Plasma Physics
Plasma Confinement State Classification via FPP Relevant Microwave Diagnostics
Randall Clark, Vacslav Glukhov, Georgy Subbotin et al. (+4 more)
We present a parsimonious and robust machine learning approach for identifying
plasma confinement states in fusion power plants (FPPs) where reliable identification of the low-confinement (L-mode) and high-confinement (H-mode) regimes is critical for safe and efficient operation. Unlike research-oriented devices, FPPs must operate with a severely constrained set of diagnostics. To address this challenge, we demonstrate that a minimalist model, using only electron cyclotron emission (ECE) signals, can deliver accurate and reliable state classification. ECE provides electron temperature profiles without the engineering or survivability issues of in-vessel probes, making it a primary candidate for FPP-relevant diagnostics. Our framework employs ECE as input, extracts features with radial basis functions, and applies a gradient boosting classifier, achieving high accuracy with test accuracy averaging 96% correct predictions. Robustness analysis and feature importance study confirm the reliability of the approach. These results demonstrate that state-of-the-art performance is attainable from a restricted diagnostic set, paving the way for minimalist yet resilient
plasma control architectures for FPPs.
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All authors Randall Clark, Vacslav Glukhov, Georgy Subbotin, Maxim Nurgaliev, Aleksandr Kachkin, Max Austin, Dmitri M. Orlov
Plasma Physics
Structure of self-generated magnetic fields in laser-solid interaction from proton tomography
Jesse Griff-McMahon, Christopher A. Walsh, Vicente Valenzuela-Villaseca et al. (+9 more)
Strong
magnetic fields are naturally self-generated in high-power,
laser-solid interactions through the Biermann-battery mechanism. This work experimentally characterizes the 3D location and strength of these fields, rather than path-integrated quantities, through multi-view
proton radiography and tomographic inversion on the OMEGA
laser. We infer
magnetic fields that extend several millimeters off the target surface into the hot, rarefied corona and are sufficient to strongly magnetize the
plasma ($\Omega_{e}\tau_e \gg 1$). The data is used to validate MHD simulations incorporating recent improvements in
magnetic transport modeling; we achieve reasonable agreement only with models with re-localization of transport by
magnetic fields. This work provides a key demonstration of tomographic inversion in
proton radiography, offering a valuable tool for investigating
magnetic fields in
laser-produced
plasmas.
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All authors Jesse Griff-McMahon, Christopher A. Walsh, Vicente Valenzuela-Villaseca, Sophia Malko, Brendan McCluskey, Kirill Lezhnin, Huws Landsberger, Laura Berzak Hopkins, Gennady Fiksel, Michael J. Rosenberg, Derek B. Schaeffer, William Fox
Optics
Giant bias-free nonreciprocity for unpolarized light via synthetic motion
L.M. Máñez-Espina, B. Amrahi, I. Faniayeu et al. (+4 more)
Reciprocity breaking at optical frequencies typically relies on bulky magnets, dynamic modulation, or nonlinearities, all of which hinder chip-scale integration and the handling of unpolarised light. We introduce a fully passive, subwavelength metasurface that achieves polarisation-insensitive one-way transparency by combining self-magnetised ferrite nanodisks in a
vortex state with
symmetry-protected quasi-bound states in the continuum. The metasurface exhibits a pure synthetic moving-medium response at optical frequencies, yielding
giant nonreciprocal directional dichroism. We report near-unity values for both the transmittance contrast and the emissivity-to-absorptivity ratio with experimentally widely available ferrite materials, all under unpolarised illumination and without external bias. Using temporal coupled-mode theory, we identify the design conditions necessary to maximise directional dichroism: critical coupling, Huygens-type resonance overlap, and strong inter-mode coupling. Furthermore, we propose a deterministic, stamp-assisted protocol for imprinting arbitrary, uniform, or patterned
vortex configurations across large arrays of nanodisk meta-atoms, enabling scalable fabrication. This work establishes a practical route toward compact nonreciprocal photonics with applications in photonic gyrators, nonreciprocal wavefront engineering, and nonreciprocal
solar cell technologies.
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All authors L.M. Máñez-Espina, B. Amrahi, I. Faniayeu, R. Cichelero, A. Dmitriev, A. Díaz-Rubio, V.S. Asadchy
Optics
Demonstration of an interferometric technique for measuring vacuum magnetic birefringence with an optical cavity
Aaron D. Spector, Todd Kozlowski and Laura Roberts
Vacuum magnetic birefringence (VMB) is an effect predicted by
quantum electrodynamics, in which the
vacuum behaves as a non-linear optical medium and exhibit a birefringence in the presence of a
magnetic field. In this work, an interferometric scheme is introduced to measure this effect for the first time by sensing the changes in the frequencies of fields stabilized to the resonances of an optical cavity. Results are presented from a prototype setup with this novel sensing technique implemented on a 19 m test cavity without a
magnetic field. We propose using this scheme for a measurement of VMB with a 245 m long optical cavity and a string of 24 superconducting magnets, arranged for the ALPS II experiment. In this manuscript we examine potential sources of noise in the prototype, and project these results in terms of the sensitivity of the full-scale experiment.
