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Physics Physics

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Thursday 23 April

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

Electrically switchable vacancy state revealed by in-operando positron experiments

Ric Fulop, Laurence Lyons IV, Robert Nick et al. (+6 more)

Whether the flash state in electrically driven solids involves non-equilibrium defect production or is accounted for by Joule heating alone has been debated since 2010. Using positron annihilation spectroscopy on copper, we observe a fully reversible, electrically switchable vacancy population: the DBS S-parameter rises above baseline whenever applied current exceeds a critical density and returns on current removal. Positron lifetime spectroscopy independently confirms open-volume defect formation and reveals a void to cluster relaxation hierarchy. The current-induced vacancy concentration exceeds the thermal-equilibrium value at 352C by > 106x, is present only while current is applied, and vanishes within minutes. The nucleation rate scales steeply with the applied current, connecting the minute-scale kinetics resolved here to the sub-second flash events observed in ceramic sintering. These results demonstrate current-induced Frenkel-pair production in a metal and identify a defect-mediated, non-equilibrium contribution to the flash state.
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All authors Ric Fulop, Laurence Lyons IV, Robert Nick, Marc H. Weber, Ming Liu, Haig Atikian, Uwe Bauer, Alexander C. Barbati, Neil Gershenfeld

Optics

Coherence toroidal vortices and statistic-veiled correlation topologies

Keyu Zhou, Yaning Zhou, Ao Zhou et al. (+7 more)

Toroidal vortices in fluid and gas dynamics underpin a broad spectrum of scientific and technological fields, from elementary particle physics to condensed matter systems, and have recently garnered significant attention in optics because of their inherent topological stability. Here we report the experimental observation of toroidal vortices in stochastic optical wavefields with partial coherence, termed coherence toroidal vortices, which eliminates deterministic topological signatures in conventional optical degrees of freedom while unveiling statistically hidden correlation topologies. These underlying topologies-including both fundamental and higher-order hopfionic textures-emerge exclusively in second-order field correlations and are accessible only through statistical measurements. We further examine the impact of chaotic channels on the stability of these statistically veiled correlation topologies, demonstrating that their topological invariants remain robust under realistic environmental perturbations. These findings are experimentally validated and offer novel insights into the potential of toroidal light vortices serving as controllable channels for directional energy and information transfer within complex media.
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All authors Keyu Zhou, Yaning Zhou, Ao Zhou, Zhao Zhang, Jinzhan Zhong, Houan Teng, Chunhao Liang, Qiwen Zhan, Yangjian Cai, Xin Liu

Applied Physics · Cross-listed from math-ph

How it cools? Studying the heat flow out of a semi-infinite slab in welding: An analytical approach

Fawzi Aly, Alex Kitt and Luke Mohr

Additive manufacturing and welding processes are highly sensitive to heat dissipation, where improper thermal management leads to residual stresses, distortions, and cracking. Existing heat transfer models, such as Rosenthal's solutions, fail to handle finite 3D geometries, cooling effects, or transient behavior, limiting their accuracy. We overcome these limitations by developing an analytical framework that incorporates cooling boundary conditions mimicking Newton's Law of Cooling. Using two different and proven-equivalent approaches, Laplace transform and Fourier series, we derive closed-form solutions for transient and steady-state temperature profiles under various heat sources, including Gaussian, ellipsoidal, double-ellipsoidal, and time-dependent on/off switch sources. We compare our analytical solutions to numerical implementations, demonstrating strong agreement while providing deeper physical insight. This approach significantly reduces computational cost and experimental requirements, making it a scalable tool for optimizing thermal predictions and mitigating residual stresses in metal-based manufacturing. Additionally, our framework enables the generation of synthetic datasets for machine learning models to predict heat distribution efficiently.
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Optics

Programming Coherent and Quantum Light with a Free-Electron Wavepacket

Songyu Zhu, Yushan Zeng, Chenhao Pan et al. (+3 more)

The pursuit of compact, programmable light sources with high coherence and spectral purity hinges on establishing a precise set of phase relationships in light-matter interactions. Here, we demonstrate that the quadratic dispersion of freely propagating electron wavepacket serves as a programmable quantum medium. Prepared in a coherent momentum-state ladder via a single laser interaction, the electron subsequently undergoes deterministic phase evolution during free propagation-an intrinsic process that compiles its quantum state into two distinct emission channels. This mechanism, quantified by a quantum bunching factor, enables: (i) Talbot-resonant bunching, where the electron density self-structures into sub-cycle combs with tunable harmonic selectivity, and (ii) coherent phase transfer of the programmed quadratic phase to light, generating nonclassical photon states such as multi-component Schrodinger cat states via measurement-conditioned interaction. This quadratic-phase programming establishes a versatile platform for on-demand quantum state synthesis, bridging beam engineering with electron wavefunction shaping for compact quantum light sources, coherent radiation control, and scalable quantum information processing.
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All authors Songyu Zhu, Yushan Zeng, Chenhao Pan, Yiming Pan, Ye Tian, Ruxin Li

Fluid Dynamics

On the role of inertia and self-sustaining mechanism in two-dimensional elasto-inertial turbulence

Haotian Cheng, Hongna Zhang, Wenhua Zhang et al. (+3 more)

