Plasma Physics
Fusion-power amplification by compressive hydrodynamic fluctuations
Henry Fetsch and Nathaniel J. Fisch
Compressive fluctuations in hot
plasma, including acoustic waves and compressible
turbulence, increase the rate of fusion reactions. This power amplification comprises hydrodynamic, “two-temperature,” and kinetic components, the first resulting from the clumping of hot ions in the peaks of the fluctuations, the second from the unequal heating of ions and electrons as fluctuations dissipate, and the third from the long mean free paths of fast ions near the Gamow peak, which allow these ions to stream across gradients in fluctuating hydrodynamic fields before colliding. In many cases, the increase in fusion power produced by waves exceeds that produced if the wave energy were instead used for heating. Response functions describing the modification to fusion power by compressive fluctuations are obtained in magnetized and unmagnetized fusion
plasmas. Comparison to the related shear flow reactivity enhancement effect, a kinetic mechanism that increases fusion power in some divergence-free flows, illustrates a fundamental distinction between compressible and solenoidal
turbulence in fusion
plasmas.
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Instrumentation and Detectors
Charge sharing and alignment performance of bent ALPIDEs measured with low-energy protons
Berkin Ulukutlu, Christopher Ehrich, Laura Fabbietti et al. (+14 more)
The upgrade of the ALICE experiments Inner Tracking System (ITS3) aims to replace its innermost detection layers with bent wafer-scale CMOS MAPS sensors. This study examines the performance of ALPIDE chips, currently used in the ALICE ITS2, when operated in a bent configuration under realistic experimental conditions.
Proton beams with energies of 80 MeV, 120 MeV and 200 MeV were used to study
proton-
proton elastic scattering on a polypropylene fiber target reconstructed using two opposing arms of trackers with sensors bent to radii of 18 mm, 24 mm and 30 mm. The measured low-momentum
protons provided a testbed for investigating clustering behavior in high-energy loss events, where no significant impact of bending was observed on cluster size. Additionally,
alignment strategies for bent detectors were evaluated using the distance of closest approach (DCA) and opening angle between scattered
proton tracks as
benchmarks. The achieved resolution matches expectations from simulations, confirming the suitability of bent MAPS sensors for future high-energy and
nuclear physics applications.
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All authors Berkin Ulukutlu, Christopher Ehrich, Laura Fabbietti, Roman Gernhäuser, Fabrizio Grosa, Hartmut Hillemanns, Tobias Jenegger, Alex Kluge, Lukas Lautner, Magnus Mager, Lukas Ponnath, Andrea Rossi, Isabella Sannaa, Serhiy Senyukov Johanna Stachele, Miljenko Šuljić, Laszlo Vargaa, Alperen Yüncü
Geophysics
Seismic full waveform inversion via a physics-guided Fourier representation neural network
Gui Chen, Yang Liu, Haoran Zhang et al. (+1 more)
Accurate subsurface velocity models are essential for seismic imaging, yet conventional full waveform inversion (FWI) often suffers from cycle skipping, noise sensitivity, and reliance on good initial models. We develop a physics-guided
Fourier representation neural network (PGFRNN) for unsupervised acoustic FWI and simultaneous-source FWI (SSFWI), which embeds
Fourier-transformed seismic data into a latent space and iteratively updates the velocity model using a softplus-approximated log-cosh (SALC) loss and a physics-guided optimizer. Numerical tests on the Overthrust model demonstrate that PGFRNN outperforms conventional L2- and SALC-loss-based FWI methods, achieving higher inversion accuracy and robustness to noise and challenging initial models.
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All authors Gui Chen, Yang Liu, Haoran Zhang, Mi Zhang
Plasma Physics
Kinetic energy from the cubic sum rule of the dynamic structure factor
Fotios Kalkavouras, Panagiotis Tolias, Sebastian Schwalbe et al. (+6 more)
The third frequency moment sum rule of the dynamic structure factor $S(\mathbf{q},\omega)$ is explored for the first time as an alternative
estimator of the kinetic energy $K$ of
quantum many-body systems. As a practical example, the uniform electron gas at warm dense matter conditions is considered. First, $K$ is extracted from quasi-exact ab initio path integral Monte Carlo results for the imaginary-time density–density correlation function $F(\mathbf{q},\tau)$ and the expected excellent self-consistency with the thermodynamic differentiation route is confirmed. Second, $K$ is extracted from approximate dielectric formalism results for $S(\mathbf{q},\omega)$ and it is observed that common semi-classical approximations lead to a wave-number dependent $K$ with an incorrect short-wavelength limit. Our results are expected to be of broad interest for a great variety of applications, including time-dependent density functional theory, dielectric formalism schemes and warm dense matter models, as well as for the design of dedicated x-ray Thomson scattering experiments with the potential to provide model-free access to the full electronic equation of state.
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All authors Fotios Kalkavouras, Panagiotis Tolias, Sebastian Schwalbe, Thomas Gawne, Alexander Benedix Robles, Jan Vorberger, Zhandos Moldabekov, Maximilian Böhme, Tobias Dornheim
Medical Physics
Adversarial robustness of a U-Net-based model observer for CT protocol optimization
Filippo Maria Balli, Giorgia Stendardo, Sandra Doria et al. (+6 more)
Artificial intelligence is increasingly used in medical imaging, yet its robustness to input perturbations remains a critical concern for a wide clinical adoption. To this end, we used
adversarial examples to systematically probe vulnerabilities of a U-Net-based model observer for computed
tomography protocol optimization, performing detection and localization of low-contrast objects in a phantom
dataset.
