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Wednesday 25 February

Plasma Physics

Collisional-radiative data for tokamak disruption mitigation modeling

Prashant Sharma, Christopher J. Fontes, Dmitry V. Fursa et al. (+6 more)

Effective tokamak disruption mitigation is crucial for ensuring the safety and integrity of fusion power reactors. Accurate collisional-radiative (CR) modeling of a radiative plasma is a critical component in predictive disruption mitigation design. In this paper, we focus on quasi-steady-state CR modeling applicable to the current quench phase of a tokamak disruption. We employ the ATOMIC collisional-radiative code from the Los Alamos suite and the newly developed Fusion Collisional-Radiative (FCR) code to model the atomic processes, providing high-fidelity data for radiative power loss, as well as average and effective charge states for hydrogen, helium, neon, and argon plasma species over a wide range of tokamak-relevant electron temperatures and electron densities. Fine-structure-resolved CR models are used for hydrogen and helium plasma species, while configuration-average CR models are implemented for neon and argon plasma species. The calculated values are compared with the superconfiguration CR model (FLYCHK) and the commonly used coronal equilibrium approximation to demonstrate the advantages and limitations of each model. To facilitate coupling of high-fidelity CR data to plasma simulation models, we represent the ATOMIC/FCR results over the relevant plasma parameter range using a smooth tensor product B-spline surface in electron temperature and electron density. This approach yields compact coefficient tables that can be evaluated efficiently while preserving spline smoothness across the domain. These data were previously used to examine ways to minimize runaway electrons in a tokamak current quench, and they are now made available in easy-to-use forms for community use and benchmarking.
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All authors Prashant Sharma, Christopher J. Fontes, Dmitry V. Fursa, Igor Bray, Mark Zammit, James Colgan, Hyun-Kyung Chung, Nathan Garland, Xian-Zhu Tang

Applied Physics

Size-Dependent Properties of Miura-ori Tessellations

Chaewon Baek, Tomohiro Tachi, Jinkyu Yang et al. (+1 more)

We investigate the size-dependent behavior of Miura-ori-based origami tessellations by changing the number of origami unit cells. For large tessellations, the Miura-ori sheet generally exhibits a negative in-plane Poisson's ratio, whereas if the size of the Miura-ori tessellations becomes small, the transition between positive and negative Poisson's ratio emerges in the middle of the folding process. Here, we show that such a transitioning point, i.e., zero Poisson's ratio, yields a kinematic locking state. We also experimentally demonstrate the tunable locking behavior altered by tessellation sizes. Extending the analysis to three-dimensional origami tessellations, we find that the direction of kinematic locking changes depending on the tessellation size. Varying tessellation size thus enables control over both the onset and the direction of locking in origami metamaterials.
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All authors Chaewon Baek, Tomohiro Tachi, Jinkyu Yang, Hiromi Yasuda

Optics

Non-reciprocal electrooptic intermodal scattering with momentum engineered RF waves

Jieun Yim, Gwan In Kim, Violet Workman et al. (+4 more)

Spatiotemporal modulation approaches have been often employed as alternatives for producing optical non-reciprocity without magneto-optic materials. Unidirectional inter-modal scattering, enabled by either acousto-optic or electro-optic (EO) modulation, is a promising method in this category as it can directly modify optical dispersions and even enables linear non-reciprocal photonic devices in the strong coupling limit. While EO approaches are often preferred for their practicality, it is challenging to generate the large spatiotemporal momentum required for inter-modal phase matching without EO drive schemes involving multiple drive stimuli. Here, we demonstrate highly selective non-reciprocal inter-modal EO scattering enabled by a single high-index radiofrequency (RF) traveling wave stimulus. Our experimental demonstration is performed on a thin-film lithium niobate integrated photonics platform, in which we engineer a slow-wave radiofrequency (SWRF) transmission line with an effective RF index > 9 that natively generates the required RF momentum while simultaneously maintaining strong RF-optical mode overlap. By additionally engineering the interaction length, we achieve a directional ~20 dB non-reciprocal scattering contrast. The SWRF architecture provides a scalable route to magnetic-free non-reciprocity and establishes momentum-engineered RF waves as a powerful tool for next-generation, fully integrated non-reciprocal photonic systems.
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All authors Jieun Yim, Gwan In Kim, Violet Workman, Seho Kim, Omar A. Barrera, Ruochen Lu, Gaurav Bahl

Optics

Coupling nitrogen vacancy centers in silicon carbide to nanophotonic resonators

Ivan Zhigulin, Konosuke Shimazaki, Samuel M. Stephens et al. (+5 more)