Elasto-inertial turbulence (EIT) is primarily driven by polymer elasticity, yet the modulating role of fluid inertia is non-negligible and remains largely unexplored. To investigate the effect of inertia, we perform direct numerical simulations of two-dimensional EIT in channel flow over a wide range of Reynolds numbers ($Re$). We show that increasing inertia promotes both the enhancement of dynamic amplitudes and the wallward migration of core structures. Specifically, inertia intensifies the turbulent fluctuations, facilitates the fragmentation of large-scale structures, and amplifies statistical quantities such as the root-mean-square of velocity fluctuations and polymer extension. The peak location of nonlinear elastic shear stress follows a scaling law $y^+ \propto Re_\tau^{1/2}$, closely resembling that of Reynolds shear stress in Newtonian turbulence, indicating a change of the momentum transfer mechanism. Meanwhile, the peak location of energy conversion between elastic and turbulent kinetic energies exhibits a $y^+ \propto Re_\tau^{0.1}$ scaling law migration, remaining mostly confined to the near-wall region. Remarkably, despite the inertial modulation, the probability density functions (PDFs) of velocity and elastic stress fluctuations extracted at the energy-conversion peak collapse convincingly over the range of $Re$ investigated. This reveals a robust statistical self-similarity across a wide range of inertia magnitude. Furthermore, the PDFs of wall-normal velocity and elastic stress fluctuations exhibit pronounced exponential heavy tails.
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All authors Haotian Cheng, Hongna Zhang, Wenhua Zhang, Yuke Li, Xiaobin Li, Fengchen Li

Optics

Ultra-low-noise supercontinuum in normal-dispersion ZBLAN fibres pumped at 1.85 $\mu$m

Shreesha Rao D. S., Anupamaa Rampur, Ole Bang et al. (+1 more)

We demonstrate, for the first time to our knowledge, ultra-low-noise supercontinuum (SC) generation in normal-dispersion fluoride fibres pumped by femtosecond (fs) pulses. We have investigated two elliptical-core polarisation-maintaining (PM) ZBLAN fibres with core dimensions 6.7$\times$2.7 $\mu$m and 8.9$\times$4.1 $\mu$m, experimentally measured to have normal dispersion up to 3.77 $\mu$m and 3.25 $\mu$m, respectively; the smaller-core fibre yields ultra-low-noise SC spanning 1.537-2.196 $\mu$m with a minimum relative-intensity noise (RIN) of 0.22% at 1.7 $\mu$m, and the larger-core fibre yields 1.507-2.250 $\mu$m with 0.36% at 2.0 $\mu$m. To aid the generation of low-noise SC, we developed an all-PM thulium chirped-pulse amplifier delivering 58 fs pulses at 1.85 $\mu$m, 210 mW average power at 40 MHz, with 0.41% RIN, seeded by a part of an ultra-low-noise SC using a 1.55 $\mu$m fs laser and an all-normal-dispersion (ANDi) silica fibre for precise seed control. These results establish a robust, alignment-free pathway to extend ultra-low-noise ANDi-fibre SC towards the mid-infrared using PM fluoride fibres.
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All authors Shreesha Rao D. S., Anupamaa Rampur, Ole Bang, Alexander M. Heidt

Atmospheric and Oceanic Physics

Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2

Shady E. Ahmed, Hui Wan, Saad Qadeer et al. (+5 more)

Toward the goal of using Scientific Machine Learning (SciML) emulators to improve the numerical representation of aerosol processes in global atmospheric models, we explore the emulation of aerosol microphysics processes under cloud-free conditions in the 4-mode Modal Aerosol Module (MAM4) within the Energy Exascale Earth System Model version 2 (E3SMv2). To develop an in-depth understanding of the challenges and opportunities in applying SciML to aerosol processes, we begin with a simple feedforward neural network architecture that has been used in earlier studies, but we systematically examine key emulator design choices, including architecture complexity and variable normalization, while closely monitoring training convergence behavior. Our results show that optimization convergence, scaling strategy, and network complexity strongly influence emulation accuracy. When effective scaling is applied and convergence is achieved, the relatively simple architecture, used together with a moderate network size, can reproduce key features of the microphysics-induced aerosol concentration changes with promising accuracy. These findings provide practical clues for the next stages of emulator development; they also provide general insights that are likely applicable to the emulation of other aerosol processes, as well as other atmospheric physics involving multi-scale variability.
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All authors Shady E. Ahmed, Hui Wan, Saad Qadeer, Panos Stinis, Kezhen Chong, Mohammad Taufiq Hassan Mozumder, Kai Zhang, Ann S. Almgren

Applied Physics

A transfer-learning-enhanced POD-FNN surrogate for rapid signal prediction and inverse fitting in thermoreflectance with patterned transducers

Bingjia Xiao, Tao Chen and Puqing Jiang

Patterned-transducer thermoreflectance enhances sensitivity to low-thermal-conductivity materials by suppressing lateral heat spreading in the metal transducer, but its wider use is limited by the cost of repeated high-fidelity forward evaluations in iterative fitting. Here, we develop a transfer-learning-enhanced POD-FNN surrogate for rapid phase prediction in patterned-transducer thermoreflectance, using patterned FDTR as a representative case. A validated COMSOL model is first constructed, and proper orthogonal decomposition is applied directly to the phase signals to build a compact reduced-order representation. A feedforward neural network is then trained to predict the POD coefficients from thermophysical and geometric parameters. Within the original parameter domain, the surrogate achieves mean and median RMSE values of 0.19 and 0.17 degrees, with a maximum RMSE below 0.47 degrees, while reducing the average prediction time per signal from 5.39 s to 0.01 s (about 534x). In inverse analysis, the fitting time for a representative case is reduced from about 18950 s to about 65 s with comparable accuracy. The framework is further applied to measured Al/SiO2 samples, yielding stable silica thermal conductivities of 1.44 +/- 0.088, 1.43 +/- 0.093, and 1.50 +/- 0.079 W/(m K) for conventional FDTR and patterned FDTR with pattern radii of 5.3 and 3.25 um, respectively. Transfer learning further improves performance in expanded parameter domains, with the TL-FR strategy giving the best overall results. Reducing the additional target-domain dataset from 6000 to 1000 samples also lowers the high-fidelity data-generation time from about 34179 s to about 5885 s. The proposed framework provides an accurate and efficient route for repeated forward evaluation, rapid inverse fitting, and cost-effective model updating in patterned thermoreflectance workflows.
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Applied Physics