Adversarial attacks were generated using both gradient-based and optimization-based white-box methods. Fast gradient perturbations produced high misclassification rates, reaching up to 75% at intermediate perturbation levels while remaining visually imperceptible. Localization was more robust, with success rates of about 25% for small perturbations and 42% at moderate levels. In contrast, optimization-based attack achieved success rates close to 50% for both tasks. To mitigate these vulnerabilities, dynamic
adversarial training was implemented. This reduced the success rate of optimization-based attacks to 7% for classification and 13% when including localization-specific training, demonstrating a substantial robustness improvement without compromising task performances, confirmed by localization receiver operating characteristic analysis. To further interpret model behavior, radiomic texture analysis was performed on original and
adversarial images. While most global image statistics remain stable, specific texture-related features exhibit consistent changes in successful attacks, highlighting the model's sensitivity to subtle local intensity patterns. Overall,
adversarial training improves robustness without degrading performance, while radiomic analysis reveals interpretable links between texture alterations and prediction failures, supporting more reliable and explainable AI systems for medical imaging.
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All authors Filippo Maria Balli, Giorgia Stendardo, Sandra Doria, Michele Ginolfi, Alessio Gnerucci, Diego Sona, Adriana Taddeucci, Cesare Gori, Evaristo Cisbani
Atomic Physics · Cross-listed from quant-ph
Cooperative control of dissipative qubits: steering along the fastest dissipative contraction
Robert Weiß, Sandro Wimberger and David Guéry-Odelin
In the control of dissipative
quantum systems, the slow relaxation modes usually set the ultimate manipulation timescale. We show that this bottleneck can be bypassed when dissipation itself is used as the engine of the transfer. For a
qubit with non-unital, anisotropic Bloch relaxation characterized by the ratio $\kappa=T_1/T_2>1$, coherent control can only rotate the Bloch vector, whereas the bath alone can change its length. The fastest transfer between two thermal states therefore consists in steering the Bloch vector, at every instant, along the direction in which the bath contracts it most rapidly. This direction is obtained in closed form and lies slightly beyond the equatorial plane. With strong pulses, the resulting minimum transfer time is a fraction of $T_2$, instead of several $T_1$ for passive relaxation. With bounded fields, the Pontryagin maximum principle yields a bang–singular–bang synthesis whose singular arc is precisely this fastest-contraction direction, and the minimum time exceeds its strong-pulse limit by a correction inversely proportional to the field amplitude. We also determine the full time–energy Pareto front: moderate accelerations require little energy, and the time optimum lies at its steep, high-energy end. Off-axis targets are treated within the same framework; their only additional benefit is a shorter final rotation. These results apply directly to
magnetic-resonance
spins, nitrogen-vacancy centers and superconducting
qubits, all of which operate deep in the strong-pulse regime.
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Fluid Dynamics
Efficient Wall-Modeled High-Order Compact Gas-Kinetic Scheme for Compressible Turbulent Flows
Yaqing Yang, Fengxiang Zhao and Kun Xu
Scale-resolving simulations of wall-bounded turbulent flows remain prohibitively expensive at high Reynolds numbers, owing to the stringent near-wall resolution requirements. High-order compact gas-kinetic schemes (CGKS) are accurate, robust, and efficient for compressible flows, making them an attractive foundation for reducing this cost. Building on the fifth-order scheme CGKS-5th, we develop a wall-modeled CGKS framework that alleviates the near-wall resolution burden through a pressure-gradient-based non-
equilibrium wall model while preserving the resolving power of the outer solver. CGKS-5th resolves the outer flow and supplies the wall model with data at the exchange location. On coarse near-wall meshes, the wall model reconstructs the under-resolved viscous wall stress, while CGKS-5th provides the inviscid wall flux directly; the two combine to form the wall momentum flux. To capture non-
equilibrium effects in adverse-pressure-gradient and separated regions, the wall model retains a pressure-gradient source term together with a pressure-gradient-corrected near-wall damping function. We assess the framework on two distinct flows: bluff-body separation past a circular cylinder, and a shock-induced separation bubble on the transonic RAE 2822 airfoil, using near-wall meshes far coarser than wall-resolved simulations require. For the RAE 2822 case, this corresponds to a twentyfold coarsening in the wallnormal direction, with comparable coarsening in other directions. In both cases, the wall-modeled CGKS-5th reproduces the separated flow structures and markedly improves near-wall predictions over its wall-model-free counterpart, most notably the skin-friction coefficient. The framework thus delivers accurate predictions of these separated flows at substantially reduced near-wall cost, while its lightweight coupling adds less than 1% runtime overhead in a multi-GPU implementation.