Silicon carbide (SiC) is a promising platform for scalable quantum technologies owing to its well-established, wafer-scale industrial processing. SiC also hosts a variety of optically active color centres including the nitrogen vacancy defect that has a spin-triplet ground state. However, strong phonon coupling in the infrared range limits photon extraction from these defects. Here, we use nanophotonic structures, specifically micro-pillar and micro-disk resonators, to enhance optical collection and spin-readout. The micro-pillar geometry yields a 4-fold increase in photon collection, accompanied by a 2.4-fold reduction in spectral noise in optically detected magnetic resonance measurements. Consequently, the magnetic field sensitivity is improved by 24%. The large mode volume of the micro-disk supports resonances spanning 1150-1250 nm, enabling broadband coupling to nitrogen vacancy emission lines. Our results demonstrate that fabrication of scalable photonic structures efficiently improves performance of silicon carbide color centers for integrated quantum light generation and sensing.
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All authors Ivan Zhigulin, Konosuke Shimazaki, Samuel M. Stephens, Angus Gale, Karin Yamamura, Hark Hoe Tan, Igor Aharonovich, Mehran Kianinia

Physics and Society

Modeling plant disease spread via high-resolution human mobility networks

Varun K. Rao, Ryan Higgs, Hautahi Kingi et al. (+3 more)

Human mobility plays a crucial role in the spread of human diseases, but is rarely quantified in plant disease epidemics. To address this gap, we integrate a unique, high-resolution network of human movements in New Zealand with a metapopulation model to mechanistically simulate pathogen transmission. We calibrate the model on the nationwide 2010 kiwifruit vine disease (Psa-V) outbreak, and show that it accurately reproduces the observed spatiotemporal spread, confirming that the human mobility network is a strong foundation for modeling transmission dynamics. By analyzing spatial infection trends, we find that most dispersal occurs locally, as often illustrated in the plant-outbreak literature. However, sporadic long-range connections are necessary to model a nationwide outbreak. Using the model as an in-silico laboratory, we demonstrate that enhanced surveillance accelerates detection and that outbreak severity is highly sensitive to the timing and location of initial disease importation. We observe a potential causal link between seasonal labor patterns and epidemic risk in high-traffic seasons. This study provides a robust, data-driven framework for modeling and predicting the spatiotemporal spread of agricultural pathogens. It underscores the importance of leveraging human mobility networks to design timely interventions and surveillance systems, protecting global food security.
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All authors Varun K. Rao, Ryan Higgs, Hautahi Kingi, Filippo Radicchi, Santo Fortunato, Maria Litvinova

Instrumentation and Detectors

A novel perspective on crystal electromagnetic calorimeter design for the CEPC

Weizheng Song, Yang Zhang, Shengsen Sun et al. (+9 more)

Crystal electromagnetic calorimeters (ECALs) are essential for high-precision measurements of electrons and photons in particle physics experiments. However, the conventional design, in which long crystal bars point radially toward the interaction region and lack longitudinal segmentation, is incompatible with the three-dimensional shower imaging required by Particle Flow Approach (PFA). We propose a novel perspective on crystal ECAL design to address this limitation. The key innovation is a geometric reconfiguration in which crystal bars are oriented to face the interaction region and arranged orthogonally in adjacent longitudinal layers. This layout achieves fine spatial segmentation of energy deposits by correlating measurements of orthogonal crystal bars. An interleaved structure of regular and inverted trapezoidal modules is incorporated to maximize structural uniformity and detector hermeticity. This design is engineered to preserve the excellent intrinsic energy resolution of crystal ECALs while simultaneously providing the detailed three-dimensional shower imaging essential for PFA. Simulation results confirm the feasibility of achieving excellent energy resolution of $1.14\%/\sqrt{E} \oplus 0.44\%$. Consequently, the proposed design repositions crystal ECAL as a foundational component for PFA-oriented detector systems at facilities such as the Circular Electron Positron Collider (CEPC), offering a new technical pathway to advance the physics goals of future colliders.
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All authors Weizheng Song, Yang Zhang, Shengsen Sun, Fangyi Guo, Yuanzhan Wang, Linghui Wu, Jie Guo, Shaojing Hou, Yong Liu, Quan Ji, Jinfan Chang, Yifang Wang

Fluid Dynamics

Sorting prolate and oblate spheroids with a diatomic gas in a magnetic field

Ruben Lier

For a gas of diatomic particles with a nonzero magnetic moment, the Senftleben-Beenakker effect implies that transport can be affected by a magnetic field even when the particles are charge neutral. In such a gas, two independent anisotropic odd viscosities are allowed by symmetry and can have opposite signs. We study the resulting odd-viscous Stokes flow around spheroids in the regime where the odd viscosities are small compared to the shear viscosity. Using a perturbative expansion around the spherical solution and the Lorentz reciprocal theorem, we compute the lift forces on oblate and prolate spheroids for general values of the two odd viscosity coefficients. We show that the resulting Hall angles can differ for oblate and prolate spheroids, implying that sedimentation in a diatomic gas subject to a background magnetic field can separate particles according to shape.
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Computational Physics · Cross-listed from cs.LG

Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics

YuQing Xie, Ameya Daigavane, Mit Kotak et al. (+1 more)

$E(3)$-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of such networks is the tensor product, which allows interaction between different feature types. Because this operation scales poorly, there has been considerable work towards accelerating this interaction. However, recently \citet{xieprice} have pointed out that most speedups come from a reduction in expressivity rather than true algorithmic improvements on computing Clebsch-Gordan tensor products. A modification of Gaunt tensor product \citep{gaunt} can give a true asymptotic speedup but is incomplete and misses many interactions. In this work, we provide the first complete algorithm which truly provides asymptotic benefits Clebsch-Gordan tensor products. For full CGTP, our algorithm brings runtime complexity from the naive $O(L^6)$ to $O(L^4\log^2 L)$, close to the lower bound of $O(L^4)$. We first show how generalizing fast Fourier based convolution naturally leads to the previously proposed Gaunt tensor product \citep{gaunt}. To remedy antisymmetry issues, we generalize from scalar signals to irrep valued signals, giving us tensor spherical harmonics. We prove a generalized Gaunt formula for the tensor harmonics. Finally, we show that we only need up to vector valued signals to recover the missing interactions of Gaunt tensor product.
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All authors YuQing Xie, Ameya Daigavane, Mit Kotak, Tess Smidt

Instrumentation and Detectors · Cross-listed from hep-ph

Precision measurements of 2-3 oscillation parameters in the next-generation long-baseline experiments

Ritam Kundu

Over the past few decades, data from leading neutrino experiments have firmly established neutrino oscillation, implying non-zero neutrino masses and leptonic mixing and thereby providing confirmed evidence of physics beyond the Standard Model. On the backdrop of the precision era of neutrino oscillation, this thesis underscores its relevance by demonstrating the physics reach of the forthcoming long-baseline experiments – Deep Underground Neutrino Experiment (DUNE) and Hyper-Kamiokande (Hyper-K) – to establish non-maximal $\theta_{23}$, resolve the correct $\theta_{23}$ octant, and improve the precision on $\theta_{23}$ and $\Delta m^2_{31}$ by efficiently breaking parameter degeneracies. This is enabled by DUNE's high-resolution LArTPC detector and its wide-band beam, achieving sensitivity at a high confidence level compared to the global fits of world neutrino data. The combined analysis of DUNE and Hyper-K not only significantly enhances sensitivity to these phenomenological studies but also demonstrates their capabilities at lower exposures when operated together, relative to their nominal individual exposures. In addition, we investigate the impact of flavor-dependent long-range interactions arising from anomaly-free U(1)' extensions of the Standard Model, showing that although subdominant long-range interactions can substantially influence the sensitivity and precision of oscillation parameter measurements, the complementary strengths of DUNE and Hyper-K mitigate these challenges to a large extent.
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Tuesday 24 February

General Physics

Rainbow formation: from Descartes to Venus

Andrey Zaikin

The mechanism of rainbow formation proposed by Descartes and Newton is analyzed. The parameters of rainbows on Venus are calculated using geometric optics. Assuming that solar radiation is refracted by spherical droplets of aqueous sulfuric acid solution, the angular dimensions of the primary and secondary rainbows are determined as functions of the solution's concentration. It is shown that changes in concentration have the greatest effect on the size of the Alexander's dark band, the angular distance between the primary and secondary rainbows.
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General Physics

A state chaining-based objective collapse model

Roman V. Li

The quantum-to-classical transition hinges on the nature of wavefunction collapse, which remains a central controversy in foundational physics. Objective collapse theories aim to modify quantum mechanics by introducing a physical, non-subjective mechanism for irreversible events, but existing models face significant conceptual and empirical challenges. Here, we propose a novel collapse mechanism based on a specific form of quantum correlation termed "chaining", formalized within a new diagrammatic framework (quantum illustrations, or qils). This approach does not rely on system size or environmental complexity, but on the probabilistic occurrence of a collapse event with a fixed, universal probability $1/\Sigma$ per chaining step. We demonstrate that this model naturally explains the emergence of classicality in paradigmatic scenarios (measurement devices, Schrodinger's cat, spontaneous decay) and makes testable predictions for interference experiments. The theory is shown to be consistent with existing data from delayed-choice quantum eraser and matter-wave interference experiments, yielding an estimate for the fundamental constant $\Sigma \geq 1.5$. By providing a unified, parameter-sparse mechanism for objective collapse, this work bridges quantum and classical descriptions and has implications for the interpretation of quantum experiments, the design of quantum computers/sensors, and the understanding of decoherence in complex systems.
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Computational Physics · Cross-listed from cs.GR

Physics-Informed Video Diffusion For Shallow Water Equations

Yang Bai, George Eskandar, Ziyuan Liu et al. (+1 more)