Skull-Conforming Acoustic Holographic Lenses for Transcranial Targeting

Ceren Cengiz, Mihir Pewekar, Hrishikesh Kulkarni et al. (+6 more)

Transcranial focused ultrasound (tFUS) offers noninvasive access to deep brain circuits but remains limited by skull-induced phase aberration, acoustic impedance mismatch, and poor volumetric control of intracranial pressure fields. Conventional phased-array and planar holographic strategies compensate aberrations electronically or computationally, yet do not resolve geometric and coupling inconsistencies imposed by subject-specific cranial morphology. We introduce personalized skull-conforming acoustic holograms that physically encode individualized wavefront corrections into a conformal acoustic interface. Within a subject-specific volumetric holography (SSVH) framework, cranial geometry and therapeutic constraints are embedded into a physics-based optimization pipeline for holographic phase synthesis. The resulting lens is integrated with a skull- and skin-conforming coupling layer that enhances impedance continuity, reduces reflection losses, and stabilizes spatial alignment, enabling simultaneous aberration mitigation and efficient transcranial transmission. Numerical simulations across multiple subjects and targets demonstrate consistent volumetric focusing and reliable target coverage while maintaining pressure fields within safety limits. Experimental validation using an ex vivo human skull confirms accurate fabrication, effective acoustic coupling, and faithful reconstruction of designed three-dimensional acoustic fields. By unifying wavefront engineering with anatomical conformity, this work establishes skull-conforming acoustic holography as a scalable strategy for high-fidelity, anatomically adaptive transcranial ultrasound targeting.
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All authors Ceren Cengiz, Mihir Pewekar, Hrishikesh Kulkarni, Yunruo Ni, Nathan Sambo, Adam Maxwell, Eli Vlaisavljevich, Wynn Legon, Shima Shahab

Atomic Physics · Cross-listed from cond-mat.quant-gas

Magnetic-field control of interactions in alkaline-earth Rydberg atoms and applications to XXZ models

Masaya Kunimi and Takafumi Tomita

We study the magnetic-field dependence of the interactions between two alkaline-earth(-like) Rydberg atoms, ${}^{88}$Sr and ${}^{174}$Yb. Considering the pair of Rydberg states $|ns,{}^3S_1,m_J\rangle$ and $|(n+1)s,{}^3S_1,m_J\rangle$, we show that the effective Hamiltonian takes the form of an XXZ-type quantum spin model, as in the alkali-atom case [M. Kunimi and T. Tomita, Phys. Rev. A 112, L051301 (2025)]. We find that the behavior of the anisotropy parameter for ${}^{174}$Yb at zero magnetic field is significantly different from that for other atomic species. This behavior arises from the interplay of strong spin-orbit coupling and the resulting multichannel redistribution of Förster defects in ${}^{174}$Yb. We systematically calculate the interaction parameters of the XXZ model in the presence of a magnetic field and show that they can be tuned by the field. As applications to quantum many-body problems, we investigate one-dimensional systems in the large-anisotropy regime and show that the folded XXZ model can be realized in ${}^{174}$Yb systems without fine-tuning of the field. We also investigate two-dimensional square-lattice systems and show that a supersolid phase can emerge in the ground state at the mean-field level.
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Space Physics

Properties and Radial Evolution of Solar Wind Turbulence Near Mercury's Orbit

Xinmin Li, Chuanfei Dong, Lina Z. Hadid et al. (+6 more)

We present a comprehensive statistical study of the radial evolution of solar wind turbulence near Mercury's orbit using long-term magnetic field measurements from the MESSENGER mission. Owing to Mercury's highly elliptical orbit and the spacecraft's repeated, extended residence in the upstream solar wind, the data set provides more than 17,000 hours of observations, enabling robust statistics across well-defined heliocentric distance intervals (0.31-0.47 au). We find that inertial-range spectral slopes remain close to -3/2 throughout Mercury's orbit, showing no significant radial evolution. Combined with low magnetic compressibility, this result indicates a stable, predominantly Alfvenic inertial-range cascade already established here. In contrast, kinetic-range spectral slopes exhibit clear radial evolution, becoming progressively shallower with increasing heliocentric distance, highlighting the greater sensitivity of kinetic-scale turbulence to heliocentric conditions. The ion-scale spectral break frequency decreases with distance in the spacecraft frame, while its normalized form increases relative to the local proton cyclotron frequency, demonstrating that the break is not tied to proton cyclotron frequency but reflects evolving local plasma conditions. Magnetic compressibility shows a similar frequency dependence at all distances, with a subtle radial enhancement of compressive fluctuations at kinetic scales. Autocorrelation analysis reveals strong anisotropy, with the correlation times of field-aligned magnetic fluctuations increasing with heliocentric distance, while those of perpendicular fluctuations remain shorter and nearly invariant. Together, these results demonstrate a clear scale-dependent radial evolution of solar wind turbulence near Mercury-s orbit, providing new constraints on the development of kinetic processes in the inner heliosphere.
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All authors Xinmin Li, Chuanfei Dong, Lina Z. Hadid, Sae Aizawa, Chi Zhang, Hongyang Zhou, Liang Wang, Jiawei Gao, James A. Slavin