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Fluid Dynamics · Cross-listed from hep-th
Nonlinear nature of near-equilibrium viscous fluids
Yan Liu and Hao-Tian Sun
We study the late-time relaxation of a neutral relativistic viscous fluid in $d+1$ dimensions. In the long-wavelength regime, linearized hydrodynamics predicts that the sound mode at momentum $nk$ decays as $e^{-n^2\omega_I t}$. However, nonlinear analysis gives a decay of $e^{-n\omega_I t}$. We derive a closed asymptotic attractor solution in which the frequency of the $n$-th harmonic locks to $n$ times the complex frequency of the fundamental mode. The amplitude envelopes for energy current $J$ obey a simple cascading relation, $J_n=\alpha_J^{\,n-1}J_1^n$, with $\alpha_J$ fixed by the equation of state, the longitudinal viscosity, and the fundamental wavenumber. For conformal fluids, $\alpha_J=1/(8\eta k)$, in agreement with the
holographic result of arXiv:2512.07242. The existence of the attractor shows that, even near
equilibrium, field powers are not equivalent to amplitude order.
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Biological Physics · Cross-listed from cond-mat.soft
A phase-field model for viscoelastic compressible tumor growth
Luise Zieger, Min Wu, Chaozhen Wei et al. (+2 more)
It is well known that growing tumors generate and respond to stress in their local microenvironment. Tissue re-arrangements can relax these mechanical stresses and make the tissue more fluid-like. Further, intricate coupling between mechanotransduction and biochemical signaling leads to complex patterns of growth. To predict the outcomes of these nonlinear interactions, we develop a phase-field model to simulate tumors growing into a surrounding medium taking into account their elastic and viscous properties as well as their compressibilities. We couple continuum modeling of the viscoelastic mechanics to the concentration of a diffusible growth-promoting nutrient in a mass conservative way. The phase-field method is a stable and flexible way to describe the dynamics of arbitrarily shaped tumors. We demonstrate convergence of the phase-field model to a sharp interface model in radially symmetric geometries and can observe progression to stationary tumors. However, our results show that these stationary symmetric tumors are subject to
symmetry-breaking instabilities in 2D and 3D driven by two primary mechanisms: (i) elastic buckling instabiliies due to differential growth induced by the nutrient gradient and (ii) instabilities generated by apoptosis-related volumetric loss. Further, tissue fluidity and compressibility can lead to changes in tumor
topologies. Our modeling framework provides a robust methodology for investigating how tissue mechanics and growth factor signaling influence the progression and invasive potential of solid tumors.
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All authors Luise Zieger, Min Wu, Chaozhen Wei, John Lowengrub, Sebastian Aland
Instrumentation and Detectors
Improving Muon-Scattering Material Identification via Coarse Momentum Encoding and Unsupervised Domain Adaptation
Yuxin Bao, Zhao Zhang, Pei Yu et al. (+7 more)
Cosmic-ray
muon scattering has shown considerable potential for detecting
nuclear materials and other dense contraband, but practical deployment remains challenging. A major difficulty arises from the coupling between material properties and
muon momentum, since the broad natural momentum distribution influences the scattering angle and prevents unambiguous material identification. In this work, we propose a Coarse Momentum-Aware Domain Adaptation (CMADA) method to enable precise identification of materials. Instead of relying on high-precision momentum measurements, the proposed framework adopts coarse momentum binning combined with unsupervised domain adaptation to learn transferable scattering representations. In addition, a precision review mode based on averaging repeated samplings was proposed to further enhances identification performance. The coarse momentum binning strategy improves same-domain identification accuracy from 62.15% without momentum information to 89.52% with 5-bin momentum information, and further to 93.37% (precision review mode). Furthermore, the proposed unsupervised domain adaptation framework improves the cross-domain identification accuracy from 71.71% for the source-only baseline to 89.00% without requiring target domain labels.
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All authors Yuxin Bao, Zhao Zhang, Pei Yu, Liangwen Chen, Weibo He, Yu Zhang, Yuhong Yu, Xueheng Zhang, Lei Yang, Zhiyu Sun
Applied Physics
Probing Light-Matter Interaction with Topological Data Analysis
Timothy Holt, Maxim Goryachev and Jeremy Bourhill
We explore application of Topological Data Analysis to study light matter interaction through scattering response data in different dimensions. This method is robust against Fano resonance backgrounds in both strong and weak coupling regimes, maintaining accuracy even with reduced mode contrast, distorted lineshape, and the introduction of random trace noise. It scales to any number of interacting modes, reflecting the system's effective degrees of freedom. Crucially, TDA is not merely peak counting but reveals phase-encoded features in the scattering response and may be used even for a fully saturated amplitude response. The analysis is also applied to a three mode system with time reversal
symmetry breaking, revealing change in apparent number of loops and voids in combined two way scattering data. This approach is demonstrated to differentiate the three Dyson ensembles through their topological complexity and probability density functions, enabling analysis of complex modal systems.