Traditional fluid dynamics simulation pipelines combine numerical solvers with rendering, producing highly realistic results but at considerable computational cost. Diffusion-based generative video models offer a faster alternative, yet often ignore physical laws and thus fail to capture consistent dynamics. We propose a physics-informed video diffusion framework that jointly generates visual outputs and physical states. Unlike prior two-stage approaches that first simulate the physical variables and then render, we directly integrate physics constraints into the generative process, enabling simultaneous prediction of physical states and realistic videos without a separate rendering step. Built on the two-dimensional shallow water equations with terrain topography, our method produces temporally coherent water flow while maintaining physical plausibility. Experiments show that it outperforms purely data-driven video diffusion baselines in both realism and physical fidelity, while generating videos significantly faster than traditional simulation-plus-rendering pipelines.
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All authors Yang Bai, George Eskandar, Ziyuan Liu, Gitta Kutyniok

Chemical Physics

Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective

Sanjanasri JP, Pratiti Bhadra, N. Sukumar et al. (+1 more)

Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields.
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All authors Sanjanasri JP, Pratiti Bhadra, N. Sukumar, Soman KP

Geophysics

Physics-constrained symbolic regression for discovering closed-form equations of multimodal water retention curves from experimental data

Yejin Kim and Hyoung Suk Suh

Modeling the unsaturated behavior of porous materials with multimodal pore size distributions presents significant challenges, as standard hydraulic models often fail to capture their complex, multi-scale characteristics. A common workaround involves superposing unimodal retention functions, each tailored to a specific pore size range; however, this approach requires separate parameter identification for each mode, which limits interpretability and generalizability, especially in data-sparse scenarios. In this work, we introduce a fundamentally different approach: a physics-constrained machine learning framework designed for meta-modeling, enabling the automatic discovery of closed-form mathematical expressions for multimodal water retention curves directly from experimental data. Mathematical expressions are represented as binary trees and evolved via genetic programming, while physical constraints are embedded into the loss function to guide the symbolic regressor toward solutions that are physically consistent and mathematically robust. Our results demonstrate that the proposed framework can discover closed-form equations that effectively represent the water retention characteristics of porous materials with varying pore structures. To support third-party validation, application, and extension, we make the full implementation publicly available in an open-source repository.
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Computational Physics · Cross-listed from q-bio.NC

Characterization of Phase Transitions in a Lipkin-Meshkov-Glick Quantum Brain Model

Elvira Romera and Joaquín J. Torres

In this work we analyze the emergence of phase transitions in a quantum brain model inspired by the Lipkin-Meshkov-Glick framework, where biologically motivated synaptic feedback modulates the collective interaction in a nonlinear and state-dependent manner. We demonstrate that incorporating this retroactive mechanism substantially reshapes the phase structure, yielding an expansion of the paramagnetic phase at the expense of the ferromagnetic phases relative to the feedback-free scenario. This effect is markedly enhanced in the presence of a longitudinal field, as the feedback couples directly to the longitudinal magnetization, leading to an appreciable displacement of the critical boundaries. We characterize the ensuing transitions from a phase-space perspective by means of the ground-state Husimi distribution and the Wehrl entropy, which provide a robust diagnosis of qualitative changes in localization and enable a quantitative assessment of feedback-induced deformations of the phase diagram. Additionally, we perform an explicit dynamical analysis based on mean-field equations for the collective-spin orientation self-consistently coupled to the synaptic dynamics, which reproduces with high fidelity the quantum time evolution of collective observables for the protocols considered. Overall, these findings substantiate the suitability of this quantum brain model as a controlled theoretical framework for elucidating how synaptic plasticity mechanisms can parametrically tune and reshape collective criticality.
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Popular Physics · Cross-listed from astro-ph.GA

Constraining the Lifespan of Intelligent Technological Civilization in the Galaxy

Sohrab Rahvar and Shahin Rouhani

In this work, we explore constraints on the emergence and longevity of technologically intelligent civilizations in our Galaxy, considering the Fermi paradox. We argue that under optimistic assumptions about the probability of life and intelligence emerging on Earth-like planets, the absence of contact with extraterrestrial civilizations imposes limits on their lifespan. Our analysis suggests that if intelligent life is common, technological civilizations must be relatively short-lived, with lifetimes constrained to $\lesssim 5\times10^3$ years under our most optimistic scenario. Considering electromagnetic communication, we note that our current light cone encompasses the entire Galactic history over the past $\sim 10^5$ years, making the lack of detected signals particularly puzzling for long-lived civilizations. We emphasize that these results should be interpreted as upper bounds derived from the Fermi paradox, not as predictions of actual lifespans.
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Instrumentation and Detectors