Optics

Nanoscale Fluorescence Thermometry: Probes, Recent Advances and Emerging Directions

Md Shakhawath Hossain, Nhat Minh Nguyen, Thi Ngoc Anh Mai et al. (+10 more)

The transition of materials and devices to nanometer, atomic, and quantum scales makes thermal characterization increasingly challenging, driving the need for advanced nanoscale thermometry. Fluorescence nanothermometry has emerged as a powerful approach, enabling remote, spatially resolved temperature measurements with sub-micrometer-to-nanometer precision across applications in nanoelectronics, microfluidics, and biological systems. In these systems, temperature is inferred from variations in fluorescence observables, including spectral position, intensity, linewidth, and excited-state dynamics. This review provides a comprehensive and critical overview of fluorescence nanothermometry, covering fundamental mechanisms, material platforms, recent advances, and emerging applications. It further presents a critical evaluation of key challenges and discusses emerging strategies and future research directions toward achieving robust, real-time thermometry. It is anticipated that this review will stimulate further advances in material platforms and system design, accelerating the development of accurate, scalable, and application-ready nanoscale thermometers.
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All authors Md Shakhawath Hossain, Nhat Minh Nguyen, Thi Ngoc Anh Mai, Trung Vuong Doan, Chaohao Chen, Qian Peter Su, Jiayan Liao, Yongliang Chen, Quynh Le-Van, Vu Khac Dat, Toan Dinh, Xiaoxue Xu, Toan Trong Tran

Computational Physics

A High-Order Nodal Galerkin Formulation for the Müller Equation: Bypassing Divergence Conformity via Kernel Cancellation

Yao Luo

The Müller boundary integral equation for penetrable electromagnetic scattering is conventionally discretized using divergence-conforming basis functions, a restriction inherited from the PMCHWT framework. This paper demonstrates that this constraint can be bypassed. The double-gradient operator in the Müller formulation acts on the kernel difference $\varphi_a - \varphi_i$, so that the $\mathcal{O}(R^{-3})$ hypersingularity cancels identically, reducing the operators to weakly singular $\mathcal{O}(R^{-1})$ kernels. Exploiting this cancellation, we develop a nodal, high-order Galerkin formulation using $\mathrm{P}_2$ isoparametric shape functions on curved manifolds. The surface vector field is constructed via a metric-weighted orthonormal tangent frame. The singular integrals are evaluated by Sauter–Schwab quadrature, and a Morton-ordered Block Jacobi preconditioner is introduced. By capturing the dominant near-field interactions within geometrically clustered diagonal blocks, it yields robust, superlinear GMRES convergence under extreme material and geometric parameters. Validation against semi-analytical EBCM references confirms high-order spatial accuracy and optical-theorem satisfaction to high precision.
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Fluid Dynamics

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models

Aditya Sai Pranith Ayapilla, Kazuya Miyashita, Yuki Yasuda et al. (+1 more)

Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accurate probabilistic DA is often computationally expensive because it requires repeated high-resolution (HR) forecasts and large ensembles. In this study, we develop DiffSRDA, a probabilistic spatiotemporal super-resolution data assimilation framework based on denoising diffusion models, and evaluate it on an idealized barotropic ocean jet instability testbed. DiffSRDA is trained offline to generate short HR analysis windows conditioned on (i) a time series of low-resolution (LR) forecast frames and (ii) sparse HR observations. Repeated reverse diffusion sampling then produces an ensemble of HR analyses, providing both point estimates and uncertainty information. Despite relying only on low-cost LR forecasts, DiffSRDA achieves reconstruction quality close to that of an Ensemble Kalman Filter (EnKF) driven by HR forecasts, while improving over deterministic CNN-based SRDA baselines. The sampled ensemble also yields physically meaningful uncertainty patterns, with spread concentrated in dynamically active regions similarly to EnKF. A key practical result is that accurate base DiffSRDA cycling does not require long reverse chains: most of the full-chain accuracy is retained with only a few reverse steps, making diffusion-based SRDA practical for repeated cycling. Finally, by exploiting the score-based structure of diffusion sampling, we demonstrate training-free observation-consistency guidance for deployment-time sensor-layout shifts, enabling improved use of changed observation configurations without retraining. Overall, diffusion models provide a practical, uncertainty-aware, and computationally efficient approach for spatiotemporal SRDA in chaotic fluid flows.
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All authors Aditya Sai Pranith Ayapilla, Kazuya Miyashita, Yuki Yasuda, Ryo Onishi

Medical Physics

PsSource: an installable Geant4 extension for transport-coupled positronium annihilation with validated Ore-Powell three-photon physics