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Fluid Dynamics
Exact analytical solutions for the piston effect in supercritical fluids under post-acoustic approximation – Short-time asymptotics, thermal penetration depth and comparison with the Spacelab D-2 experiments
Mátyás Szücs
Near the
liquid-vapor critical point, fluids become highly compressible, giving rise to a special, strongly coupled thermo-mechanical process: the piston effect. In this phenomenon, a thin thermal boundary layer develops near a heated wall; owing to strong thermal expansion, this layer acts like a piston, compressing the bulk fluid adiabatically and resulting in a seemingly accelerated thermal response. Although the piston effect is a thermo-acoustic process, the characteristic time scale of the boundary perturbation is typically orders of magnitude larger than the acoustic time scale of the setup. Consequently, rapid acoustic propagation can be neglected, justifying a post-acoustic approximation with a spatially uniform but time-dependent bulk pressure. Within the linear regime, the temporal
evolution of pressure can be directly connected to the heat flux entering through the boundaries. As a result, the problem reduces to a
diffusion equation governed by a spatially homogeneous source term that depends explicitly on the boundary conditions. Exact, closed-form analytical solutions are derived for effectively one-dimensional problems in both Cartesian and spherical coordinates, considering boundary conditions of the first and second kinds. Short-time asymptotic behavior and thermal penetration depth are analyzed for all four cases. By incorporating the heat capacity of a container via a homogeneous model, an effective boundary condition coupling the wall heat flux and the time derivative of the wall temperature is derived, allowing for a direct comparison with experimental data from the Spacelab D-2 mission. The analytical predictions show good agreement with the experimental results without relying on any numerical simulations.
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Optics
Dynamic Topological Light Control in Reconfigurable Non-Hermitian Metastacks
Ryan Hogan, Zihan Lu, Han Peng et al. (+4 more)
Metasurfaces often require complex lithography for dynamic optical control. To overcome this, we utilize a lithography-free, non-Hermitian planar metastack comprising a distributed Bragg reflector and a vanadium dioxide (VO2) thin film. By virtue of temperature and thermal hysteresis as an active synthetic dimension and exploiting the VO2
insulator-to-metal transition, we actively tune topological interface states to achieve polarization-sensitive spectral control. Notably, our system hosts path-dependent exceptional points (EPs); the intermediate hysteretic states generate a continuum of hot and cold EP pairs that ultimately converge into a single, degenerate EP. Furthermore, we experimentally observe wide-range dynamic optical control, comprising reversible 8% spectral shifts with near-unity reflectance modulation, alongside potential for ultrafast dynamics. Ultimately, our CMOS-compatible design provides a scalable, simple platform for active and topological photonics.
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All authors Ryan Hogan, Zihan Lu, Han Peng, Jie Zhou, Yanling Xiao, Shining Zhu, Hui Liu
Applied Physics
Design and Realization of Broadband Magnonic Spectrometers With Local Electrical Outputs
Johannes Greil, Maximilian Hofschen, Felix Naunheimer et al. (+3 more)
Microscopic
radio-frequency (RF) devices based on propagating
spin waves (SWs) are promising for compact, energy-efficient RF signal processing, but their implementation is impeded by fabrication complexity and the lack of efficient electrical readout. In this work, we demonstrate a SW-based Rowland circle spectrometer with electrical input and local electrical output transducers. The device is realized using a scalable fabrication process based on sputter deposition and wet-chemical etching of Yttrium-Iron-Garnet (YIG), forming concave grating structures with micrometer-scale features. The device functionality is confirmed by combined electrical and magneto-optical measurements, which show that the deflection of SW wavefronts at different input frequencies closely follows the analytically predicted behavior. The linear excitation of SWs via two input tones further confirms the spectrometer operation for simultaneously propagating waves. Beyond the single-device demonstration, we propose a concept for scalable architectures comprising multiple Rowland circles with tunable operating points. When combined with broadband parallel electrical readout, this approach enables control over bandwidth and spectral resolution, which are relevant to spectral occupancy detection in wireless communication systems.
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All authors Johannes Greil, Maximilian Hofschen, Felix Naunheimer, Ádám Papp, György Csaba, Markus Becherer
Popular Physics · Cross-listed from cs.GR
Rendering Coherent Scattering via Quantum Collision Models
João S. Ferreira, Spencer S. Topel, Pierre Fromholz et al. (+1 more)
Traditional light rendering techniques treat the optical properties of materials as static, yet this assumption breaks down in cases where these properties dynamically evolve in response to incident illumination. We present a novel shading framework that combines classical ray-tracing with a
quantum collision model to explore the effect of coherent light-matter interactions in rendering. By treating incident light and material excitations as quantized modes, we model sub-surface scattering as a sequence of
symmetry-constrained
unitary collisions. This formulation allows for the incorporation of non-integrable dynamics and chaotic optical responses due to multi-layer interference effects. We demonstrate how these collision operators can be pre-computed using near-term
quantum computers to generate standard BSDFs, enabling the rendering of new physics-inspired materials with distinct optical signatures.
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All authors João S. Ferreira, Spencer S. Topel, Pierre Fromholz, James R. Wootton
Medical Physics
Prediction of biological radiation effects based on ionization clusters (nanodosimetry)
Hans Rabus
This article reviews approaches that link the formation of ionization clusters in nanometric volumes to radiobiological effectiveness. The corresponding models were developed as the field of nanodosimetry developed. Some address early biological radiation effects, such as DNA damage, while most aim to predict
cell survival or inactivation. The models also differ in the nanodosimetric quantities considered, with many based on the probability distribution of ionization cluster formation in a single target. Some models account for the synergistic effects of pairs of ionization clusters formed in different targets. Several models feature macroscopic aggregation frameworks based on particle fluence, which are proposed for use in radiotherapy treatment planning, particularly in ion-beam radiotherapy. The models are presented here using harmonized terminology and notation for nanodosimetric quantities. An extension of the conceptual framework of nanodosimetry is also discussed. This extension transitions from a target-centered description to a track-centered description. It also introduces nanodosimetry-based analogs of dosimetric concepts, such as dose and linear energy transfer. This paper traces and summarizes the historical development of nanodosimetry-based biological effect models and discusses conceptual aspects of the models to reveal their underlying assumptions and the extent to which they are mechanistic or merely elucidate correlations. Eventually, an attempt is made to identify the key open questions in this field that still need to be addressed.