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Computational Physics · Cross-listed from quant-ph
Quantum Search in Superposed Quantum Lattice Gas Automata and Lattice Boltzmann Systems
Călin A. Georgescu and Matthias Möller
As the scope of Computational Fluid Dynamics (CFD) grows to encompass ever larger problem scales, so does the interest in whether
quantum computing can provide an advantage. In recent years,
Quantum Lattice Gas Automata (QLGA) and
Quantum Lattice Boltzmann Methods (QLBM) have emerged as promising candidates for
quantum-native implementations of CFD solvers. Though the progress in developing QLGA and QLBM
algorithms has been significant, it has largely focused on the development of models rather than applications. As a result, the zoo of QLGA and QLBM
algorithms has grown to target several equations and to support many extensions, but the practical use of these models is largely limited to
quantum state
tomography and observable measurement. This limitation is crucial in practice, because unless very specific criteria are met, such measurements may cancel out any potential
quantum advantage. In this paper, we propose an application based on discrete optimization and
quantum search, which circumvents flow field measurement altogether. We propose methods for simulating many different
lattice configurations simultaneously and describe how the usage of amplitude estimation and
quantum search can provide an asymptotic
quantum advantage. Throughout the paper, we provide detailed complexity analyses of gate-level implementations of our
circuits and consider the benefits and costs of several encodings.
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Accelerator Physics
Three-Dimensional Simulation of the University of Hawai`i FEL Oscillator with Cavity Desynchronization
Amir Weinberg, Levi K.C. Fisher, Eremey Valetov et al. (+1 more)
In this paper, we present three-dimensional, time-dependent simulations of the University of Hawai`i (UH) at Mānoa free-electron
laser (FEL) oscillator. Using beam parameters from the UH facility, we study the pass-by-pass
evolution of the radiation field, including its temporal, spectral, and transverse properties. At nominal bunch length, the radiation pulse develops temporal spiking near saturation, together with sideband formation and increased sensitivity to machine timing jitter. Our results show that modest cavity desynchronization can enhance the radiation energy by 63%. Large cavity desynchronization, on the other hand, can effectively suppress the spiking instabilities and improve robustness to timing fluctuations. Finally, we simulate a short-bunch operational mode with a bunch length comparable to the slippage length, which accelerates saturation and further amplifies the FEL power. Overall, these results provide a quantitative foundation for pulse control studies in the UH FEL oscillator and a critical
benchmark for future experimental validation and machine optimization.
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All authors Amir Weinberg, Levi K.C. Fisher, Eremey Valetov, Siqi Li
Optics
Optical Computation-in-Communication enables low-latency, high-fidelity perception in telesurgery
Rui Yang, Jiaming Hu, Jian-Qing Zheng et al. (+12 more)
Artificial intelligence (AI) holds significant promise for enhancing intraoperative perception and decision-making in telesurgery, where physical separation impairs sensory feedback and control. Despite advances in medical AI and
surgical robotics, conventional electronic AI architectures remain fundamentally constrained by the compounded latency from serial processing of inference and communication. This limitation is especially critical in latency-sensitive procedures such as endovascular interventions, where delays over 200 ms can compromise real-time AI reliability and patient safety. Here, we introduce an Optical Computation-in-Communication (OCiC) framework that reduces end-to-end latency significantly by performing AI inference concurrently with optical communication. OCiC integrates Optical Remote Computing Units (ORCUs) directly into the optical communication pathway, with each ORCU experimentally achieving up to 69 tera-operations per second per channel through spectrally efficient two-dimensional photonic convolution. The system maintains ultrahigh inference fidelity within 0.1% of CPU/GPU baselines on classification and coronary angiography
segmentation, while intrinsically mitigating cumulative error propagation, a longstanding barrier to deep optical network scalability. We validated the robustness of OCiC through outdoor dark fibre deployments, confirming consistent and stable performance across varying environmental conditions. When scaled globally, OCiC transforms long-haul fibre infrastructure into a distributed photonic AI fabric with exascale potential, enabling reliable, low-latency telesurgery across distances up to 10,000 km and opening a new optical frontier for distributed medical intelligence.
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All authors Rui Yang, Jiaming Hu, Jian-Qing Zheng, Yue-Zhen Lu, Jian-Wei Cui, Qun Ren, Yi-Jie Yu, John Edward Wu, Zhao-Yu Wang, Xiao-Li Lin, Dandan Zhang, Mingchu Tang, Christos Masouros, Huiyun Liu, Chin-Pang Liu
Accelerator Physics
Beam-beam
Xavier Buffat
The interaction of the two beams in a
collider leads to a variety of effects that may limit the performance of the machine. This lecture introduces the basic aspects necessary to understand the design of modern
colliders.
