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Physics Physics

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Tuesday 4 November

Medical Physics · Cross-listed from cs.LG

Large-scale automatic carbon ion treatment planning for head and neck cancers via parallel multi-agent reinforcement learning

Jueye Zhang, Chao Yang, Youfang Lai et al. (+9 more)

Head-and-neck cancer (HNC) planning is difficult because multiple critical organs-at-risk (OARs) are close to complex targets. Intensity-modulated carbon-ion therapy (IMCT) offers superior dose conformity and OAR sparing but remains slow due to relative biological effectiveness (RBE) modeling, leading to laborious, experience-based, and often suboptimal tuning of many treatment-planning parameters (TPPs). Recent deep learning (DL) methods are limited by data bias and plan feasibility, while reinforcement learning (RL) struggles to efficiently explore the exponentially large TPP search space. We propose a scalable multi-agent RL (MARL) framework for parallel tuning of 45 TPPs in IMCT. It uses a centralized-training decentralized-execution (CTDE) QMIX backbone with Double DQN, Dueling DQN, and recurrent encoding (DRQN) for stable learning in a high-dimensional, non-stationary environment. To enhance efficiency, we (1) use compact historical DVH vectors as state inputs, (2) apply a linear action-to-value transform mapping small discrete actions to uniform parameter adjustments, and (3) design an absolute, clinically informed piecewise reward aligned with plan scores. A synchronous multi-process worker system interfaces with the PHOENIX TPS for parallel optimization and accelerated data collection. On a head-and-neck dataset (10 training, 10 testing), the method tuned 45 parameters simultaneously and produced plans comparable to or better than expert manual ones (relative plan score: RL $85.93\pm7.85%$ vs Manual $85.02\pm6.92%$), with significant (p-value $<$ 0.05) improvements for five OARs. The framework efficiently explores high-dimensional TPP spaces and generates clinically competitive IMCT plans through direct TPS interaction, notably improving OAR sparing.
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All authors Jueye Zhang, Chao Yang, Youfang Lai, Kai-Wen Li, Wenting Yan, Yunzhou Xia, Haimei Zhang, Jingjing Zhou, Gen Yang, Chen Lin, Tian Li, Yibao Zhang

Fluid Dynamics

Simulation of a Non-Newtonian drop impact on a rigid surface: A mess-free approach

Tapan Jana, Amit Shaw and L. S. Ramachandra

The present study explores the impact of a non-Newtonian fluid drop on a rigid surface using a mesh-free approach, Smoothed Particle Hydrodynam-ics. The complex interaction between viscous and elastic forces during drop impact may be reproduced by integrating the Oldroyd-B model, which de-scribes viscoelastic fluids, into the developed computational framework. A suit-able boundary condition is assumed for interaction between fluid and solid sur-faces. The ongoing study examines how a droplet deforms, spreads, and recoils after an impact due to its viscoelasticity. The developed computational frame-work is validated by comparing results from existing literature, providing an understanding of how viscoelasticity influences drop-impact behaviour and opening up new avenues for subsequent research in fluid dynamic problems.
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Optics

Local Wigner-Mass Maps and Integrated Negativity as Measures of nonclassicality in Quantum Chaotic Billiards

Kyu-won Park, Soojoon Lee and Kabgyun Jeong

The Wigner function is a phase space quasi-probability distribution whose negative regions provide a direct, local signature of nonclassicality. To identify where phase-sensitive structure concentrates, we introduce local positive- and negative Wigner-mass maps and adopt the integrated Wigner negativity as a compact scalar measure of nonclassical phase space structure. A decomposition of the density operator reveals that off-diagonal coherences between hybridizing components generate oscillatory, sign-alternating patterns, with the negative contribution maximized when component weights are comparable. Non-Gaussian chaotic eigenmodes exhibit a baseline negativity that is further amplified by such hybridization. We validate these diagnostics across two billiard geometries and argue that the framework is transferable to other wave-chaotic platforms, where it can aid mode engineering and coherence control.
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Optics · Cross-listed from quant-ph

Localized to delocalized spatial quantum correlation evolution in structured bright twin beams

Jerin A Thachil, Chirang R Patel, U. Ashwin et al. (+1 more)

Quantum correlations in the spatial domain hold great promise for applications in quantum imaging, quantum cryptography and quantum information processing, owing to the infinite dimensionality of the associated Hilbert space. Here, we present a theoretical investigation, complemented by experimental measurements, of the propagation dynamics of the spatial quantum correlations in bright structured twin beams generated via a four-wave mixing process in a double-$\Lambda$ configuration in atomic vapor. We derive an analytical expression describing the evolution of the spatial quantum correlation distribution from the near field to the far field. To qualitatively support the theoretical predictions, we perform experiments measuring intensity-difference noise between different spatial subregions of the twin beams as they propagate from the near field to the far field. The presence of quantum correlations is manifested as squeezing in the intensity difference noise measurement. With a Gaussian pump, we observe localized correlations in the near field and localized anti-correlations in the far field. In contrast, with a structured Laguerre-Gaussian pump, there is a transition from localized correlations in the near field to delocalized correlations in the far field. The present results offer valuable insights into the fundamental behavior of spatial quantum correlations and open possibilities for potential applications in quantum information, quantum imaging and sensing.
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All authors Jerin A Thachil, Chirang R Patel, U. Ashwin, Ashok Kumar