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Optics
Bifurcation structure of soliton self-injection locking in microresonators
S. Deshmukh, T. M. Schneider and A. Tikan
Self-injection locking (SIL) of a diode
laser to a high quality-factor microresonator has recently become increasingly important in hybrid integrated photonics, providing access to compact sub-Hz linewidth
lasers. It was also shown to facilitate the access to dissipative Kerr
solitons - the key to a low-noise coherent frequency comb on a photonic chip. However, the existence and stability ranges of SIL
soliton states in experimentally controlled parameters are still not fully understood. Here we study the bifurcation structure of solutions in a model of
soliton SIL in the weak-backscattering limit. We show that SIL produces
soliton-number-dependent existence ranges of multi-
soliton solutions in free-
laser detuning and feedback phase parameters. We identify exclusive single-
soliton existence regions and demonstrate dynamical access to single
solitons in this region by direct numerical simulations using prescribed parameter sweeps.
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Computational Physics
High-order tensor neural network for iteration-free structure relaxation
Shaobo Yu, Haoting Zhang, Yu Han et al. (+5 more)
Structure relaxation is important for the discovery of new materials, yet conventional ab initio optimization remains a major bottleneck in high-throughput screening workflows. Machine learning potentials have accelerated relaxation by orders of magnitude, but they still rely on iterative optimization and high-quality DFT force labels. Here, we present HotRelax, a high-order tensor message-passing neural network for one-shot, end-to-end prediction of relaxed structures. Trained directly on paired unrelaxed and relaxed structures, HotRelax requires no DFT force labels and predicts relaxed structures in a single forward pass, without iterative inference or post-processing. Across five diverse
datasets spanning 3D bulk
crystals, 2D layered materials and catalysts, HotRelax shows strong performance relative to state-of-the-art end-to-end relaxation models, achieving lower prediction errors on several
benchmarks while maintaining a compact model size and efficient inference. Extensive DFT calculations further show that the predicted structures are close in energy to their DFT-relaxed counterparts. When integrated into catalytic workflows, HotRelax also improves the accuracy and generalization of relaxed-state energy prediction models. Together, these results support HotRelax as an efficient and widely applicable framework for end-to-end structure relaxation, with strong potential to accelerate high-throughput materials discovery.
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All authors Shaobo Yu, Haoting Zhang, Yu Han, Zhennan Zhang, Zhiyue Guo, Junjie Wang, Hao Gao, Jian Sun
Optics
Broadband gain characterization of Co:MgF$_2$ for mid-infrared femtosecond pulse amplification
Natsuki Kanda, Kaito Nishimiya and Eiji J. Takahashi
The broadband gain characteristics of Co:MgF$_2$ were investigated to assess its potential as a gain medium for ultrashort-pulse amplification around the 2 $\mu$m spectral region. Single-pass gain measurements performed using femtosecond seed pulses revealed broadband amplification in the 1.5-2.4 $\mu$m region. The temporal dynamics and spectra of the gain were experimentally characterized and utilized for numerical simulations to assess the feasibility of Co:MgF$_2$ as a broadband gain medium for ultrashort pulses. The results indicate its potential for broadband amplification in future short-wave infrared to mid-infrared ultrafast
laser systems, particularly when combined with coherent waveform synthesis or post-compression techniques.
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Optics
Ultrasensitive infrared-to-visible artificial vision via self-evolving projection guided by single-pixel detection
Yao Wang, Baolei Liu, Muchen Zhu et al. (+4 more)
Infrared detection and visualization are essential for augmenting human perception across diverse fields, ranging from night
vision to industrial inspection and bio-imaging. Conventional infrared cameras are often hindered by high cost, bulky architecture, and complex fabrication requirements. Upconversion sensing systems offer a pixel-free and cost-effective alternative solution by upconverting infrared
photons into visible-light signals. However, existing upconversion systems suffer from limitations such as high operating voltages, low
quantum efficiency, which prevent their applications in
photon-starved environments. Here, we report self-evolving infrared-to-visible upconversion with single-pixel detection (SIVIS) that enables real-time upconverted visualization under
photon-starved conditions by integrating self-evolving projection with single-pixel sensing. SIVIS iteratively optimizes illumination patterns with a digital micromirror device based on real-time feedback from a single-pixel infrared detector. This self-evolving process enables the autonomous reconstruction of the target's
geometric profile. Simultaneously, it projects a co-modulated visible beam onto the object itself or an adjacent screen, rendering the infrared target directly perceptible to the naked eye in real-time. SIVIS achieves sensing and projection without latency under an ultra-low infrared detection limit of 0.11
photons per pixel per frame (sub-pW -cm2 level) benefited from the high sensitivity. Furthermore, we also validate SIVIS to decrypt infrared-encoded anti-counterfeiting features and visualize vascular-like structures embedded within biological tissues. This
photon-feedback-driven artificial
vision framework offers a scalable and adaptive solution for ultrasensitive infrared
vision, opening promising avenues for night
vision, biomedical imaging, and sensing under extreme low-light conditions.