Physics Education
Using Unguided Peer Collaboration to Facilitate Early Educators' Pedagogical Development: An Example from Physics TA Training
Apekshya Ghimire and Chandralekha Singh
Many early career educators, such as teaching assistants (TAs) in college courses, as well as pre-college educators, need help both with content and pedagogical knowledge to effectively help their students learn. One pedagogical approach that has been found effective in prior studies is collaboration with peers. Collaborative learning not only has the potential to help educators develop content knowledge but can also improve their pedagogical knowledge. This study examines the performance of physics graduate students, enrolled in a professional development course for teaching assistants (TAs), on the Magnetism Conceptual Survey, highlighting the impact of peer collaboration on learning both content and pedagogy. Peer interaction significantly improved performance, driven by both construction of knowledge (where the group answered a question correctly but only one member had the correct individual response) and co-construction of knowledge (where the group succeeded despite both members initially answering incorrectly). Beyond improving content understanding, peer collaboration can also foster pedagogical skills by encouraging early educators such as TAs to use peers as learning resources and communicate ideas effectively to support mutual understanding. These dual benefits-enhancing both content mastery and teaching abilities-demonstrate that this approach holds value not only for the professional development of TAs but can also be adapted for pre-college professional development programs to improve teaching and learning outcomes.
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Physics Education
Introductory Physics Students in Algebra-based Courses Who Typically Worked Alone or in Groups: Insights from Gender-Based Analysis before and during COVID-19
Apekshya Ghimire and Chandralekha Singh
Collaboration with peers both inside and outside the classroom can be an invaluable tool for helping students learn physics. We investigated the impact of peer collaboration on learning physics by examining the characteristics of women and men who typically worked alone versus those who typically collaborated with peers in their
algebra-based introductory physics course when they took the course before and during the COVID-19 pandemic when the classes were on Zoom. Our findings indicate that, on average, students who worked with peers had higher grades and reported greater peer influence on their physics self-efficacy during the pandemic compared to those who worked alone. We also observed that, for both women and men, a larger percentage of students typically worked in groups before the pandemic, while a greater percentage typically worked alone during the pandemic. We discuss these results in relation to students' prior academic preparation, physics grades, self-efficacy and their perception of the effectiveness of peer collaboration on their physics self-efficacy.
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Optics
Nonlinear near-field spectroscopy of exciton-polaritons in a van der Waals layered waveguide
Valeriy I. Kondratyev, Vanik Shahnazaryan, Mikhail Tyugaev et al. (+6 more)
Layered van der Waals materials offer novel opportunities for on-chip waveguiding and development of integrated photonic
circuits. In the strong light-matter coupling regime, their nonlinear response can be significantly enhanced, which is crucial for developing active photonic devices. However, probing the nonlinearity of waveguide modes in subwavelength-thick structures is challenging as they are not directly accessible from far-field. Here we apply a novel nonlinear near-field spectroscopic technique based on a GaP solid immersion lens and femtosecond
laser excitation to study nonlinearity of guided modes in monolayer WS$_2$ encapsulated in hBN under the strong light-matter coupling regime. We reveal formation of
exciton-polaritons with $\sim 50$ meV Rabi splitting and demonstrate a pump-induced transition from strong to weak coupling. Our results show that
exciton resonance saturation and broadening lead to an efficient nonlinear response of guided polaritons, which can be employed for developing compact van der Waals photonic switches and modulators.
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All authors Valeriy I. Kondratyev, Vanik Shahnazaryan, Mikhail Tyugaev, Tatyana V. Ivanova, Ivan E. Kalantaevskii, Dmitry V. Permyakov, Ivan V. Iorsh, Anton K. Samusev, Vasily Kravtsov
Optics
Nonlinear dynamics in breathing-soliton lasers
Junsong Peng, Xiuqi Wu, Huiyu Kang et al. (+5 more)
We review recent advances in the study of nonlinear dynamics in mode-locked fibre
lasers operating in the breathing (pulsating)
soliton regime. Leveraging advanced diagnostics and control strategies – including genetic
algorithms – we uncover a rich spectrum of dynamical behaviours, including frequency-locked breathers, fractal Farey hierarchies, Arnold tongues with anomalous features, and breather molecular complexes. We also identify a novel route to chaos via modulated subharmonic states. These findings underscore the utility of fibre
lasers as model systems for exploring complex dissipative dynamics, offering new opportunities for ultrafast
laser control and fundamental studies in nonlinear science.