Jason G Parker

PsSource is an installable Geant4 extension for configurable positronium-aware terminal positron annihilation. Geant4 transports the positron normally, after which PsSource replaces only terminal at-rest annihilation, preserves the terminal position and global time, resolves a fixed or host-defined environment, samples the annihilation class and delay, and returns ordinary two- or three-photon secondaries. Supported outcomes are direct two-photon, para-positronium two-photon, ortho-positronium two-photon, and ortho-positronium three-photon annihilation. Fixed and exponential delays and approximate phase-space, Geant4 Ore-Powell, and polarized Geant4 Ore-Powell backends are available. Regression tests verified terminal-state preservation, timing decomposition, all four classes, photon multiplicity and parentage, equivalent environment pathways, and unchanged photon physics when PsSource-specific truth recording was disabled. The Ore-Powell backend was evaluated using 100,000 three-photon events. Comparison with an analytic joint-energy distribution yielded chi-square = 1236.91 for 1274 degrees of freedom, a standardized score of -0.735, and a maximum marginal empirical CDF difference of 0.00208. Ordered photon-energy spectra agreed with native Geant4 and GATE references, with no pairwise empirical CDF difference greater than 0.00452. Photon directions and event-plane normals were isotropic with no detectable Cartesian-axis bias. The polarized backend satisfied normalization and transversality to within 1.5 x 10^-12 and preserved the ordinary Ore-Powell energy spectrum. PsSource provides a validated integration layer for positronium-aware terminal annihilation in Geant4.
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2604.21173 · 23 Apr · Major revision following peer review. PsSource was redesigned as an installable transport-coupled Geant4 annihilation extension. Added Ore-Powell and polarized Ore-Powell physics, independent analytic validation, native Geant4 and GATE comparisons, isotropy and polarization tests, and transport-coupled regressions. Previous detector-reconstruction analyses were removed

Abstract PDF $PHOT ×11

Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

Percolation Critical Probability of Aperiodic Smith Hat tile(1, $\sqrt3$)

Haitao Gao and Aaryash Bharadwaj

The Smith Hat tile is the first known aperiodic monotile, having been discovered in 2023. The simple structure, constructed using only 8 kites, is unique and well motivated for analysis within percolation theory. The primary goal of this paper is to discover the critical threshold $p_c$ in both site and bond Bernoulli structures using Monte Carlo simulation for the Smith hat tile(1,$\sqrt3$). Our findings are site and bond values of $p_c^s = 0.822725 \pm 0.000044$ and $p_c^b = 0.798161 \pm 0.000044$ for edge percolation and $0.544247 \pm 0.000101$ for site percolation on the dual graph.
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Wednesday 22 April

Fluid Dynamics · Cross-listed from cs.LG

On the Role of Strain and Vorticity in Numerical Integration Error for Flow Matching

Chenxi Tao and Seung-Kyum Choi

Flow matching generates data by integrating a learned velocity field, where the number of integration steps (NFE) directly determines inference cost. We analyze which properties of the velocity field govern integration error by decomposing the velocity Jacobian into its symmetric part S (strain rate) and antisymmetric part Omega (vorticity). We prove that strain and vorticity play different roles: strain controls exponential error amplification through the logarithmic norm, while vorticity contributes only linearly to the local truncation error. We further show that the optimal transport velocity field is irrotational and has zero material derivative, implying second-order Euler accuracy; for exact displacement interpolation, the associated Lagrangian particle dynamics are integrated exactly by Euler. Motivated by this analysis, we study weighted Jacobian regularization with strain weight alpha and vorticity weight beta. Experiments on 2D synthetic data confirm the main theoretical predictions, showing up to 2.7x lower integration error at NFE=5. Preliminary CIFAR-10 experiments show consistent trends, with a lightweight fine-tuning procedure improving FID by 14 percent at NFE=10 while preserving high-NFE quality.
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Instrumentation and Detectors

Charging-up and reverse charging-up phenomena in a double-mask triple GEM detector

S. Mandal, S. Ghosh, S. Das et al. (+1 more)

The Gas Electron Multiplier (GEM) detectors are widely used in high-energy physics (HEP) experiments as tracking devices because of their excellent position resolution and to handle high particle rates capability. Charging-up effect is a well known phenomenon in GEM detectors because of the presence of the dielectric medium – Kapton in the foil. Charging-up of GEM foil takes place when it is exposed to high radiation after application of high voltage. A new phenomenon of reverse charging-up, a complementary behaviour is also observed when the irradiation rate is reduced, where the gain relaxes gradually towards its initial value. In this study, the charging-up and reverse charging-up effects are investigated for a double-mask triple GEM chamber operated with an Argon and Carbon dioxide (70/30) gas mixture. The measurements provide a detailed understanding of the gain variation under irradiation and its stabilisation behaviour. The experimental setup, methodology and results are presented in this article.
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All authors S. Mandal, S. Ghosh, S. Das, S. Biswas

Applied Physics

Autonomous Reliability Qualification of Ga$_2$O$_3$-based diode sensors via Safe Active Learning

Davi Febba, William A. Callahan, Anna Sacchi et al. (+1 more)

Ultra-wide bandgap (UWBG) Ga$_2$O$_3$ is a promising semiconductor for high-power and high-temperature electronics. Reliable qualification of these devices under extreme operating conditions is essential, yet conventional reliability testing is inherently time-consuming. Autonomous experimentation offers a new paradigm by enabling measurement planning and model refinement to evolve in parallel in real time. We present a Safe Active Learning (SAL) framework for autonomous reliability characterization of Ga$_2$O$_3$-based diode sensors under coupled thermal and hydrogen stress. We first evaluate SAL in simulation, where it safely expands the explored region while learning the evolving rectification surface. Second, we demonstrate SAL experimentally on an automated high-temperature probe-station platform using a Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$ diode sensor of H$_2$ and temperature, spanning 0-800 ppm H$_2$ and 350-550 {\deg}C. Finally, we use the SAL-generated dataset for offline long-horizon forecasting of the diode current at a target voltage with a structured Gaussian-process model. Its condition-dependent Kohlrausch–Williams–Watts mean and residual covariance kernel were engineered with artificial-intelligence assistance using the SAL data and an auxiliary validation dataset spanning 1,000 hours at 400 {\deg}C across multiple H$_2$ concentrations. This dataset guided kernel design and validation, and the resulting model captures its long-time, saturating degradation trends. Although demonstrated here for a rectifying Ga$_2$O$_3$-based diode, SAL is applicable to other device classes whenever a suitable safety observable can be measured in situ.
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All authors Davi Febba, William A. Callahan, Anna Sacchi, Andriy Zakutayev