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All authors Yao Wang, Baolei Liu, Muchen Zhu, Linjun Zhai, Dajing Wang, Zhaohua Yang, Fan Wang
Fluid Dynamics
A second-order unified gas-kinetic wave-particle method with enhanced mesh independence for hypersonic flows
Junzhe Cao, Rui Zhang, Wenpei Long et al. (+2 more)
Benefiting from the direct modeling of physical laws in a discretized space and the automatic decomposition of the gas distribution function into hydrodynamic waves and particles, the UGKWP method offers significant advantages for multiscale flows such as hypersonic flows,
plasma transport, and radiation transport. In this study, the particle sampling accuracy in the UGKWP method is improved from first order to second order, so that the second-order spatial and temporal accuracy is preserved across the full scheme. Specifically, the modifications include second-order particle sampling based on local macroscopic gradients, a weighted least-squares gradient reconstruction that incorporates wall values, a revised Venkatakrishnan limiter for highly stretched
cells, and conservation corrections after particle sampling. Moreover, the first-order Chapman–Enskog term is considered in the free-transport part of the hydrodynamic wave flux, enabling better recovery of the GKS in the near-continuum regime. Based on these improvements, the mesh-independence behavior of the UGKWP method is notably enhanced, which is more consistent with the performance of the UGKS, validated by a detailed hypersonic cylinder flow test case. Furthermore, systematic comparisons with the single-scale DSMC method are performed for two-dimensional hypersonic flow over a cylinder and three-dimensional flow over a blunt cone. Wall pressure, shear stress, and heat flux coefficients (CP, CF, and CQ) are examined in the cylinder case, while the overall aerodynamic coefficients (CL, CD, and L/D) are assessed in the cone case. The multiscale UGKWP method exhibits significantly better mesh-independence performance than DSMC for mesh-sensitive quantities such as CF, CQ, CD, and L/D, which are critical for aerodynamic and thermal protection design of near-space hypersonic vehicles.
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All authors Junzhe Cao, Rui Zhang, Wenpei Long, Chengwen Zhong, Kun Xu
Optics
Hessian sparsity-constrained self-supervised network for near-infrared single-photon single-pixel imaging
Yao Wang, Muchen Zhu, Linjun Zhai et al. (+6 more)
Near-infrared (NIR) imaging has emerged as an important technology for night
vision, remote sensing, and biological imaging, yet conventional array-detector-based systems are often limited by insufficient sensitivity, high cost, and substantial dark noise. Single-pixel imaging (SPI) offers an attractive alternative, enabling single-
photon-level NIR imaging by using a cost-effective single-element detector. Nevertheless, SPI remains restricted by
photon noise, leading to degraded imaging quality and limited frame rate under extremely low
photon flux conditions. Here, we present a Hessian sparsity-constrained self-supervised network (HS3N) for single-
photon NIR SPI, which can suppress noise and enable high-fidelity and real-time imaging under ultra-low illumination conditions. The HS3N integrates the physical forward model of SPI with an untrained neural network regularized by both sparsity priors and Hessian-based structural constraints, enabling effective noise suppression while preserving structural fidelity and continuity. Both simulated and experimental results demonstrate that HS3N enables high-fidelity reconstructions under ultra-low NIR
photon levels down to ~0.01
photons per pixel. Furthermore, we demonstrate its dynamic capability by monitoring the dynamic
evolution and detachment of infrared-absorbing droplets, at a frame rate of ~20 Hz under ~0.19
photons per pixel, highlighting its potential for high-sensitivity infrared inspection. The proposed reconstruction framework paves the way for practical NIR imaging in extreme low light conditions, which can be extended to visible, mid-infrared or terahertz imaging, offering broad potential for
photon-efficient sensing across a wide spectral range.
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All authors Yao Wang, Muchen Zhu, Linjun Zhai, Huiyuan Zhang, Junnan Chen, Yiming Yu, Zhaohua Yang, Baolei Liu, Fan Wang
Fluid Dynamics
Gappy Reconstruction of Bubbly Flows by Guided Diffusion Models
Hridey Narula, Tianyi Li, Michele Buzzicotti et al. (+2 more)
Experiments in multiphase flows are often limited in their ability to simultaneously obtain velocity measurements in different phases. At the same time, flow reconstruction from phase-limited measurements is a challenging problem due to the substantially different velocity statistics across the phases. We address this problem for buoyancy-driven bubbly flows in the pseudo-
turbulence regime by using a guided
diffusion model. We train the model using two-dimensional slices of the velocity field extracted from fully resolved three-dimensional direct numerical simulations. The model generates physically realistic velocity fields both unconditionally and when conditioned on the surrounding
liquid flow. The reconstructed bubble-phase velocity field accurately reproduces key statistical features of the flow. We further show that a simple patching procedure for adjacent two-dimensional slices enables a reasonable reconstruction of the three-dimensional flow inside a bubble. These results establish the potential of
diffusion models to serve as generative priors for three-dimensional turbulent multiphase flows, opening a route toward the reconstruction of unobserved or experimentally inaccessible velocity fields from sparse, partial, or phase-limited measurements.