Medical Physics

CT density estimation and beam hardening correction for single material objects using reference scans - a simulation and experimental study

Nikhil Deshmukh

We describe the results from a simulation and experimental study of density estimation and beam hardening correction for Bremsstrahlung source computed tomography scan of objects made of a single material. The correction is done using transmission scans of a number of sheets of known thickness and density and made of the same material as the object under test.
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Fluid Dynamics

A Unified Spectrum for Turbulence in Microfluidic Flow

Chit Yau Kuan, Xiaochen Liu, Yi-Ping Ho et al. (+1 more)

We present a predictive master spectrum describing turbulence-like flows in microfluidic systems. Extending Pao's viscous-range closure, the model introduces (i) an adaptive inertial-range slope dependent on measurable dimensionless numbers and (ii) a physics-specific cutoff that captures entropy-producing sinks such as electrokinetic forcing, compliant walls, active stresses, and interfacial tension. This formulation unifies turbulence regimes – electrokinetic, active, interfacial, and compressible – within one compact expression. Comparison with reported data reproduces both spectral slopes and dissipation cutoffs while requiring only global observables (velocity, viscosity, Taylor microscale, and forcing strength). The framework provides a design-level predictive tool for turbulent microflows prior to computationally heavy DNS or CFD.
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All authors Chit Yau Kuan, Xiaochen Liu, Yi-Ping Ho, Ken-Tye Yong

Chemical Physics

Signal attenuation and phase evolution evaluation under the influence of nonlinear gradient

Chenghao Xua and Guoxing Lin

Accurately analyzing NMR and MRI diffusion experimental data relies on the theoretical expression used for signal attenuation or phase evolution. In a complex system, the encountered magnetic field is often inhomogeneous, which may be represented by a linear combination of z^n gradient fields, where n is the order. Additionally, the higher the order of the nonlinear gradient field, the more sensitive the phase variances are to differences in diffusion coefficients and delay times. Hence, studying higher-order fields has both theoretical and experimental importance, but this is a challenge for traditional methods. The recently proposed phase diffusion method proposed a general way to overcome the challenge. This method is used and demonstrated in detail in this paper to determine the phase evolution in a quadric field (n = 4). Three different types of phase evolution in the quadric gradient field are obtained. Moreover, a general signal attenuation expression is proposed to describe the signal attenuation for spin diffusion from the origin of the nonlinear gradient field. This approximation is based on the short gradient pulse (SGP) approximation but is extended to include the finite gradient pulse width (FGPW) effect by using the mean square phase. Compared to other forms of signal attenuation, such as Gaussian and Lorentzian, this method covers a broader range of attenuation, from small to relatively large. Additionally, this attenuation is easier to understand than the Mittag-Leffler function-based attenuation. The results, particularly the phase and signal attenuation expressions obtained in this paper, potentially advance PFG diffusion research in nonlinear gradient fields in NMR and MRI.
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Fluid Dynamics

Role of varying Reynolds number for flow past a rotating cylinder at high rotation rate

Aditi Sengupta, Santosh Kumar and Sanjeev Kumar

The present study reports comprehensive bifurcation analysis of flow past a rotating cylinder at a fixed rotation rate by varying free-stream Reynolds number ($Re_{\infty}$) from 1000-6000 in intervals of 50. Two-dimensional compressible Navier-Stokes equations are solved using dispersion relation preserving numerical methods over 101 test cases, amounting to $10^6$ core hours of computing. The dataset produced from high-fidelity simulations serve as useful benchmarking tools for testing compressible flow solvers, estimating unsteady force distribution and vorticity dynamics. For moderate $Re_{\infty}$, rotation induces circulation that reduces pressure drag with increasing $Re_{\infty}$. For higher $Re_{\infty}$, boundary layer becomes thinner with suppressed flow separation, but effect of rotation saturates. Thus, benefits of increasing $Re_{\infty}$ taper off and pressure recovery stalls. The bifurcation analysis reveals a critical $Re_{\infty}$ of 5650 beyond which global behavior of Magnus-Robins effect changes significantly. Supercritical flow is receptive to time-dependent instabilities and structures in wake of the cylinder become dynamically unstable. Even small changes in $Re_{\infty}$ leads to different instantaneous force distributions and sharp fluctuations in lift and drag calculations. Stronger, coherent vortices in the wake generate consistent, high-energy periodic signals, contributing to strong Fourier amplitudes in spectra. An artificial neural network (ANN) is trained using simulation datasets to serve as fast, inexpensive alternatives for calculating lift, drag, and onset time of instability. The ANN reduces time required for simulation by 99.9%, enabling dense parametric sweeps. Maximum accuracy achieved for the ANN is between 90-99% for the parameters examined.
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Medical Physics