Optics · Cross-listed from eess.IV

Broadband Wide Field of View Imaging with Computational Mirrors

Vishwanath Saragadam, Niki Nezakati, Amit Roy-Chowdhury et al. (+1 more)

Traditional glass-based optics are typically optimized for narrow spectral bands, such as the visible (400-700nm) or shortwave infrared (1000-1800nm). While the emergence of VIS-SWIR sensors (400-1700nm) offers transformative potential, refractive optics struggle to focus this entire range simultaneously. Mirrors represent a promising achromatic alternative; however, they are often sidelined by field curvature, and off-axis aberrations. This paper introduces Computational Mirrors, a framework that enables high-resolution, wide-field-of-view imaging across the complete VIS-SWIR spectrum using a single sensor. Our method is built on the observation that distinct regions of the field of view reach focus at varying distances from the mirror. By capturing a minimal focal stack (2-4 images), we utilize a computational backend to recover a sharp, all-in-focus image. A key contribution of this work is SeidelConv, a novel, physics-inspired, spatially-varying point spread function (PSF) model designed to accurately characterize and correct the off-axis aberrations inherent in simple concave mirrors. We demonstrate the efficacy of our approach using a first-of-its-kind 50mm F/1 optical system equipped with a VIS-SWIR sensor. Our system produces sharp images across RGB, NIR, and SWIR wavelengths without requiring refocusing, revealing material details invisible within individual spectral bands. We further validate the scalability of our approach with a 100mm F/2 system optimized for long-range imaging.
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All authors Vishwanath Saragadam, Niki Nezakati, Amit Roy-Chowdhury, Vivek Boominathan

Chemical Physics

A Noble-Gas-Centered Coordinate for Within-Period Atomic Property Trends

Jonathan Washburn, Megan Simons and Elshad Allahyarov

We introduce a single dimensionless landscape function $J_{\rm chem}(\rho) = \cosh(\rho \ln \varphi) - 1$, $\varphi = (1+\sqrt{5})/2$, on the noble-gas-centred coordinate $\rho = d/L_p \in [0,1)$, and show that it organizes four central atomic observables: first ionization energy \IE$_1$, electron affinity EA, Mulliken electronegativity $\chi_M$, and Pearson chemical hardness $\eta$, on one periodic-table axis. The outward step $\Delta J_{\rm chem}^{+}$ delivers IE$_1$; the inward gap $\Delta J_{\rm chem}^{-} = J_{\rm chem}(1) - J_{\rm chem}(\rho)$ delivers EA and $\eta$; $\chi_M$ follows by Mulliken's identity. Three results establish the empirical content. (i) The within-period IE$_1$ envelope reproduces the full noble-gas-to-alkali ordering across periods 2–6: of 34 atoms compiled across periods 2-4, 26 lie on the predicted monotone descent and the 8 upward deviations occur exactly at the textbook anomaly sites $\{p^3, d^5, f^7, s^2, d^{10}\}$. (ii) Two golden-ratio identities, ${\rm IE}_1(G_p)/{\rm IE}_1(G_{p+1}) \approx \varphi^{1/4}$ on three heavy noble-gas pairs and ${\rm IE}_1(\text{halogen})/{\rm IE}_1(\text{alkali}) \approx \varphi^2$ on four within-period pairs, agree with NIST data to MAD $\approx 1\%$ and $\approx 5\%$, respectively. (iii) The shared kernel $\Delta J_{\rm chem}^{-}$ provides single-parameter analytical fits to EA across periods 4–6 (MAE $0.3$–$0.4$~eV), to Pearson hardness $\eta$ across periods 2–4 (MAE $\sim 1$~eV on noble-gas maxima up to $10.8$~eV), and to Mulliken $\chi_M$ across a 15-atom four-class benchmark ($R^2 = 0.73$). \edit{At the period-averaged scale level, the shared-kernel relation ${\rm EA}/\eta \approx C^{(p)}_{\mathrm{EA}}/C^{(p)}_{\eta}$ is supported on period-4 NIST data: the empirical nine-atom mean $\overline{{\rm EA}/\eta} = 0.180$ agrees with the predicted constant $0.182$ to better than $1\%$, although individual-atom scatter ($\sigma \approx 0.13$) is much larger.
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Chemical Physics

Non-Equilibrium Dynamics of the Time-Dependent Excitonic Coupling in Fluorescent Protein Dimers

Robson Christie, Cerys Murray, Youngchan Kim et al. (+1 more)