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All authors Junsong Peng, Xiuqi Wu, Huiyu Kang, Anran Zhou, Ying Zhang, Heping Zeng, Christophe Finot, Sonia Boscolo
Fluid Dynamics · Cross-listed from astro-ph.SR
Conversion and Damping of Nonaxisymmetric Internal Gravity Waves in Magnetized Stellar Cores
Cy S. David, Daniel Lecoanet and Pascale Garaud
Magnetism is thought to play an important role in the
evolution and dynamics of
stars, though little is known about
magnetic fields deep within
stellar interiors. A promising avenue for probing these fields uses asteroseismic observations of global oscillations that result from the coupling of acoustic waves in the convective zone to internal
gravity waves (IGWs) in the radiative interior. Recent modeling efforts implicate deep
magnetic fields in the suppression of dipole mixed modes observed in 20% of red
giants and a number of high-mass main sequence
stars. Previous numerical and theoretical work shows that core
magnetic fields could suppress axisymmetric global modes by refracting down-going IGWs into slow-magnetosonic (SM) waves that damp at
magnetic cutoff heights. Here, we extend these results to the nonaxisymmetric case, for which the IGWs and SM waves are coupled to a continuous spectrum of Alfven waves (AWs). We consider a Cartesian model of the radiative interior with uniform stratification and a spatially-varying, current-free
magnetic field. Using a Wentzel-Kramers-Brillouin approximation to solve for the vertical mode structure, corroborated with numerical simulations, we show that IGWs convert to up-going SM waves, which resonate with the Alfven spectrum and produce mixed SM-AW modes. We find cutoff heights (as in the axisymmetric case), above which the SM/SM-AWs convert to AWs. Latitudinal variations of the background
magnetic field lead to phase mixing of the AWs, resulting in rapid damping. Our results suggest that energy in both axisymmetric and nonaxisymmetric IGWs is lost via interactions with a strong
magnetic field.
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Chemical Physics
Quantized Skeletal Learning (QSL): A Differentiable Programming Approach for Skeletal Reduction of Chemical Mechanisms
Opeoluwa Owoyele
This paper presents a data-driven approach, referred to as Quantized Skeletal Learning (QSL), for generating skeletal mechanisms. The approach has two key components: (1) a weight vector that can be used to eliminate relatively unimportant species and reactions, and (2) an end-to-end differentiable program whose loss-function gradients, with respect to the weight vector, can be used to adjust those weights. To promote sparsity in the weight vector – and to reduce the influence of certain reactions or species to zero – an $l_1$-regularized objective is employed alongside the standard mean squared error loss, thus removing the least important components. The proposed QSL approach is validated by generating skeletal mechanisms for methane and ethylene based on the GRI 3.0 and USC II mechanisms, respectively, demonstrating effectiveness in deriving skeletal mechanisms with various levels of fidelity. Two variants of QSL, designated as QSL-R and QSL-S, are tested; these focus on eliminating reactions and species, respectively. Analysis of ignition delay times and species mass fractions demonstrate QSL's capabilities to reliably and efficiently extract data-driven skeletal mechanisms of varying fidelities from detailed mechanisms.
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Applied Physics
Impact of irradiation conditions on the magnetic field sensitivity of spin defects in hBN nano flakes
Saksham Mahajan, Ravi Kumar, Aferdita Xhameni et al. (+8 more)
We study $V_{\mathrm{B}}^-$ centres generated by helium focused ion beam (FIB) irradiation in thin ($\sim$70 nm) hBN nanoflakes, in order to investigate the effect of implantation conditions on the key parameters that influence the
magnetic field sensitivity of $V_{\mathrm{B}}^-$
quantum sensors. Using a combination of photoluminescence, optically detected
magnetic resonance, and Raman spectroscopy, we examine the competing factors of maximising signal intensity through larger $V_{\mathrm{B}}^-$ concentration against the degradation in
spin coherence and
lattice quality observed at high ion fluences. Our results indicate that both the $V_{\mathrm{B}}^-$
spin properties and hBN
lattice parameters are largely preserved up to an ion fluence of $10^{14}$ ions/cm$^2$, and beyond this significant degradation occurs in both. At the optimal implantation dose, an AC
magnetic sensitivity of $\sim 1\,\mu\mathrm{T}/\sqrt{\mathrm{Hz}}$ is achieved. Using the patterned implantation enabled by the FIB, we find that $V_{\mathrm{B}}^-$ centres and the associated
lattice damage are well localised to the implanted regions. This work demonstrates how careful selection of fabrication parameters can be used to optimise the properties of $V_{\mathrm{B}}^-$ centres in hBN, supporting their application as
quantum sensors based on 2D materials.