Quantitative Risk Assessment in Radiation Oncology via LLM-Powered Root Cause Analysis of Incident Reports

Yuntao Wang, Siamak P. Najad-Davarani, Elizabeth Bossart et al. (+3 more)

Background: Modern large language models (LLMs) offer powerful reasoning that converts narratives into structured, taxonomy-aligned data, revealing patterns across planning, delivery, and verification. Embedded as agentic tools, LLMs can assist root-cause analysis and risk assessment (e.g., failure mode and effect analysis FMEA), produce auditable rationales, and draft targeted mitigation actions. Methods: We developed a data-driven pipeline utilizing an LLM to perform automated root cause analysis on 254 institutional safety incidents. The LLM systematically classified each incident into structured taxonomies for radiotherapy pathway steps and contributory factors. Subsequent quantitative analyses included descriptive statistics, Analysis of Variance (ANOVA), multiple Ordinal Logistic Regression (OLR) analyses to identify predictors of event severity, and Association Rule Mining (ARM) to uncover systemic vulnerabilities. Results: The high-level Ordinal Logistic Regression (OLR) models identified specific, significant drivers of severity. The Pathway model was statistically significant (Pseudo R2 = 0.033, LR p = 0.015), as was the Responsibility model (Pseudo R2 = 0.028, LR p < 0.001). Association Rule Mining (ARM) identified high-confidence systemic rules, such as "CF5 Teamwork, management and organisational" (n = 8, Conf = 1.0) and the high-frequency link between "(11) Pre-treatment planning process" and "CF2 Procedural" (n = 152, Conf = 0.916). Conclusion: The LLM-powered, data-driven framework provides a more objective and powerful methodology for risk assessment than traditional approaches. Our findings empirically demonstrate that interventions focused on fortifying high-risk process steps and mitigating systemic failures are most effective for improving patient safety.
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All authors Yuntao Wang, Siamak P. Najad-Davarani, Elizabeth Bossart, Matthew T. Studenski, Mariluz De Ornelas, Yunze Yang

Medical Physics

High-Resolution Magnetic Particle Imaging System Matrix Recovery Using a Vision Transformer with Residual Feature Network

Abuobaida M.Khair, Wenjing Jiang, Yousuf Babiker M. Osman et al. (+2 more)

This study presents a hybrid deep learning framework, the Vision Transformer with Residual Feature Network (VRF-Net), for recovering high-resolution system matrices in Magnetic Particle Imaging (MPI). MPI resolution often suffers from downsampling and coil sensitivity variations. VRF-Net addresses these challenges by combining transformer-based global attention with residual convolutional refinement, enabling recovery of both large-scale structures and fine details. To reflect realistic MPI conditions, the system matrix is degraded using a dual-stage downsampling strategy. Training employed paired-image super-resolution on the public Open MPI dataset and a simulated dataset incorporating variable coil sensitivity profiles. For system matrix recovery on the Open MPI dataset, VRF-Net achieved nRMSE = 0.403, pSNR = 39.08 dB, and SSIM = 0.835 at 2x scaling, and maintained strong performance even at challenging scale 8x (pSNR = 31.06 dB, SSIM = 0.717). For the simulated dataset, VRF-Net achieved nRMSE = 4.44, pSNR = 28.52 dB, and SSIM = 0.771 at 2x scaling, with stable performance at higher scales. On average, it reduced nRMSE by 88.2%, increased pSNR by 44.7%, and improved SSIM by 34.3% over interpolation and CNN-based methods. In image reconstruction of Open MPI phantoms, VRF-Net further reduced reconstruction error to nRMSE = 1.79 at 2x scaling, while preserving structural fidelity (pSNR = 41.58 dB, SSIM = 0.960), outperforming existing methods. These findings demonstrate that VRF-Net enables sharper, artifact-free system matrix recovery and robust image reconstruction across multiple scales, offering a promising direction for future in vivo applications.
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All authors Abuobaida M.Khair, Wenjing Jiang, Yousuf Babiker M. Osman, Wenjun Xia, Xiaopeng Ma

Fluid Dynamics · Cross-listed from cs.CE

A Multi-Fidelity Global Search Framework for Hotspot Prevention in 3D Thermal Design Space