We quantify the excitonic coupling in the homodimer of the dimeric Venus fluorescent protein using a quantum-classical hybrid workflow. Because the anionic chromophore carries appreciable double-excitation character, we obtain its site energy from domain-based local-pair-natural-orbital similarity-transformed equation-of-motion coupled-cluster theory (DLPNO-STEOM-CCSD), which places the in-protein bright $\pi\pi^{*}$ state at $523.90~\mathrm{nm}$, in good agreement with the experimental absorption maximum near $515~\mathrm{nm}$ and validated against an explicit triples-corrected EOM-CCSD(fT) benchmark; single-excitation time-dependent density functional theory (TDDFT) blueshifts this state. Applying a transition-density coupling (TDC) formalism to the STEOM transition density and simulating the VenusA206 tandem-dimer construct (two $\beta$-barrels covalently joined by an inter-domain linker), the covalent tether holds the A206 interface docked throughout $1~\mathrm{ns}$ of room-temperature dynamics. We obtain a thermally averaged coupling of $J = 32.8 \pm 1.6~\mathrm{cm^{-1}}$ across the $1000$-frame trajectory (Davydov splitting $2|J| = 65.6 \pm 3.1~\mathrm{cm^{-1}}$), representing a $19\%$ near-field enhancement over the ensemble point-dipole estimate ($J_{\mathrm{PDA}} = 27.6 \pm 1.3~\mathrm{cm^{-1}}$) at the $24.69 \pm 0.32~\text{\AA}$ chromophore centroid separation. Furthermore, we argue that a separation of timescales resolves the apparent theoretical tension between intermediate experimental excitonic couplings and the highly decoherent biological environment. While it has been hypothesised that the fluorescent protein $\beta$-barrel scaffold sustains coupling by shielding the chromophore from thermal fluctuations, we emphasise that the separation of timescales applies irrespective of the exact degree of environmental decoherence and dissipation.
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All authors Robson Christie, Cerys Murray, Youngchan Kim, Jaewoo Joo

Chemical Physics · Cross-listed from quant-ph

Multidimensional semiclassical single- and double-quantum spectroscopy of anharmonic molecular polaritons

Michael Reitz, Harsh Bhakta, Wei Xiong et al. (+1 more)

We present a general and efficient approach to compute phase-resolved multidimensional spectra of anharmonic molecular polaritons, based on a semiclassical evolution of the molecular Hamiltonian and cavity field in the large-$\mathcal{N}$ limit of many molecules coupled to a confined photonic mode. By systematically expanding the response in both amplitudes and phases of the input fields, our method enables a transparent and computationally simple construction of phase-cycled two-dimensional single- and double-quantum polariton spectra from the underlying nonlinear signal components. Here, phase cycling acts as an analogue of phase matching with oblique pulses, allowing for the isolation of the contributing nonlinear pathways in Liouville space. We specialize to vibrational polaritons and benchmark the method through direct comparison with experimentally measured single-quantum spectra, providing an explanation for the longstanding puzzle of the polariton bleach effect observed at short waiting times. Further, we show how the imprint of various types of anharmonicities on the double-excitation manifold can be directly probed and analyzed through double-quantum coherence spectroscopy. Taken together, our results establish a practical and powerful framework for the modeling and interpretation of nonlinear spectroscopic experiments on strongly coupled light-matter platforms and for guiding the design of cavity-enhanced molecular platforms.
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All authors Michael Reitz, Harsh Bhakta, Wei Xiong, Joel Yuen-Zhou

Plasma Physics

Development of Anisotropic Magnetized Viscosity for Magnetized Liner Inertial Fusion Simulations in FLASH

Ashwyn Sam, Fernando Garcia-Rubio, Scott Davidson et al. (+7 more)

Magnetized liner inertial fusion (MagLIF) operates in a regime where anisotropic transport phenomena fundamentally influence implosion dynamics. In strongly magnetized plasmas, the viscous stress tensor becomes highly anisotropic, yet no prior work has incorporated or examined magnetized viscosity effects in MagLIF configurations. We present the first implementation of the full Braginskii magnetized viscosity tensor for arbitrary magnetic field orientations in the Pacific Fusion branch of FLASH. The implementation is verified through analytical comparisons, direct verification against Braginskii's original formulation, Method of Manufactured Solutions, and against analytical shock solutions. Application to MagLIF-relevant configurations reveals that magnetized viscosity damps vortical structures, converts kinetic energy in those vortical structures into thermal energy, and mitigates the Rayleigh-Taylor instabilities. Simulations with seeded perturbations demonstrate yield preservation when magnetized viscosity is included. These results establish magnetized viscosity as a non-negligible physical mechanism in MagLIF plasmas and provide a validated capability for predictive modeling of magnetized high-energy-density plasmas.
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All authors Ashwyn Sam, Fernando Garcia-Rubio, Scott Davidson, C. Leland Ellison, Jason Hamilton, Raymond Lau, Nathan Meezan, Adam Reyes, Paul Schmit, Alexander Velikovich

Plasma Physics

Quantum Computing Framework for Transient Scattering of Electromagnetic Waves by Dielectric Structures

Min Soe, Abhay K. Ram, Efstratios Koukoutsis et al. (+3 more)

Quantum computers are ideally set up to solve linear systems which are of a form similar to the Schrodinger/Dirac equation of quantum mechanics. In the framework of linear response theory, the propagation and scattering of electromagnetic waves in a dielectric medium are described by Maxwell equations. The qubit lattice algorithm consists of a series of alternating unitary streaming and entanglement operators acting on qubit amplitudes constructed from the electric and magnetic fields. It is not a direct discretization of Maxwell equations, but recovers the desired equations to second order in lattice grid spacing. The resulting algorithm is implemented on a present-day supercomputer and is the basis of studying scattering of electromagnetic waves by an elliptical dielectric. As opposed to the steady state description of Mie scattering in frequency domain, the temporal evolution provides insights into transient scattering. The QLA simulations, reveal that a spatially localized wave packet propagating past an elliptic dielectric, embedded in vacuum, leads to several reflections generated by wave fields trapped within the dielectric. The physics insight brought forth by these simulations is not apparent from frequency domain studies of scattering. A complimentary simulation on transient scattering of a wave packet by an elliptical vacuum bubble inserted in a uniform dielectric demonstrates a stark contrast with respect to scattering off an elliptical dielectric in vacuum. Essentially, there is only a single internal reflection in which the field amplitudes are significantly smaller than those for side and forward scattering. A simple model based on the Kirchhoff tangent plane approximation helps explain the differences between these two scattering examples.
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All authors Min Soe, Abhay K. Ram, Efstratios Koukoutsis, George Vahala, Linda Vahala, Kyriakos Hizanidis