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All authors Saksham Mahajan, Ravi Kumar, Aferdita Xhameni, Gautham Venu, Basanta Mistri, Felix Donaldson, T.Taniguchi, K. Watanabe, Siddharth Dhomkar, Antonio Lombardo, John J.L. Morton
Computational Physics
Surrogate Models for Linear Response
L. Jin, A. Ravlić, P. Giuliani et al. (+2 more)
Linear response theory is a well-established method in physics and chemistry for exploring excitations of many-body systems. In particular, the quasiparticle random-phase approximation (QRPA) provides a powerful microscopic framework by building excitations on top of the mean-field
vacuum; however, its high computational cost limits model calibration and uncertainty quantification studies. Here, we present two complementary QRPA surrogate models and apply them to study response functions of finite
nuclei. One is a reduced-order model that exploits the underlying QRPA structure, while the other utilizes the recently developed parametric
matrix model
algorithm to construct a map between the system's
Hamiltonian and observables. Our
benchmark applications, the calculation of the electric dipole polarizability of ${}^{180}$Yb and the $\beta$-decay half-life of ${}^{80}$Ni, show that both emulators can achieve 0.1%–1% accuracy while offering a six to seven orders of magnitude speedup compared to state-of-the-art QRPA solvers. These results demonstrate that the developed QRPA emulators are well-positioned to enable
Bayesian calibration and large-scale studies of computationally expensive physics models describing the properties of many-body systems.
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All authors L. Jin, A. Ravlić, P. Giuliani, K. Godbey, W. Nazarewicz
Medical Physics · Cross-listed from cs.LG
Distributional Consistency Loss: Beyond Pointwise Data Terms in Inverse Problems
George Webber and Andrew J. Reader
Recovering true signals from noisy measurements is a central challenge in inverse problems spanning medical imaging, geophysics, and signal processing. Current methods balance prior signal priors (regularization) with agreement with noisy data (data-fidelity). Conventional data-fidelity loss functions, such as mean-squared error (MSE) or negative log-likelihood, seek pointwise agreement with noisy measurements, often leading to overfitting to noise. In this work, we instead evaluate data-fidelity collectively by testing whether the observed measurements are statistically consistent with the noise distributions implied by the current estimate. We introduce distributional consistency (DC) loss, a data-fidelity objective that replaces pointwise matching with distribution-level calibration. DC loss acts as a direct and practical plug-in replacement for standard data consistency terms: i) it is compatible with modern unsupervised regularizers that operate without paired measurement-ground-truth data, ii) it is optimized in the same way as traditional losses, and iii) it avoids overfitting to measurement noise without early stopping or priors. Its scope naturally fits many practical inverse problems where the measurement-noise distribution is known and where the measured
dataset consists of many independent noisy values. We demonstrate efficacy in two key example application areas: i) in image denoising with deep image prior, using DC instead of MSE loss removes the need for early stopping and achieves higher PSNR; ii) in medical image reconstruction from Poisson-noisy data, DC loss reduces artifacts in highly-iterated reconstructions and enhances the efficacy of hand-crafted regularization. These results position DC loss as a statistically grounded, performance-enhancing alternative to conventional fidelity losses for an important class of unsupervised noise-dominated inverse problems.
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Data Analysis and Statistics · Cross-listed from astro-ph.CO
Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning
Kyle Boone, Georgios Valogiannis, Marco Gatti et al. (+1 more)
We present forecasts for constraints on the matter density ($\Omega_m$) and the amplitude of matter density fluctuations at 8h$^{-1}$Mpc ($\sigma_8$) from CMB
lensing convergence maps and
galaxy weak
lensing convergence maps. For CMB
lensing convergence auto statistics, we compare the angular power spectra ($C_\ell$'s) to the wavelet scattering transform (WST) coefficients. For CMB
lensing convergence $\times$
galaxy weak
lensing convergence statistics, we compare the cross angular power spectra to wavelet phase harmonics (WPH). This work also serves as the first application of WST and WPH to these probes. For CMB
lensing convergence, we find that WST and $C_\ell$'s yield similar constraints in forecasts for all surveys considered in this work. When CMB
lensing convergence is crossed with
galaxy weak
lensing convergence projected from $\textit{Euclid}$ Data Release 2 (DR2), we find that WPH outperforms cross-$C_\ell$'s by factors between $2.2$ and $3.4$ for individual parameter constraints. To compare these different summary statistics, we develop a novel learned binning approach. This method compresses summary statistics while maintaining interpretability. We find this leads to improved constraints compared to more naive binning schemes for our wavelet-based statistics, but not for $C_\ell$'s. By learning the binning and measuring constraints on distinct data sets, our method is robust to overfitting by construction.