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All authors Hridey Narula, Tianyi Li, Michele Buzzicotti, Luca Biferale, Prasad Perlekar
Data Analysis and Statistics
The Squealer: Sensification of model exploration and model misfit
Andrew Gelman, Andrew H. Jaffe, Eliot Carlson et al. (+1 more)
We introduce a method for visual and auditory feedback when exploring the fit of a model to data. Starting with a best-fit curve fit to data, the user can drag the curve to a new position and the computer will emit a squeal, becoming louder and more unpleasant as the discrepancy between curve and data increases. We demonstrate with four examples: a two-parameter curve fit to golf putting data, a four-parameter curve fit to dilution assays, a fit to cosmological data sensitive to the parameters of the Big Bang model, and a nonparametric Gaussian process fit to temperature readings.
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All authors Andrew Gelman, Andrew H. Jaffe, Eliot Carlson, Philip Greengard
Plasma Physics
Harnessing Toroidal Neutral Flows to Enhance Divertor Particle Exhaust
M. Moscheni, A. Herrmann, J.D. Lore et al. (+5 more)
In 1991 Reiter et al. (1991
Plasma Phys. Control. Fusion 33 1579) considered the onerous exhaust requirements of ITER, and wrote: "The
vacuum pumping problem of a fusion reactor will probably require some novel solution". Here we show that a toroidally oriented pump inlet can passively exploit intrinsic neutral flows to reduce back-flow, raise duct pressure, and ultimately improve particle-exhaust performance. Drawing on previous experimental observations and SOLPS-ITER edge-
plasma simulations, we consolidate the evidence for a
plasma-imprinted, multi-species toroidal neutral "wind" in detached tokamak divertors. We isolate the underlying mechanism in a prototypical divertor private-flux region using a database of two-dimensional direct simulation Monte Carlo (DSMC) calculations. The ordered neutral motion is recovered with a strong toroidal
alignment, kilometre-per-second velocities, and persistence up to several centimetres across slip-to-transitional rarefied regimes (Kn=0.02$-$2). We then assess the consequences of capturing this ordered motion using a second database of idealised proof-of-principle DSMC simulations. Compared to the traditional poloidal arrangement, a toroidally oriented pump inlet reduces back-flow by up to 20% for deuterium and up to 33% for helium at 10% concentration. Partial pressures in the toroidal exhaust path are enhanced across the database, nominally by a factor of 1.78$\pm$0.04 for deuterium and 2.00$\pm$0.05 for helium. For fixed throughput, this implies a reduction in the required effective pumping speed and corresponding hardware. More broadly, these results motivate explicit retention of toroidal neutral momentum in divertor and sub-divertor modelling, and dedicated studies of neutral aerodynamics, including in stellarators, where an analogous directional imprinting is expected to occur.
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All authors M. Moscheni, A. Herrmann, J.D. Lore, M. Kryjak, C. Soika, R. Kembleton, S. Lazerson, K. Revel
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Design Method of Quasi-Lumped Element Bandpass Filters Using Superconducting Coplanar Waveguide for Millimeter-Wave Multichroic Imaging
Shinsuke Uno, Kah Wuy Chin, Tai Oshima et al. (+6 more)
An on-chip band-defining filter coupled with a superconducting
photon detector is a promising technology for developing multi-band imaging cameras at millimeter and submillimeter wavelengths. In this paper, we present the design of on-chip bandpass filters based on coplanar waveguide geometry, which can be easily integrated into large-format multi-band detector arrays. A lumped element filter design is suitable not only for achieving a compact footprint but also for suppressing harmonics to reduce band-to-band crosstalk in a multiplexer. However, the coplanar waveguide geometry and the photolithography process rule limit the maximum available inductance and capacitance of lumped elements, which does not sufficiently meet the requirements of filter
circuits. To overcome this limitation, we have established a design method for quasi-lumped element filters, in which the maximum element size is relaxed to a quarter wavelength, exceeding the ideal lumped element size. We achieved design solutions for 150, 220, and 270 GHz 8th-order Chebyshev bandpass filters and a triplexer. We also report on the measurement results of a scaled model of the bandpass filter, demonstrating the validity of our proposed filter design.
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All authors Shinsuke Uno, Kah Wuy Chin, Tai Oshima, Satoshi Ono, Takeshi Sakai, Kazuki Watanabe, Shuhei Inoue, Tatsuya Takekoshi, Kotaro Kohno
Optics · Cross-listed from quant-ph
Coherent manipulation of the biphoton generation in cavity-QED system
Jia-Ni Yang, Xin-Yi Ling, Yuan Feng et al. (+2 more)
We theoretically investigate the coherent manipulation of biphoton generation via spontaneous four-wave mixing in a cavity-QED system with a single atom. The atom is driven by pumping, coupling, and driving fields, and the generation of the Stokes and anti-Stokes
photons are enhanced by two cavities. By solving the master equation in the steady state, we analyze the spectral brightness, as well as the degree of the auto-correlation and cross-correlation. Our results show that when the pumping and driving fields are in two-
photon resonance, the dark state established between the ground and Rydberg states. efficiently enhances the controllability of the driving field over the biphoton generation and the
quantum statistics. In contrast, under large two-
photon detuning, the control capability of the driving field is significantly reduced. The coupling field, which directly relates to the electromagnetically induced transparency, modifies the linewidth of the biphoton, while the atom-cavity coupling strength only changes the brightness without affecting the linewidth.