Morteza Sadeghi, Hadi Keramati and Sajjad Bigham

We present a Bézier-based Multi-Fidelity Thermal Optimization Framework, which is a computationally efficient methodology for the global optimization of 3D heat sinks. The flexible Bézier-parameterized fin geometries and the adopted multi-fidelity pseudo-3D thermal modeling strategy meet at a balance between accuracy and computational cost. In this method, the smooth and compact Bézier representation of fins defines the design space from which diverse topologies can be generated with minimal design variables. A global optimizer, the Covariance Matrix Adaptation Evolution Strategy, minimizes the pressure drop with respect to a given surface-average temperature constraint to achieve improvement in the pressure loss. In the framework, the pseudo-3D model couples two thermally interacting 2D layers: a thermofluid layer representing the fluid domain passing through the fins, and a conductive base plate representing the surface where excessive average temperature is to be avoided. Both layers are coupled with calibrated heat transfer coefficients obtained from high-fidelity 3D simulations. For several fin geometries, the proposed framework has been validated by comparing the pseudo-3D results with those of full 3D simulations, which yielded good agreement in terms of temperature distribution and pressure drops when the computational cost was reduced by several orders of magnitude. Optimization results show that it attains up to 50% pressure loss reduction compared to conventional straight-fin configurations, and it reveals a clear trade-off between thermal performance and hydraulic efficiency. Thus, the proposed method forms a new basis for fast, geometry-flexible, and optimized heat sink design, enabling efficient exploration of complex geometries.
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Optics

Lithium Niobate Vertical Cavity Electro-Optic Modulator

Jikun Liu, Weiye Liu, Wei Wu et al. (+13 more)

Electro-optic modulators (EOMs) are vital for optical imaging and information processing, with free-space devices enabling LiDAR and beam control. Lithium niobate (LN), powered by the strong Pockels effect and scalable LN-on-insulator (LNOI) platform, has become a leading material for high-performance EOMs. Here we realize a vertical-cavity EOM in which an LN membrane is sandwiched between two photonic crystal (PhC) mirrors with integrated electrodes. The cavity supports sharp defect-mode resonances that shift efficiently under the Pockels effect, enabling strong modulation of transmission. Experiments show a depth of 43 % at 50 V and a bandwidth of 5 MHz. This architecture combines free-space compatibility with fabrication simplicity, opening new routes to compact electro-optic platforms for ranging, holography, and beam steering.
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All authors Jikun Liu, Weiye Liu, Wei Wu, Ziang Guo, Changrui Zhu, Lun Qu, Pengfei Zhu, Yiting Zhang, Zhihao Chen, Qinglian Li, Dahuai Zheng, Hongde Liu, Shaowei Wang, Wei Cai, Mengxin Ren, Jingjun Xu

Optics

Process-structure-property relationships in subtractive fabrication of silicon nitride microresonators for nonlinear photonics

Lala Rukh, Gabriel M. Colación, Franco H. Buck et al. (+1 more)

Silicon nitride (SiN) has emerged as a popular platform for nonlinear photonics due to its large bandgap, relatively large nonlinear index of refraction, and high degree of CMOS-compatibility. Nonlinear optical processes in microresonators require both large quality factors to promote build-up of large intracavity powers and sufficient control over resonator dispersion to implement phase-matching, which is generally achieved through careful design and fabrication of the resonator cross-sectional dimensions. Additionally, generating dissipative Kerr soliton optical frequency combs requires operation in the anomalous dispersion regime, which calls for such thick layers of SiN as to make achieving accuracy during etching a challenge. In this work, we investigate the relationship between fabrication process details, including lithography and etching, and optical properties, such as optical loss and resonator dispersion, for two different subtractive fabrication approaches: one utilizing a polymer-based electron beam resist as the protective mask during etching and the other employing a thin metallic etch-mask. Using both approaches, we identify the advantages and disadvantages of polymer and metal etch-masks, determine the origins of optical loss through variations in electron beam lithography parameters, and connect resonator sidewall roughness to optical loss. Finally, we compare frequency comb generation in resonators fabricated using either approach.
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All authors Lala Rukh, Gabriel M. Colación, Franco H. Buck, Tara E. Drake

Applied Physics · Cross-listed from cond-mat.mes-hall

Nanofluidic logic based on chiral skyrmion flows

Xichao Zhang, Jing Xia, Yan Zhou et al. (+4 more)

Particle-like chiral magnetic skyrmions can flow in nanotracks and behave like chiral fluids. Using interacting flows to perform logical operations is an important topic in microfluidics and nanofluidics. Here, we report a basic nanofluidic logic computing system based on chiral magnetic skyrmions flowing in parallel pipelines connected by an H-shaped junction. The flow behaviors could be manipulated by adjusting the spin polarization angle, which controls the intrinsic skyrmion Hall angle. We demonstrate that within certain range of the spin polarization angle, fully developed skyrmion flows could lead to fluidic logical operations, which significantly reduce the complexity of skyrmion logic as there is no need for deterministic creation, precise control, and detection of a single isolated skyrmion. Our results suggest that the chiral flow behaviors of magnetic quasiparticles may offer possibilities for spintronic and nanofluidic functions.
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All authors Xichao Zhang, Jing Xia, Yan Zhou, Guoping Zhao, Xiaoxi Liu, Yongbing Xu, Masahito Mochizuki

Optics

Extending Reflectometry Range, A Zero-Crossing Algorithm for Thick Film Metrology

Zimu Zhou, Enrique Lopez-Guerra, Iulica Zana et al. (+7 more)