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All authors Kyle Boone, Georgios Valogiannis, Marco Gatti, Cora Dvorkin
Plasma Physics · Cross-listed from hep-ph
No cosmological constraints on dark photon dark matter from resonant conversion: Impact of nonlinear plasma dynamics
Anson Hook, Junwu Huang and Mohamad Shalaby
We revisit and invalidate all dark
photon dark matter constraints from resonant conversion of dark
photons into
photons (plasmons) in the early universe. These constraints rely on the resonant transfer of a substantial portion of the dark
photon energy density into the SM
plasma, heating the
plasma in the process. We demonstrate that this resonant transfer saturates because of
plasma nonlinearities. Dark
photon dark matter resonantly converts into $k \simeq 0$ Langmuir waves in the early universe electron-ion
plasma. Once the Langmuir-wave energy approaches the thermal energy of the
plasma, nonlinear effects driven by the ponderomotive force become significant. In particular, we show using dedicated Particle-in-
Cell simulations that large-amplitude $k = 0$ Langmuir waves excite higher-k Langmuir and ion acoustic waves, producing strong spatial variations in density and
plasma frequency. These inhomogeneities suppress further resonant conversion, limiting the deposited energy to about the thermal energy of the electrons at the time of conversion, orders of magnitude below observable cosmological thresholds. Consequently, the dark
photon dark matter constraints are weaker by factors of $3000$ to $10^7$ across ten orders of magnitude in dark
photon mass.
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Plasma Physics · Cross-listed from astro-ph.HE
Bell Instability and Cosmic-Ray Acceleration in Active Galactic Nuclei Ultrafast Outflow Shocks
Rei Nishiura and Tsuyoshi Inoue
We investigate
magnetic-field amplification driven by the nonresonant hybrid (NRH or Bell) instability and its impact on
cosmic-ray (CR) acceleration at reverse shocks of ultrafast
outflows (UFOs) from active
galactic nuclei (AGN). Previous kinetic studies by particle-in-
cell simulations have demonstrated that when maximum CR energy is near the injection scale, NRH instability efficiently amplifies
magnetic field up to the saturation level. However, the efficiency of NRH instability goes down as maximum energy increase since CR current is carried by escaping CRs near the maximum energy. We employ a one-dimensional MHD–CR framework solving telegraph-type
diffusion–convection equations to trace the coupled
evolution of CRs,
magnetic fields, and shock dynamics under realistic parameters. We find a distinct transition with
magnetic field strength: for weak background fields ($B_{0}\!\lesssim\!10^{-4}\,\mathrm{G}$), NRH instability efficiently amplifies upstream
turbulence, driving a self-regulated state where $E_{\max}$ becomes independent of initial strength of
magnetic turbulence. In contrast, for stronger background fields ($B_{0}\!\gtrsim\!10^{-3}\,\mathrm{G}$), the escaping CR current is too weak to drive NRH instability, and
magnetic turbulence further decays through parametric instabilities, potentially reducing the acceleration efficiency. We give the physical interpretation for the transition and discuss conditions for PeV–EeV acceleration at UFO reverse shocks.
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History and Philosophy of Physics
Is Gravity Truly Balanced? A Historical-Critical Journey Through the Equivalence Principle and the Genesis of Spacetime Geometry
Jaume de Haro and Emilio Elizalde
We present a novel derivation of the spacetime metric generated by matter, without invoking Einstein's field equations. For static sources, the metric arises from a relativistic formulation of D'Alembert's principle, where the inertial force is treated as a real dynamical entity that exactly compensates
gravity. This leads to a conformastatic metric whose geodesic equation, parametrized by proper time, reproduces the relativistic version of Newton's second law for free fall. To extend the description to moving matter, uniformly or otherwise, we apply a Lorentz transformation to the static metric. The resulting non static metric accounts for the motion of the sources and, remarkably, matches the weak field limit of general relativity as obtained from the linearized Einstein equations in the de Donder or Lorenz gauge. This approach, at least at
Solar System scales, where gravitational fields are weak, is grounded in a new dynamical interpretation of the Equivalence Principle. It demonstrates how
gravity can emerge from the relativistic structure of inertia, without postulating or solving Einstein's equations.
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Physics Education
How often does unguided peer interaction lead to correct response consensus? An example from Conceptual Survey of Electricity and Magnetism
Apekshya Ghimire and Chandralekha Singh
In this research, we investigated the impact of peer collaboration and changes from individual to group performance of graduate students on the Conceptual Survey of Electricity and Magnetism (CSEM) without any guidance from the instructor. We define construction of knowledge as a case in which the group answered the question correctly but in the individual administration of the survey before the group work, one member gave the correct answer and the other gave incorrect answer. We find that there was a significant improvement in the performance of students after peer interaction, which was mostly attributed to construction of knowledge. However, students had very few opportunities to co-construct knowledge as there were hardly any situations in which neither student in a group provided a correct answer. We analyzed the effect size for improvement from individual to group scores for each CSEM item to understand the characteristics of these questions that led to productive group interaction. We also compared the group performance of the graduate students to the introductory physics students in a prior study using the CSEM to get insight into the concepts that showed differences for the two groups and those that were challenging for both groups of students before and after collaboration with peers. Our findings can motivate physics instructors to incorporate group interactions both inside and outside of the classroom even without instructor's involvement so that students at all levels can learn from each other and develop a functional understanding of the underlying concepts.