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All authors Jia-Ni Yang, Xin-Yi Ling, Yuan Feng, Xiao-Jun Zhang, Jin-Hui Wu
Optics
All-optical Synchronization of Breather Solitons in a Kerr Microresonator
Wang Liao, Stuart G. Murdoch, Stephane Coen et al. (+3 more)
Microresonator Kerr
solitons are promising candidates for the realization of miniaturized on-chip optical frequency combs. For specific system parameters, these
solitons are associated with oscillatory instabilities, leading to breathing dynamics characterized by periodically modulated temporal and spectral profiles. In this regime, the
solitons form a frequency comb comprised of primary comb lines surrounded by sidebands separated by the breathing frequency. Here, we numerically and experimentally demonstrate that the breathing sidebands can be all-optically synchronized to a weak monochromatic
laser injected into the cavity, thus providing direct control of the
soliton oscillation frequency. We judiciously characterize the synchronization process, and show that it is accompanied by a strong reduction of noise in the
soliton's breathing. Our results provide fundamental insights on oscillatory dissipative structures, and could enable new forms of composite optical frequency combs.
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All authors Wang Liao, Stuart G. Murdoch, Stephane Coen, Gregory Moille, Kartik Srinivasan, Miro Erkintalo
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Atomically Thin Amorphous Carbon with an Ultralow Dielectric Constant
Chee-Tat Toh, Artem K. Grebenko, Ugur Karadeniz et al. (+17 more)
Two-dimensional (2D) materials exhibit excellent properties at monolayer thickness and are viable replacements for various microelectronic components as scaling gradually approaches the atomic limit. Despite significant advancements in the ongoing 2D revolution of integrated
circuits, one crucial building block, namely a 2D ultralow-k (ULK) dielectric, remains unreported. The challenge lies in achieving a dielectric constant less than 3, as traditional low-k dielectrics are inherently unstable at the 2D limit due to their amorphous or porous nature. The realisation of ultrathin dielectrics with low-k is also needed to address current bottlenecks in integrated
circuits scaling. Specifically, low-k materials are necessary to minimise parasitic capacitances as the distance between conductive elements shrinks below 10 nm. Moreover, advanced architectures like gate-all-around field effect transistors (GAA FET) require even lower dielectric constants (k<2) at sub-3nm thickness. Here, we show that layer-by-layer grown multilayer amorphous carbon (ML-AC), as thin as 0.8 nm, is a mechanically robust 2D ULK dielectric with k of 1.35 and dielectric strength of 28-31 MV cm-1. The lack of any long-range order, its intrinsic 2D nature, sp2 carbon character and low density are all essential for minimising dielectric permittivity. Moreover, ML-AC overcomes the vulnerability of existing dielectrics to ion
diffusion degradation with a record metal ion
diffusion time to failure (TTF) of 10^10 s for even a single layer. Therefore, otherwise necessary additional layers occupying up to 3 nm can be eliminated, which is especially significant as metal line widths approach 10 nm. Combined with its low-temperature, direct and conformal growth even on a dielectric, these critical features enable substantial improvements in silicon-based
semiconductor electronics and ensure compatibility with future 2D electronics.
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All authors Chee-Tat Toh, Artem K. Grebenko, Ugur Karadeniz, Usha Bhat, Ya He, Hongji Zhang, Denis V. Vyalikh, Anna Makarova, Alexander Fedorov, Alena A. Alekseeva, Kostya Iakoubovskii, Lu Shi, Andrei Starkov, Chuan Chu Tee, Lucas M. Sassi, Michel Bosman, Naoto Kamiuchi, Yuta Sato, Kazutomo Suenaga, Barbaros Oezyilmaz
Computational Physics · Cross-listed from cs.LG
ScaleAware-JEPA: Latent Representation for Discovery in Multiscale Physical Fields
Guang-Xing Li
Continuous physical fields represent a large fraction of data under scientific investigation. Their multiscale structures are central to discovery, yet useful coordinates are not known in advance. Standard self-supervised methods define context and targets in fixed image coordinates, posing a predictive task misaligned with fields organized across a continuous scale hierarchy. We introduce ScaleAware-JEPA, a framework that constructs dense, label-free latent coordinates for continuous scalar fields. Constrained
Diffusion Decomposition (CDD) separates each field into pixel-registered scale components and provides the scale coordinates that define the masking geometry. The resulting JEPA objective predicts hidden structure with a context footprint tied to the
diffusion scale of each component rather than to an arbitrary patch size. Across MHD
turbulence, interstellar molecular gas and urban nighttime-light structure, the learned geometry maps back to coherent morphology, forming dense structural atlases without labels or predefined
segmentation rules. By tying latent prediction to the scale hierarchy of a field, ScaleAware-JEPA constructs latent coordinates through which complex physical patterns can be inspected before their relevant structures have been prescribed. Code is available at https://github.com/gxli/SA-JEPA.
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