Accurate and high-efficiency film metrology remains a key challenge in High-Volume Manufacturing (HVM), where conventional spectroscopic reflectometry and white light interferometry (WLI) are either limited by model dependence or throughput. In this work, we extend the measurable film-thickness range of reflectometry to at least 50 um through a new model-free algorithm, the Linearized Reflectance Zero-Crossing (LRZ) method. The approach builds upon the previously reported Linearized Reflectance Extrema (LRE) technique but eliminates the sensitivity to spectral sampling and fringe attenuation that degrade performance in the thick-film regime. By linearizing phase response and extracting zero-crossing positions in wavenumber space, LRZ provides robust and repeatable thickness estimation without iterative fitting, achieving comparable accuracy with much higher computational efficiency than conventional model-based methods. Validation using more than 80 measurements on alumina films over NiFe substrates shows excellent correlation with WLI (r = 0.97) and low gauge repeatability and reproducibility (GR&R < 3%). Moreover, LRZ achieves an average Move-Acquire-Measure (MAM) time of approximately 2 s, which is about 7 times faster than WLI. The proposed method enables fast, accurate, and model-independent optical metrology for thick films, offering a practical solution for advanced HVM process control.
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All authors Zimu Zhou, Enrique Lopez-Guerra, Iulica Zana, Vu Nguyen, Nguyen Quoc Huy Tran, Bojun Zhou, Gary Qian, Michael Kwan, Peter Wilkens, Chester Chien

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

Homogeneous All-Inorganic Perovskite Films via High-Pressure Recrystallization

Asma Miled, Trong Tam Nguyen, Anh Vui Nguyen et al. (+13 more)

Metal halide perovskites are promising materials for optoelectronic applications owing to their outstanding optical and electronic properties. Among them, all-inorganic perovskites such as CsPbBr$_3$ offer superior thermal and chemical stability. However, obtaining high-quality CsPbBr$_3$ thin films via solution processing remains challenging due to the precursor's low solubility, and current additive or solvent engineering strategies are often complex and poorly reproducible. High-pressure recrystallization has recently emerged as a promising route to improve film quality, yet its impact on film properties remains insufficiently explored. Here, we systematically investigate the morphological, structural, and optical properties of CsPbBr$_3$ thin films prepared by high-pressure recrystallization, in comparison with standard non-recrystallized films. Optimized recrystallization at 300 bar produces smooth, pinhole-free, single-phase 3D perovskite layers with sub-nanometer roughness, while the film thickness is precisely tunable via precursor concentration. The process enhances both grain and crystallite sizes, leading to amplified spontaneous emission with a reduced excitation threshold and improved photostability. Temperature-dependent X-ray diffraction further reveals the orthorhombic–tetragonal–cubic phase transition, consistent with single-crystal behavior. This study provides fundamental insights into pressure-driven recrystallization and establishes a reproducible, scalable approach for fabricating high-quality CsPbBr$_3$ films for optoelectronic devices.
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All authors Asma Miled, Trong Tam Nguyen, Anh Vui Nguyen, José Penuelas, Aziz Benamrouche, Céline Chevalier, Thi Kim Anh Hoang, Gaëlle Trippé-Allard, Elsa Cassette, Brice Devif, Emmanuel Drouard, Emmanuelle Deleporte, Hong Hanh Mai, Abdelaziz Bouazizi, Christian Seassal, Hai Son Nguyen

Applied Physics · Cross-listed from cond-mat.mes-hall

Fabrication and characterization of high-Q silicon nitride membrane resonators

Atkin D. Hyatt, Oscar A. Flores, Aman R. Agrawal et al. (+2 more)

Silicon nitride membranes are a powerful and ubiquitous optomechanical resonator technology, enabling high mechanical Q, low optical loss, and enhanced optomechanical coupling via a panoply of strain-, phononic-, and photonic-crystal engineering techniques. Fabrication and characterization of silicon nitride membranes has become a form of tacit knowledge in optomechanics research groups. Here we present a video run-through of the design, fabrication, and characterization of a contemporary silicon nitride membrane resonator (specifically, a centimeter-scale Si3N4 nanoribbon supporting $Q>10^8$ torsion modes). Our tutorial can serve as a starting point or refresher for practitioners in the field.
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All authors Atkin D. Hyatt, Oscar A. Flores, Aman R. Agrawal, Charles A. Condos, Dalziel J. Wilson