Atmospheric and Oceanic Physics

climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer

Shuochen Wang, Nishant Yadav, Joy Merwin Monteiro et al. (+1 more)

Accurate representation of moist convective sub-grid-scale processes remains a major challenge in global climate models, as traditional parameterization schemes are both computationally expensive and difficult to scale. Neural network (NN) emulators offer a promising alternative by learning efficient mappings between atmospheric states and convective tendencies while retaining fidelity to the underlying physics. However, most existing NN-based parameterizations are memory-less and rely only on instantaneous inputs, even though convection evolves over time and depends on prior atmospheric states. Recent studies have begun to incorporate convective memory, but they often treat past states as independent features rather than modeling temporal dependencies explicitly. In this work, we develop a temporal memory-aware Transformer emulator for the Emanuel convective parameterization and evaluate it in a single-column climate model (SCM) under both offline and online configurations. The Transformer captures temporal correlations and nonlinear interactions across consecutive atmospheric states. Compared with baseline emulators, including a memory-less multilayer perceptron and a recurrent long short-term memory model, the Transformer achieves lower offline errors. Sensitivity analysis indicates that a memory length of approximately 100 minutes yields the best performance, whereas longer memory degrades performance. We further test the emulator in long-term coupled simulations and show that it remains stable over 10 years. Overall, this study demonstrates the importance of explicit temporal modeling for NN-based parameterizations.
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All authors Shuochen Wang, Nishant Yadav, Joy Merwin Monteiro, Auroop R. Ganguly

Biological Physics

A Critical Assessment of the Brain Criticality Hypothesis

Chesson Sipling, Yuan-Hang Zhang and Massimiliano Di Ventra

A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the “criticality hypothesis,” which proposes that the brain operates near criticality, optimizing various information processing functions. However, the nature and behavior of criticality in cortical systems are still unclear. Alternatively, this opinion paper highlights that the coupling between neurons and slowly varying energetic resources, which may act as a form of “memory,” alone may be sufficient to generate a robust phase of neural activity with scale-invariant correlations. This memory-induced long-range order phase could provide a more natural explanation of the existing experimental data than the criticality hypothesis.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

Accelerating point defect simulations using data-driven and machine learning approaches

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai et al. (+1 more)

Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable rapid defect property predictions and high-throughput screening. In this article, we provide an overview of prominent efforts to accelerate defect simulations using these approaches. We begin by discussing the motivations for data-driven techniques in defect modeling, and describe efforts over the past decade to use descriptor-based models for rapid screening of defect properties – most notably in oxides. In particular, we discuss case studies where surrogate models and interatomic potentials were trained on density functional theory (DFT) data, leading to predictions with quantum-mechanical accuracies at a fraction of the cost. In addition to geometry relaxation and formation energy predictions, these interatomic potentials are capable of predicting phonon modes and vibrational free energies to yield defect energetics at finite temperatures – representing a key frontier for computational defect research. We finish with a discussion on how to connect these approaches and their outputs with experimental data, and provide an outlook on this burgeoning sub-field.
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All authors Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, Seán R. Kavanagh

Fluid Dynamics

Particle-resolved simulations of settling particles: A methodology for long time-integration intervals

M. Moriche, M. García-Villalba and M. Uhlmann

We present a methodology for simulating dilute suspensions of particles settling under gravity, with the main purpose of overcoming limitations of triply periodic configurations, mainly the strong vertical correlation that hinders the study of cluster dynamics. The current approach removes vertical periodicity and employs a moving reference frame, enabling efficient simulations of both single- and many-particle cases. We illustrate the method with two examples of increasing complexity: a single particle in the steady vertical regime, and a many-particle case at a parametric point where collective effects were previously observed and recovered here. A converged, free-of-corrections time interval of approximately $600 D/U_g$ is simulated in the many-particle case, representing the first simulation of this kind to date. New physical insights can be explored thanks to this new configuration, for example the effect of still fluid on the first layer of particles encountered by the fluid, or the turbulent character of the flow after a swarm of particles has passed by. Finally, the method only requires parameter tuning, allowing implementation within existing solvers without changes to their core formulation: for a standard configuration with an imposed free stream velocity at the inlet, only the input velocity (or the viscosity of the fluid) and the time step need to be updated.
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Medical Physics

Radiomics-Guided Vision Transformers for Survival Analysis

Qiyuan Shi and Yi Li

Vision Transformers (ViTs) have shown strong empirical performance on high-dimensional medical imaging data, yet their behavior under survival objectives and the interpretability of their attention mechanisms remain poorly understood. Under shallow ViTs, we design controlled experiments showing that token-level attention dynamics can recover outcome-relevant regions and that attention-based thresholding enables effective token pruning, improving both interpretability and predictive performance. We also study pretrained deep ViTs for survival analysis and propose a radiomics-guided hybrid model that integrates pixel-based embeddings with interpretable radiomic features through a multimodal Cox framework and contrastive alignment. Applied to a COVID-19 chest X-ray cohort with a composite ICU admission or mortality endpoint, the proposed approach achieves competitive discrimination while providing clinically meaningful attention maps and feature-group importance.
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