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Data Analysis and Statistics · Cross-listed from q-fin.ST
Multifractality and its sources in the digital currency market
Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień et al. (+1 more)
Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency
markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate
market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of
market inefficiencies, may improve
volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, in the present contribution a recently proposed method of disentangling sources of multifractality in time series was applied to the most representative instruments from the digital
market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible
tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality are temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency
market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series.
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All authors Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Optics
Ultracompact high-Q whispering gallery mode microresonator in a non-closed waveguide path
Ziyang Xiong, Tong Lin, Liu Li et al. (+6 more)
Integrated photonic
circuits are foundational for versatile applications, where high-performance traveling-wave optical resonators are critical. Conventional whispering-gallery mode microresonators (WGMRs) confine light in closed-loop waveguide paths, thus inevitably occupy large footprints. Here, we report an ultracompact high loaded Q silicon photonic WGMR in an open curved path instead. By leveraging spatial mode multiplexing, low-loss mode converter-based photonic routers enable reentrant
photon recycling in a single non-closed waveguide. The fabricated device achieves a measured loaded Q-factor of 1.78*10^5 at 1554.3 nm with a 1.05 nm free spectral range in a ultracompact footprint of 0.00137 mm^2-6*smaller than standard WGMRs while delivering 100*higher Q-factor than photonic
crystal counterparts. This work pioneers dense integration of high-performance WGMR arrays through open-path mode recirculation.
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All authors Ziyang Xiong, Tong Lin, Liu Li, Hao Deng, Haoran Wang, Yan Fan, Shihua Chen, Junpeng Lu, Zhenhua Ni
Atomic Physics
Splitting Isotope Shift in the $1s2p\,^3\!P_{0,1,2}$ Fine-Structure Triplet in $^{12,13,14}$C$^{4+}$: Experiment and Theory
Patrick Müller, Kristian König, Emily Burbach et al. (+6 more)
We report measurements and theoretical calculations of the fine-structure splittings in all three $1s2s\,^3\!S_1\rightarrow\,1s2p\,^3\!P_{0,1,2}$ transitions in the heliumlike systems of the isotopes $^{12,13,14}$C. The metastable triplet state was efficiently populated in an electron beam ion source and the C$^{4+}$ ions were electrostatically accelerated to 50\,keV to perform collinear
laser spectroscopy. From the determined transition frequencies, the splitting isotope shift (SIS), i.e., the difference in fine-structure splittings between different isotopes of the same element, was extracted. In the SIS, theoretical uncertainties due to higher-order
quantum electrodynamic corrections are strongly suppressed since they are independent of both
nuclear mass and the fine-structure
quantum number $J$ in lowest order. Comparison with theory provides an important test of experimental accuracy, particularly in the $^{13}$C$^{4+}$ case, for which the
nuclear spin leads to hyperfine-induced fine-structure mixing. At the same time, the even-even isotopes $^{12,14}$C$^{4+}$ without
nuclear spin can be used to confirm theory. Theoretical values of the SIS are given for all the heliumlike ions with $2\le Z\le 10$.
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All authors Patrick Müller, Kristian König, Emily Burbach, Gordon W. F. Drake, Phillip Imgram, Bernhard Maaß, Titamarie M. Maggio, Wilfried Nörtershäuser, Julien Spahn
Optics · Cross-listed from cond-mat.mes-hall
Optical Response of Graphene Quantum Dots in the Visible Spectrum: A Combined DFT-QED Approach
J. Olivo, J. Blengino Albrieu and Mauro Cuevas
We propose a model based on density functional theory (DFT) and
quantum electrodynamics (QED) to study the dynamical characteristics of
graphene quantum dots (GQDs). We assume the GQD edges are saturated with hydrogen atoms, effectively making it a polycyclic aromatic hydrocarbon (PAH) such as coronene. By combining the GQD spectrum calculated from a time-dependent DFT (TDDFT) with the dynamical behavior of a QD model derived from QED, we calculate the main optical characteristics of the GQD, such as its transition frequencies, the dipole moment associated to each of those transitions, life-time and the population dynamics of the molecular levels. Owing to the close match between the calculated spectrum and experimental results, our results represent a significant contribution to research on
quantum treatments of light-matter interactions in realistic 2D nanomaterials.