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

Julia Gonski, Jennifer Ott, Shiva Abbaszadeh et al. (+118 more)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.
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All authors Julia Gonski, Jennifer Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Dickinson, Giuseppe Di Guglielmo, Yigit Erdem Ertorer, Lindsey Gray, Ryan Herbst, Christian Herwig, Tae Min Hong, Benedikt Maier, Maryam Bayat Makou, David W. Miller, Mark S. Neubauer, Cristian Pena, Dylan Rankin, Seon-Hee Seo, Giordon Stark, Alexander Tapper, Audrey Corbeil Therrien, Ioannis Xiotidis, Keisuke Yoshihara, Nural Akchurin, Carlos Arguelles, Saptaparna Bhattacharya, Lorenzo Borella, Christian Boutan, Tom Braine, James Brau, Marco Carminati, Antonio Chahine, Yuan-Tang Chou, Talal Ahmed Chowdhury, Seokju Chung, Ryan Coffee, Alberto Coppi, Mariarosaria D'Alfonso, Abhilasha Dave, Chance Desmet, Angela Di Fulvio, Karri DiPetrillo, Javier Duarte, Auralee Edelen, Jan Eysermans, Yongbin Feng, Emmett Forrestel, Julian Garcia Pardinas, Loredana Gastaldo, Lino Gerlach, Abarajithan Gnaneswaran, Loukas Gouskos, Katya Govorkova, Carl Grace, Christopher Grant, Philip Harris, Ciaran Hasnip, Timon Heim, Abraham Holtermann, Gian Michele Innocenti, Koji Ishidoshiro, Miaochen Jin, Jyothisraj Johnson, Stephen Jones, Andreas Jung, Nicholas Kamp, Georgia Karagiorgi, Ryan Kastner, Doojin Kim, Kyoungchul Kong, Katie Kudela, Bo-Cheng Lai, Yun-Tsung Lai, Jelena Lalic, Tommy Lam, Jeffrey Lazar, Aobo Li, Zepeng Li, Haoyun Liu, Qibin Liu, Vladimir Loncar, Zhenghua Ma, Luca Macchiarulo, Christopher Madrid, Prashansa Mukim, Victoria Nguyen, Sungbin Oh, Isobel Ojalvo, Hideyoshi Ozaki, Simone Pagan Griso, Santosh Parajuli, Myeonghun Park, Benjamin Parpillon, Christoph Paus, Sara Pozzi, Ema Puljak, Benjamin Ramhorst, Amy Roberts, Larry Ruckman, Sebastian Schmitt, Kate Scholberg, Noah Singer, Eluned Anne Smith, Alexandre Sousa, Michael Spannowsky, Kaito Sugizaki, Sioni Summers, Yanwen Sun, Daniel Tapia Takaki, Antonino Tumeo, Caterina Vernieri, Belina von Krosigk, Yash Vora, Linyan Wan, Michael H. L. S. Wang, Amanda Weinstein, Andy White, Simon Williams, Liangyu Wu, Felix Yu

Popular Physics

Black holes and bits: A simple path to Bekenstein-Hawking entropy

Jorge Pinochet

In the early 1970s, Jacob Bekenstein discovered that black holes have entropy, which became one of the greatest scientific revolutions of the second half of the 20th century. The objective of this paper is to present a simple derivation – partly heuristic and partly geometric – of the equation for the entropy of a black hole, which we now know as the Bekenstein-Hawking entropy. We will also briefly explore the physical implications of this equation and its relationship to the work of Stephen Hawking.
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Medical Physics

Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation

Li Feng and Hersh Chandarana

The inherently slow acquisition speed of MRI makes abdominal imaging highly sensitive to respiratory motion artifacts. Since the early days of MRI, the development of respiratory motion compensation has been an active research topic, and this field has achieved substantial technical progress. Despite these advances, majority of these techniques are not clinically available, and motion management methods used in clinical abdominal MRI today have changed little over the past decades. This observation is striking and points to a significant gap between technical innovation and clinical translation in this area. This review is motivated by this question: why have so many motion management techniques not been adopted into routine clinical workflows? Unlike conventional survey-style reviews that focus on summarizing emerging methods, this article takes a different, and perhaps opposite, perspective to investigate why those technologically sophisticated innovations are misaligned with practical clinical needs. Specifically, we discuss the barriers behind the gap between research advances and clinical practice, clarify the clinical requirements for effective respiratory motion management in abdominal MRI, and highlight research directions with stronger relevance to routine workflows. The review begins with an overview of the clinical impact of respiratory motion in abdominal MRI, followed by a discussion of standard abdominal MRI sequences and their motion sensitivity. We then summarize current clinical strategies and advanced approaches, along with the barriers that hinder their clinical adoption. The article concludes with future directions and broader lessons learned from this translational gap, with the goal of guiding future developments towards improved clinical integration.
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Atomic Physics