Fluid Dynamics

Geometric Solution of Turbulence as Diffusion in Loop Space

Alexander Migdal

Strongly nonlinear dynamics, from fluid turbulence to quantum chromodynamics, have long constituted some of the most challenging problems in theoretical physics. This review describes a unified theoretical framework, the loop space calculus, which offers an analytical approach to these problems. The central idea is a shift in perspective from pointwise fields to integrated loop observables, a transformation that recasts the governing nonlinear equations into a universal linear diffusion equation in the space of loops. This framework, supported by recent mathematical analysis, is analytically solvable and yields an exact, parameter-free solution for decaying hydrodynamic turbulence–the Euler ensemble–which is shown to be dual to a solvable string theory. The theory's predictions include: (i) the unification of spatial and temporal scaling laws, governed by two related, infinite spectra of intermittency and decay exponents derived from the nontrivial zeros of the Riemann zeta function; (ii) a first-order phase transition in magnetohydrodynamic (MHD) turbulence; and (iii) the formation of quantized, concentric shells in passive scalar mixing. The appearance of identical mathematical structures as solutions to the turbulent regime of Yang-Mills gradient flow points to the broad applicability of this approach. The framework also yields a new type of analytic Hodge-dual matrix surface that solves the Yang-Mills fixed-point loop equation by harmonic map, opening the way for a geometric formulation of QCD string theory.
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Accelerator Physics

Plasma Processing of FRIB Low-Beta Cryomodules using Higher-Order-Modes

P. Tutt, W. Chang, K. Elliott et al. (+4 more)

Improvement in SRF accelerator performance after in-tunnel plasma processing has been seen at SNS and CEBAF. Plasma processing development for FRIB quarter-wave and half-wave resonators (QWRs, HWRs) was initiated in 2020. Plasma processing on individual QWRs (beta = 0.085) and HWRs (beta = 0.53) has been found to significantly reduce field emission. A challenge for the FRIB cavities is the relatively weak fundamental power coupler (FPC) coupling strength (chosen for efficient continuous-wave acceleration), which produces a lot of mismatch during plasma processing at room temperature. For FRIB QWRs, driving the plasma with higher-order modes (HOMs) is beneficial to reduce the FPC mismatch and increase the plasma density. The first plasma processing trial on a spare FRIB QWR cryomodule was done in January 2024, with before-and-after bunker tests and subsequent installation into the linac tunnel. The first in-tunnel plasma processing trial was completed in September 2025. For both cryomodules, before-and-after cold tests showed a significant increase in the average accelerating gradient for field emission onset after plasma processing for some cavities. In parallel with the cryomodule trials, the use of dual-drive plasma is being explored with the goal of improving the effectiveness of plasma processing.
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All authors P. Tutt, W. Chang, K. Elliott, W. Hartung, S. Kim, K. Saito, T. Xu

Monday 3 November

General Physics

Mathematical basis, phase transitions and singularities of (3+1)-dimensional phi4 scalar field model

Zhidong Zhang

The lambda phi4 scalar field model can be applied to interpret pion-pion scattering and properties of hadrons. In this work, the mathematical basis, phase transitions and singularities of a (3+1)-dimensional (i.e., (3+1)D) phi4 scalar field model are investigated. It is found that as a specific example of topological quantum field theories, the (3+1)D phi4 scalar field model must be set up on the Jordan-von Neumann-Wigner framework and dealt with the parameter space of complex time (or complex temperature). The use of the time average and the topologic Lorentz transformation representing Reidemeister moves ensure the integrability, which takes into account for the contributions of nontrivial topological structures to physical properties of the many-body interacting system. The ergodic hypothesis is violated at finite temperatures in the (3+1)D phi4 scalar field model. Because the quantum field theories with ultraviolet cutoff can be mapped to the models in statistical mechanics, the (3+1)D phi4 scalar field model with ultraviolet cutoff is studied by inspecting its relation with the three-dimensional (3D) Ising model. Furthermore, the direct relation between the coupling K in the 3D Ising model and the bare coupling lambda0 in the (3+1)D phi4 scalar field model is determined in the strong coupling limit. The results obtained in the present work can be utilized to investigate thermodynamic physical properties and critical phenomena of quantum (scalar) field theories.
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Chemical Physics · Cross-listed from cond-mat.soft

AI-assisted design of chemically recyclable polymers for food packaging

Brandon K. Phan, Chiho Kim, Janhavi Nistane et al. (+9 more)

Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(p-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries.
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All authors Brandon K. Phan, Chiho Kim, Janhavi Nistane, Wei Xiong, Haoyu Chen, Woo Jin Jang, Farzad Gholami, Yongliang Su, Jerry Qi, Ryan Lively, Will Gutekunst, Rampi Ramprasad

Physics and Society

Assessing Climate Vulnerability Risk for Substations in Massachusetts Via Sensitivity Analysis

Hritik Gopal Shah and Elli Ntakou

The electric grid is increasingly vital, supporting essential services such as healthcare, heating and cooling transportation, telecommunications, and water systems. This growing dependence on reliable power underscores the need for enhanced grid resilience. This study presents Eversource's Climate Vulnerability Assessment (CVA) for bulk distribution substations in Massachusetts, evaluating risks from storm surge, sea level rise, precipitation, and extreme temperatures. The focus is on developing a cost-efficient model to guide targeted resilience investments. This is achieved by overcoming the limitations of single-variable analyses through hazard-specific assessments that integrate spatial, climate, electrical asset, and other relevant data; and applying sensitivity analysis to establish data-driven thresholds for actionable climate risks. By integrating geospatial analysis and data modeling with power engineering principles, this study provides a practical and replicable framework for equitable, data-informed climate adaptation planning. The results indicate that thresholds for certain climate hazards can be highly sensitive and result in significantly larger sets of stations requiring mitigation measures to adequately adapt to climate change, indicating that high-fidelity long-term climate projections are critical.
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Classical Physics