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Fluid Dynamics
Turbulent transport for wall shear stress fluctuations
Myoungkyu Lee and Yongyun Hwang
Statistical structure and the underlying energy budget of wall shear stress fluctuations are studied in both Poiseulle and Couette flows with emphasis on its streamwise component. Using a dimensional analysis and direct numerical simulation data, it is shown that the spectra of streamwise wall dissipation for $\lambda \lesssim 1000 \delta_\nu$ are asymptotically invariant with the Reynolds number ($Re$), whereas those for $\lambda \gtrsim \delta$ decay with $Re$ (here, $\lambda$ is a nominal wall-parallel wavelength, and $\delta_\nu$ and $\delta$ are the viscous inner and outer length scales, respectively). The wall dissipation increases with $Re$ due to the increasing contribution of the spectra at $1000 \delta_\nu \lesssim \lambda \lesssim \delta$. The subsequent analysis of the energy budget shows that the near-wall motions associated with these wall dissipation spectra are mainly driven by turbulent transport and are `inactive' in the sense that they contain very little Reynolds shear stress (or
turbulence production). As such, turbulent transport spectra near the wall are also found to share the same $Re$-scaling behaviour with wall dissipation, and this is observed in the spectra of both the wall-normal and inter-scale turbulent transports. The turbulent transport underpinning the increase of wall dissipation with $Re$ is characterised by energy fluxes towards the wall, together with inverse energy transfer from small to large length scales along the wall-parallel directions.
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Fluid Dynamics
Efficient Wall-Modelled Large Eddy Simulation of Rotors using Homogenized Lattice Boltzmann Methods
Adrian Kummerländer, Shota Ito, Maximilian Schecher et al. (+4 more)
Accurately capturing the dynamic forces acting on rotors as well as their wake effects presents a significant challenge for computational fluid dynamics (CFD) due to high Reynolds numbers and a large range of spatio-temporal scales. The present work proposes a novel blade-resolved wall-modeled large eddy simulation (WMLES) approach based on the
lattice Boltzmann method (LBM). A homogenized hybrid regularized recursive collision scheme targeting the filtered Brinkman–Navier–Stokes equations is combined with a novel wall-model. This is implemented in the context of a platform-transparent framework for fluid-structure interaction in the open source LBM framework OpenLB. Convergence order and accuracy are validated against both experimental and numerical data for a model wind turbine, demonstrating excellent agreement for integral forces and wake velocity profiles. Computational efficiency and parallel scalability was investigated by roofline analysis and weak scaling studies for up to 384 rotors resolved by 41 billion
lattice cells on the Karolina supercomputer. The proposed framework enables efficient blade-resolved WMLES of entire wind farms and offers a new methodology for other complex wall-modeled fluid-structure interaction applications.
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All authors Adrian Kummerländer, Shota Ito, Maximilian Schecher, Davide Dapelo, Stephan Simonis, Mathias J. Krause, Fedor Bukreev
Accelerator Physics
Comparative Evaluation of Xilinx RFSoC Platform for Low-Level RF Systems
Shreeharshini Dharanesh Murthy, Victoria Moore, Qiang Du et al. (+5 more)
The rapid advancement of
Radio Frequency System-onChip (RFSoC) technology from Xilinx (AMD) has enabled the integration of high-speed data converters and programmable logic within a single package. RFSoC platforms are already widely adopted in telecommunications, radar, and satellite communications, where they promise reductions in system footprint and power consumption. However, their suitability for Low-Level RF (LLRF) control systems in accelerator environments - where stability requirements are critical - has not been quantitatively evaluated. This paper presents a comparative measurement-based assessment of RFSoC-based and conventional LLRF designs, focusing on signal fidelity, phase noise, latency, system complexity, and integration challenges. The advantages and challenges of adopting RFSoC-based direct conversion architectures are discussed, providing guidance for future LLRF system implementations.
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All authors Shreeharshini Dharanesh Murthy, Victoria Moore, Qiang Du, Angel Jurado, Michael Chin, Keith Penney, David Nett, Benjamin Flugstad
Chemical Physics
SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles
Gregor N. C. Simm, Jean Hélie, Hannes Schulz et al. (+13 more)
Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional force fields often lack the accuracy and transferability required to capture the intricate interactions governing
polymer behavior. Conversely,
quantum-chemical methods are computationally prohibitive for the large systems and long timescales required to simulate relevant
polymer phenomena. Here, we overcome these limitations with a machine learning force field (MLFF) approach. We demonstrate that macroscopic properties for a broad range of
polymers can be predicted ab initio, without fitting to experimental data. Specifically, we develop a fast and scalable MLFF to accurately predict
polymer densities, outperforming established classical force fields. Our MLFF also captures second-order phase transitions, enabling the prediction of glass transition temperatures. To accelerate progress in this domain, we introduce a
benchmark of experimental bulk properties for 130
polymers and an accompanying
quantum-chemical
dataset. This work lays the foundation for a fully in silico design pipeline for next-generation polymeric materials.
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All authors Gregor N. C. Simm, Jean Hélie, Hannes Schulz, Yicheng Chen, Guillem Simeon, Anna Kuzina, Ernesto Martinez-Baez, Piero Gasparotto, Gabriele Tocci, Chi Chen, Yatao Li, Lixue Cheng, Zun Wang, Bichlien H. Nguyen, Jake A. Smith, Lixin Sun