LHC (HL-LHC). Much of the community has rallied around a
muon-
collider, though that is most likely 25 years in the future. In this paper, we argue for a bolder approach: a tau-
collider, in which oppositely-charged $\tau$-
leptons are collided with energies on the yotta-eV scale and a potential radius that places it in the Oort cloud. Given the long time-scale and significant construction challenges, we strongly suggest the focus of the community shift to this discovery machine. We acknowledge that the technology necessary may require humanity to evolve to a Kardashev Level-I or Level-II civilization, which is all the more reason to begin R&D now.
Read the full abstract ▾
All authors Matt Bellis, Matthew Carberg, Chester Gould, Jackson Ingenito, Fasiha Khaliq, Emely Kintzel, Shane Kirschmann, Neha Matta, Sophia Pavia, Emmett Pearl, Payton Ramsdill, Grace Scherer, Cullen Wright
Classical Physics · Cross-listed from math.DS
Time-bandwidth Study of Non-classically Damped, Linear, Time-invariant Coupled Oscillators with Closely Spaced Modes
Luis M. Baldelomar Pinto, Alireza Mojahed, Sobhan Mohammadi et al. (+3 more)
In dynamics and vibrations, the concept of bandwidth for linear time-invariant systems is widely recognized as a measure of the dispersion of frequency content around resonance. Similarly, the time constant is associated with the rate of energy decay in the time domain. Notably, the time-bandwidth limit for such systems is unity, indicating that achieving sharp frequency localization while simultaneously maintaining a slow energy decay is not feasible, nor is it possible to achieve a broad frequency spread while preserving a rapid energy decay. However, the time-bandwidth concept does not have a well-defined application to multi-degree of freedom systems characterized by strong modal interactions. This research aims to develop a comprehensive time and bandwidth concept for a linear two-DOF system with significant modal interactions. We focus on a non-classically damped system, which facilitates complex mode interactions, and we investigate how the definition of bandwidth and time constant can be applied to account for the slow dynamics observed in energy decay. By examining this system under various parameters, we gain insights into the energy decay behavior at specific time-bandwidth product regimes. Our analytical results are validated through experiments. Our findings elucidate the implications of the time-bandwidth product for a linear multi-DOF system's response and provide valuable insights into the influence of modal interactions on energy decay.
Read the full abstract ▾
All authors Luis M. Baldelomar Pinto, Alireza Mojahed, Sobhan Mohammadi, Keegan J. Moore, Lawrence A. Bergman, Alexander F. Vakakis
Optics
Structured detection microscopy
Larnii Booth, Kyle Clunies-Ross, Rumelo Amor et al. (+4 more)
Super-resolution microscopy is crucial for imaging sub-wavelength biological structures. However, most techniques rely on nonlinear saturation or
stochastic switching of emitters, limiting imaging speed and increasing phototoxicity. Here, we achieve deep super-resolution without employing saturation or
stochastic dynamics, instead using a form of spatial mode demultiplexing. By shaping the point-spread function of the emitted light, our Structured Detection Microscope (SDM) redistributes information away from high shot-noise regions of the image, enhancing sensitivity to sub-diffraction emitter separations in two-dimensions and without mode-sorting optics. Implementing SDM within a high-numerical aperture total internal reflection fluorescence microscope, we demonstrate imaging of fluorophores attached to DNA nanorulers with separations as small as 50 nm at resolutions surpassing 40 nm - fivefold below the diffraction limit. This shows that spatial mode demultiplexing can achieve far sub-wavelength resolution and is applicable to biologically relevant samples. By enabling super-resolution biomolecular imaging without emitter saturation and stochasticity, our work opens the door to better understanding biological structure, function and dynamics.
Read the full abstract ▾
All authors Larnii Booth, Kyle Clunies-Ross, Rumelo Amor, Nicolas Mauranyapin, Zixin Huang, Michael A. Taylor, Warwick P. Bowen
Chemical Physics
Reliable and Efficient Automated Transition-State Searches with Machine-Learned Interatomic Potentials
Jonah Marks, Jonathon Vandezande and Joseph Gomes
Transition-state searches are central to understanding reaction mechanisms, but the high computational cost of density-functional theory (DFT) limits their application in high-throughput catalyst and materials discovery. Machine-learned interatomic potentials (MLIPs) offer near-DFT accuracy at orders-of-magnitude lower cost, yet their reliability for transition-state searches remains underexplored. Here, we systematically
benchmark hybrid transition-state-search workflows combining six freely available potentials (MACE-OMol25, UMA-Small, UMA-Medium, eSEN-S, AIMNet2, and GFN2-xTB) with two reaction-path-finding
algorithms (the freezing-string method and climbing-image nudged elastic band) across 58 diverse reactions spanning small organics, polymerization chemistry, and transition-metal catalysis. We find that models trained on the Open Molecules 2025
dataset exhibit markedly superior performance, with MACE-OMol25 achieving a 96.6% success rate while requiring fewer than four DFT-gradient evaluations per reaction on organic systems - a 94-96% reduction compared to conventional DFT-based searches. Low-level refinement on the MLIP surface before high-level DFT optimization reduces computational cost three-fold with minimal loss in reliability. For transition-metal systems, UMA-Medium demonstrates promising transferability to in-distribution transition metal complex reactions and out-of-distribution organometallic C-H activation. These results establish MLIP-accelerated workflows as practical tools for automated reaction discovery, enabling near-DFT accuracy at a fraction of traditional expense.
Read the full abstract ▾
Physics and Society
Machine-learning extraction of size-dependent temperature scales in the 2D XY model
Qingao Fan, Xu Li and Tingting Xue
Machine learning has become a useful tool for studying phase transitions in statistical systems.For the two-dimensional classical XY model, however, the topological character of the Berezinskii-Kosterlitz-Thouless (BKT) transition and pronounced finite-size effects make it nontrivial to extract robust size-dependent pseudo-critical temperatures from configuration data. Existing studies often stop at phase classification, leaving open how standard neural-network outputs can be turned into quantitatively testable observables. Here we develop a machine learning-assisted framework for the 2D XY model that uses standard network outputs to extract the size-dependent sequence of pseudo-critical temperatures T(L). Specifically, we generate Monte Carlo configurations using embedded cluster updates, train a standard ResNet18 only on samples from the Quasi-ordered Phase and the Disordered Phase, and determine T(L) from bootstrap-averaged probability curves using the 50% crossing criterion. We then analyze the finite size drift of this temperature sequence using BKT-motivated scaling and compare it with susceptibility-peak temperatures. The resulting temperature sequence shows a systematic finitesize drift consistent with BKT-type behavior and remains in the same fluctuation window as the susceptibility peak, supporting its interpretation as a finite-size pseudo-critical temperature. More broadly, this framework provides a practical route for converting standard neural-network outputs into physically interpretable finite-size observables in systems with strong crossover or topological transition signatures
Read the full abstract ▾