Comment on "Absence of a consistent classical equation of motion for a mass-renormalized point charge"

Paweł Zin and Maciej Pylak

Here we comment on the paper by Arthur D. Yaghjian, Phys. Rev. E 78, 046606 (2008) (arXiv:0805.0142). The author provides an equation of motion for a point charged particle in a certain regime of system parameters (on the other hand, claiming that in a different regime the classical equation of motion does not exist). The solutions of this equation (in the regime where it exists) presented in the paper show instantaneous jumps in the particle's velocity. We show that such jumps, in the case of a point particle, would generate infinite energy in the radiated electromagnetic field. Therefore, we claim that the point-particle limit used by the author is incorrect.
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Computational Physics · Cross-listed from math.NA

Explicit invariant-preserving integration of differential equations using homogeneous projection

Benjamin Kwanen Tapley

We develop a general framework for numerically solving differential equations while preserving invariants. As in standard projection methods, we project an arbitrary base integrator onto an invariant-preserving manifold, however, our method exploits homogeneous symmetries to evaluate the projection exactly and in closed form. This yields explicit invariant-preserving integrators for a broad class of nonlinear systems, as well as pseudo-invariant-preserving schemes capable of preserving multiple invariants to arbitrarily high precision. The resulting methods are high-order and introduce negligible computational overhead relative to the base solver. When incorporated into adaptive solvers such as Dormand-Prince 8(5,3), they provide error-controlled, invariant-preserving, high-order time-stepping schemes. Numerical experiments on double-pendulum and Kepler ODEs as well as semidiscretised KdV and Camassa-Holm PDEs demonstrate substantial improvements in both accuracy and efficiency over standard approaches.
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Fluid Dynamics

Particle Thermal Inertia Delays the Onset of Convection in Particulate Rayleigh-Bénard System

Saad Raza, Apolline Lemoine, Yan Zhang et al. (+4 more)

We investigate the linear stability of a thermally stratified fluid layer confined between horizontal walls and subject to continuous injection of dilute thermal particles at one boundary and extraction at the opposite, forming a particulate Rayleigh-Bénard (pRB) system. The analysis focuses on the influence of thermal coupling between the dispersed and carrier phases, quantified by the specific heat capacity ratio $\epsilon$. Increasing $\epsilon$ systematically enhances stability, with this effect persisting across a wide range of conditions, including heavy and light particles, variations in volumetric flux, injection velocity and direction, and injection temperature. The stabilizing influence saturates when the volumetric heat capacity of the particles approaches that of the fluid, $\epsilon = O(1)$. The physical mechanism is attributed to a modification of the base-state temperature profile caused by interphase heat exchange, which reduces thermal gradients near the injection wall and weakens buoyancy-driven motion.
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All authors Saad Raza, Apolline Lemoine, Yan Zhang, Enrico Calzavarini, Romulo B. Freitas, Leonardo S. de B. Alves, Silvia C. Hirata

Atomic Physics

Screened Thin-Target Bremsstrahlung with Partially-Ionized High-Z Species

Salomon Guinchard, Yves Savoye-Peysson and Joan Decker

Bremsstrahlung emission remains a cornerstone process in the characterization of electron dynamics in diverse high-energy environments. In particular, the accurate description of thin-target electron-ion bremsstrahlung in the presence of high-$Z$ species requires careful treatment of atomic screening effects, especially when atoms are partially ionized. We present a fully analytic screening model based on a multi-Yukawa representation of the atomic potential, enabling the calculation of bremsstrahlung cross sections for arbitrary nuclear charge and ionization state, and electron energies up to a few tens of MeV. This framework extends prior treatments of neutral atoms to include partially ionized high-$Z$ elements in a fully analytic framework.
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Plasma Physics · Cross-listed from cs.LG

Uncertainty Guided Online Ensemble for Non-stationary Data Streams in Fusion Science

Kishansingh Rajput, Malachi Schram, Brian Sammuli et al. (+1 more)

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we present an application of online learning to continuously adapt to drifting data stream for prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. The results demonstrate that online learning is critical to maintain ML model performance and reduces error by 80% compared to a static model. Moreover, traditional online learning can suffer from short-term performance degradation as ground truth is not available before making the predictions. As such, we propose an uncertainty guided online ensemble method to further improve the performance. The Deep Gaussian Process Approximation (DGPA) technique is leveraged for calibrated uncertainty estimation and the uncertainty values are then used to guide a meta-algorithm that produces predictions based on an ensemble of learners trained on different horizon of historical data. The DGPA also provides uncertainty estimation along with the predictions for decision makers. The online ensemble and the proposed uncertainty guided online ensemble reduces predictions error by about 6%, and 10% respectively over standard single model based online learning.
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All authors Kishansingh Rajput, Malachi Schram, Brian Sammuli, Sen Lin