Efimov Effect in Ultracold Microwave-Shielded Polar Molecules

Shayamal Singh and Chris H. Greene

A quantum-mechanical description is presented for the three-body physics of shielded dipolar molecules, including a prediction of observable Efimov physics. Despite the anisotropic and long-range nature of the interaction, shielding enables a regime in which universality emerges already at the two-body level and extends to the three-body sector, where Efimov physics emerges. On the negative side of the scattering-length resonance, computed trimer binding energies display the characteristic scaling expected for Efimov resonances. Finally, the sudden approximation can be used to create trimer bound states, starting from positive energy trap states as a way to create or detect these molecular trimers. Moreover, the three-body parameter expressed in dipolar units is found to be universal.
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Plasma Physics

Unstable magnetic reconnection self-generates turbulence

Nick Williams, Alessandro De Rosis and Alex Skillen

Magnetic reconnection and turbulence are deeply intertwined in magnetohydrodynamic flows, yet how reconnection self-generates turbulence remains unclear. Using an ensemble of high-resolution three-dimensional direct numerical simulations of an unstable magnetised jet with an initially weak mean field, we demonstrate a self-sustained transition from laminar reconnection to fully developed turbulence in the absence of external forcing. We show that a three-dimensional current-sheet instability triggers stochastic reconnection, leading to persistent turbulent energy injection. Energy-budget analysis reveals that the coupling between the turbulent electromotive force and the magnetic mean shear dominates turbulent production, with magnetic fluctuations subsequently transferring energy to the kinetic field through a nonlinear cascade.
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Medical Physics

A CFD-Based Investigation of Local Luminal Curvature as a Primary Determinant of Hemodynamic Environments in Cerebral Aneurysms

Marcella P. A. Dallavanzi, José L. Gasche and Iago L. Oliveira

The relationship between vascular morphology and hemodynamics is fundamental to understanding the natural history of cerebral aneurysms (CAs). While global geometric indices have been widely studied, the local interaction between luminal curvature and wall shear stress (WSS) remains poorly characterized. This study analyzed a large cohort of CAs to investigate how local surface morphology relates to hemodynamics. This was performed via CFD flow simulations of a set of 76 patient-specific CA geometries using the OpenFOAM library. Geometry and pulsatile inflow conditions were modeled based on patient arterial diameter and age. Blood was assumed to be a Newtonian incompressible fluid flowing in a laminar regime. We utilized a geometric framework to classify the aneurysm lumen into spherical-like and saddle-like patches based on Gaussian curvature. Our results demonstrate a robust, statistically significant correlation between these curvature types and hemodynamic metrics, regardless of rupture status or aneurysm type. Specifically, saddle-like patches, predominantly found at the aneurysm neck, are associated with high time-averaged WSS, a low oscillatory shear index, and intense near-wall vortical activity as identified by the lambda2-criterion. In contrast, spherical-like patches, dominant at the dome, correspond to regions of flow impingement characterized by lower time-averaged WSS and an elevated oscillatory shear index. These findings suggest that wall curvature is a primary determinant of local hemodynamics. By bridging the gap between local wall morphology and pathological wall markers, this work suggests that curvature-based mapping can serve as a powerful tool for identifying vulnerable regions susceptible to thinning and rupture. This objective geometric assessment offers valuable insights for risk stratification and the precision planning of endovascular interventions.
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Applied Physics

Mid-Infrared Thermal Radiation Harvesting using Uncooled Narrow Bandgap GeSn Thermophotovoltaic cell

Gérard Daligou, Mahmoud R. M. Atalla, Cédric Lemieux-Leduc et al. (+4 more)

Thermophotovoltaic (TPV) cells are increasingly attractive for applications in industrial waste heat harvesting, aerospace energy management, and compact power generation. Deploying midwave-infrared (MWIR) TPV in practical applications requires narrow-bandgap semiconductors that not only absorb low-energy photons but also integrate with scalable, low-cost platforms. Although high-performance TPV devices have been demonstrated using III-V materials such as InAs, GaSb, and InGaAs(P), their use remains limited by cost and substrate size. With this perspective, narrow bandgap GeSn alloys are a promising alternative that extend group-IV absorption into the MWIR while being silicon-compatible. Although the potential of GeSn TPV cells has been predicted, no experimental demonstration has been reported. Here, proof-of-concept Ge$_{0.91}$Sn$_{0.09}$ p-i-n TPV diodes (1 mm diameter) grown on silicon were fabricated and their performance was benchmarked against commercial InAs and extended-InGaAs devices. Measurements at 300 K under 2.33 $\mu$m laser and $\sim$1500 K SiC Globar illumination revealed peak responsivity of $\sim$ 0.2 A/W at $\sim$ 1.7 $\mu$m, and an output power of $\sim$ 0.41 mW/cm$^2$. These devices show trends comparable to those of the InAs diode under identical conditions, although at reduced absolute levels. To assess the intrinsic performance potential, Poisson-drift-diffusion modeling incorporating experimentally calibrated emitter emissivity predicts power densities exceeding 1 W/cm$^2$ under moderate MWIR thermal illumination, indicating that the present devices operate far below their fundamental limits and are primarily constrained by defect-assisted recombination and transport losses. These results establish GeSn as a scalable, silicon-compatible MWIR TPV platform and highlight a larger performance potential achievable through material and device optimization.
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All authors Gérard Daligou, Mahmoud R. M. Atalla, Cédric Lemieux-Leduc, Anthony Nomezine, Simone Assali, Richard Soref, Oussama Moutanabbir