Medical Physics

Characterizing the Reliability of a Novel Upright CT for Proton Therapy

Yuhao Yan, Jordan Slagowski, Jessica Miller et al. (+4 more)

Purpose: To evaluate reliability of upright CT for proton dose calculation and feasibility of a simplified phantom configuration for accelerated routine QA. Methods: A calibration phantom was scanned on an upright CT following consensus guidelines for 14 sessions/7 months. CT number repeatability was assessed by standard deviation (SD). Stopping power ratio (SPR) look-up table was derived. Phantom size dependency was assessed. The simplified phantom configuration was scanned for 15 sessions/8 months. Repeatability was assessed. CT numbers and SPR were compared with consensus configuration. Both configurations were scanned on a recumbent CT to validate the findings. An anthropomorphic phantom was scanned on upright and recumbent CT. Targets were drawn mimicking spine and prostate tumor. Proton plans were developed using pencil beam scanning techniques and robust optimization. Equivalence of dose calculation were assessed via controlled comparisons. Results: Simplified configuration measured all CT numbers in 1 scan vs 5 for consensus guidelines. Upright CT demonstrated excellent longitudinal stability (inter- and intrasession SD <4.9 HU and 1.6 HU, respectively). Size dependency was identified with significant (p<.05) differences in CT numbers, propagated to $\Delta$SPR <5.3%. Significant (p<.05) differences were found comparing upright CT numbers measured by 2 configurations ($\Delta$SPR<2.6%). Recumbent CT showed smaller $\Delta$SPR (<0.7%). Both dosimetric comparison showed local differences (<8% of prescription dose) while clinical equivalence was found with target coverage differences <0.2% and gamma pass rates=100% at 3 mm/3% for all controlled comparison of different CT machines and phantom configurations. Conclusions: The upright CT demonstrated reliability to support adaptive proton therapy. The simplified configuration shows feasibility for rapid QA.
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All authors Yuhao Yan, Jordan Slagowski, Jessica Miller, John Hayes, Carson Hoffman, Minglei Kang, Carri Glide-Hurst

Computational Physics · Cross-listed from cs.LG

Energy Loss Functions for Physical Systems

Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy et al. (+1 more)

Effectively leveraging prior knowledge of a system's physics is crucial for applications of machine learning to scientific domains. Previous approaches mostly focused on incorporating physical insights at the architectural level. In this paper, we propose a framework to leverage physical information directly into the loss function for prediction and generative modeling tasks on systems like molecules and spins. We derive energy loss functions assuming that each data sample is in thermal equilibrium with respect to an approximate energy landscape. By using the reverse KL divergence with a Boltzmann distribution around the data, we obtain the loss as an energy difference between the data and the model predictions. This perspective also recasts traditional objectives like MSE as energy-based, but with a physically meaningless energy. In contrast, our formulation yields physically grounded loss functions with gradients that better align with valid configurations, while being architecture-agnostic and computationally efficient. The energy loss functions also inherently respect physical symmetries. We demonstrate our approach on molecular generation and spin ground-state prediction and report significant improvements over baselines.
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All authors Sékou-Oumar Kaba, Kusha Sareen, Daniel Levy, Siamak Ravanbakhsh

Plasma Physics

Detecting Shearless Phase-Space Transport Barriers in Global Gyrokinetic Turbulence Simulations with Test Particle Map Models

Norman M. Cao, Hongxuan Zhu, Gabriel C. Grime et al. (+1 more)

In magnetically confined fusion plasmas, the role played by zonal E$\times$B flow shear layers in the suppression of turbulent transport is relatively well-understood. However, less is understood about the role played by the weak shear regions that arise in the non-monotonic radial electric field profiles often associated with these shear layers. In electrostatic simulations from the global total-f gyrokinetic particle-in-cell code XGC, we demonstrate how shearless regions with non-zero flow curvature form zonal "jets" that, in conjunction with neighboring regions of shear, can act as robust barriers to particle transport and turbulence spreading. By isolating quasi-coherent fluctuations radially localized to the zonal jets, we construct a map model for the Lagrangian dynamics of gyrokinetic test particles in the presence of drift waves. We identify the presence of shearless invariant tori in this model and verify that these tori act as partial phase-space transport barriers in the simulations. We also demonstrate how avalanches impinging on these shearless tori cause reconnection events that form "cold/warm core ring" structures analogous to those found in oceanic jets, facilitating transport across the barriers without destroying them completely. We discuss how shearless tori may generically arise from tertiary instabilities or other types of discrete eigenmodes, suggesting their potential relevance to broader classes of turbulent fluctuations.
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All authors Norman M. Cao, Hongxuan Zhu, Gabriel C. Grime, Timothy Stoltzfus-Dueck