Chemical Physics

The Effect of Base-Pairing on the Shape Resonances of Nucleobases

Jishnu Narayanan S J, Divya Tripathi, Idan Haritan et al. (+3 more)

In this work, we have studied the effect of base-pairing on the shape resonances of guanine and cytosine nucleobases. Among the seven {\pi}* resonances we identified in the guanine-cytosine (GC) anion radical, three were centered on cytosine, and the remaining were guanine-centered. Relative to the isolated bases, upon base pair formation, the cytosine resonances were red shifted, while the guanine-centered states showed an opposite trend - where their energy was blue shifted. In addition to the electronic interactions, geometric distortion and basis set superposition error plays a crucial role in the resonance positions and widths of the GC radical anion. The electronic interaction from the complementary base seems to have a larger effect on the stabilization of the anionic resonances than the surrounding environment.
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All authors Jishnu Narayanan S J, Divya Tripathi, Idan Haritan, Amitava Adhikary, Bhawana Pandey, Achintya Kumar Dutta

Plasma Physics

Passive freeze-out of the Richtmyer-Meshkov instability

J. Strucka, D. M. Sterbentz, B. Lukic et al. (+16 more)

The Richtmyer-Meshkov instability (RMI) poses a major challenge in inertial confinement fusion (ICF) due to its role in mixing and performance degradation. We report the first experimental observation of passive freeze-out of RMI in a low-pressure surrogate regime; an instability stagnation effect induced without modifying the driving pressure pulse or the target surface geometry. Using additively manufactured sub-surface voids in a sinusoidal target, we convert a single shock into a sequence of weaker shocks that suppress instability growth upstream of the surface by over 70%. High-speed X-ray imaging and hydrodynamic simulations suggest that this suppression arises primarily from temporal shaping, with lesser contributions from spatial curvature and shock weakening. Our results demonstrate a driver-independent pathway for controlling shock-driven hydrodynamic instabilities relevant to ICF and other high energy density systems.
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All authors J. Strucka, D. M. Sterbentz, B. Lukic, K. Mughal, Y. Yao, K. Marrow, W. J. Schill, C. F. Jekel, D. A. White, N. Asmedianov, R. Grikshtas, O. Belozerov, S. Efimov, J. Skidmore, A. Rack, Ya. E. Krasik, J. L. Belof, J. P. Chittenden, S. N. Bland

Optics

Fiber Link Stabilization with a Multicore Fiber Amplifier

Yifan Liu, Takuma Nakamura, Daniel J. Elson et al. (+7 more)

We study the use of separate cores of a multicore erbium-doped fiber amplifier (MC-EDFA) in a noise-canceled link for ultrastable optical frequency transfer. We demonstrate fractional frequency instability of $5\times10^{-19}$ at 1000 s averaging time for the stabilized MC-EDFA alone and $1.4\times10^{-18}$ at 1000 s averaging time when integrated with a 40 km-long 7-core spooled fiber. This study further establishes multicore fiber (MCF) networks as a promising platform for ultrastable frequency transfer, serving as an important step toward incorporating precision time and frequency distribution into future MCF communication infrastructures.
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All authors Yifan Liu, Takuma Nakamura, Daniel J. Elson, Yuta Wakayama, Charles A. McLemore, Tetsuya Hayashi, Mikael Mazur, Nicolas Fontaine, Franklyn Quinlan, Nazanin Hoghooghi

Physics and Society

Structurally Conditioned Diffusion Reproduces Skills-Based Stratification

Roberto Cantillan and Mauricio Bucca

Occupational hierarchies remain strikingly stable even as job content changes rapidly. We ask whether skill requirements propagate directionally along the wage hierarchy or follow symmetric diffusion. Using O*NET 2015-2024, we analyze 17.3 million directed diffusion opportunities linking 873 occupations and 161 skills. We show that propagation obeys an Asymmetric Trajectory Channeling (ATC) rule: the same requirement spreads differently upward and downward, and the asymmetry depends on skill domain and on the architecture of skill dependencies. Two mechanisms generate ATC. Directional incorporation asymmetry implies that wage gradients create distinct receiving environments: upward-moving socio-cognitive requirements encounter complementary infrastructure, whereas upward-moving sensory/physical requirements face structural indifference. Structural portability constraints imply that dependency position governs portability: requirements anchoring long prerequisite chains carry co-adoption burdens that restrict diffusion regardless of destination. Consistent with these mechanisms, socio-cognitive requirements propagate upward more often than downward (20.7% vs 14.9%), while sensory/physical requirements exhibit the mirror pattern (19.5% downward vs 10.3% upward). Nestedness amplifies these asymmetries in opposite directions: scaffolding capabilities ascend most readily, whereas structurally embedded physical requirements are most tightly confined. Identification leverages within-occupation variation in propagation direction, and results are robust to origin- and destination-side specifications. Together, these findings reveal a directional architecture of occupational change that can reproduce hierarchy through ongoing reconfiguration, even absent assortative preferences or coordinated action.
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Fluid Dynamics · Cross-listed from cond-mat.soft

Chapman-Enskog expansion for chirally colliding disks

Ruben Lier and Paweł Matus

We study a two-dimensional gas of hard disks undergoing chiral, energy- and momentum-conserving collisions. We propose a simple chiral collision rule and show that it can effectively capture collisions of spinning noncircular particles without retaining spin or orientation as kinetic variables. Despite the microscopic breaking of time-reversal symmetry, the H-theorem is obeyed, guaranteeing relaxation towards equilibrium in the absence of external forces. A Chapman-Enskog expansion yields analytical expressions for the shear and odd viscosity and the thermal conductivity. Theoretical predictions are confirmed by nonequilibrium molecular dynamics simulations
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Optics

A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography

Oliver Hoidn, Steven Henke, Albert Vong et al. (+3 more)

Ptychographic imaging at synchrotron and X-ray free-electron laser sources requires densely overlapping scans, which limits throughput and increases dose; extending coherent diffractive imaging to overlap-free operation on extended samples remains an open problem. We present a self-supervised inverse-mapping network for single-frame Fresnel coherent diffraction imaging (CDI) and overlapped ptychography with fixed, pre-estimated probes. The learned neural network reconstructs individual object patches from either one diffraction frame or several overlapping measurements at a time. In single-frame mode, the phase diversity provided by the curved-wavefront probe at the off-focus sample position removes the requirement for overlap constraints, enabling sparser scans and proportionally lower dose at fixed exposure. On synthetic line patterns, reconstructed amplitude SSIM exceeds 0.90 in single-frame mode with the curved probe and reaches 0.952-0.968 with overlap constraints. Optimization of the network via a Poisson negative log likelihood objective, rather than the more common mean absolute error, yields 10-fold improved photon-dose efficiency at doses below $10^5$ photons per image, where shot noise typically limits resolution. In addition to these synthetic studies, we demonstrate robust single-frame reconstruction of extended samples using ptychographic datasets from APS and LCLS, with end-to-end reconstruction of a 10,304-frame workload approximately $36\times$ faster than a highly optimized iterative solver. Together, these results unify single-frame Fresnel CDI and overlapped ptychography within one self-supervised framework, supporting dose-efficient, high-throughput imaging at modern light sources.
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All authors Oliver Hoidn, Steven Henke, Albert Vong, Aashwin Mishra, Apurva Mehta, Matthew Seaberg

Medical Physics

Novel Asymmetrical High-Resolution and High-Sensitivity Brain Dedicated PET system: Design optimization and performance evaluation

Yuemeng Feng, Lisa Blackberg, Arkadiusz Sitek et al. (+1 more)

Objective. This study investigates the best achievable performance of a brain-dedicated PET system with high resolution and sensitivity by evaluating different detector configurations, while maintaining a practical system design suitable for dynamic brain Imaging. Approach. Monte Carlo simulations were performed to evaluate system sensitivity and image quality under various timing resolutions (200 ps, 100 ps and 50 ps). The PET scanner geometry was optimized for human head imaging, featuring an elliptical cylindrical configuration with a neck cut-out, and front/back panels to enhance sensitivity and line of response (LOR) sampling. Detector configurations using LYSO:Ce crystals of varying thicknesses (15 mm and 20 mm) and depth of interaction (DOI) levels were simulated. Sensitivity was calculated using a point-like 511 keV back-to-back gamma source simulated at multiple locations within the field of view (FOV). Image reconstruction was conducted using list mode Maximum Likelihood Expectation Maximization (MLEM), assessing both a Derenzo-type phantom and a voxelated digital brain phantom. Main results. A location-dependent sensitivity ranging from 35.04% to 13.59% was achieved using a 20 mm thick LYSO:Ce crystal. Spatial resolution ranged from 0.8 mm to 1.5 mm within the FOV without applying resolution recovery techniques, measured using the FWHM of reconstructed hot rods. Previous results using 15 mm crystals with three DOI levels showed sensitivity between 23.42% and 15.99%, confirming the performance benefits of increased detector thickness and DOI capability. Significance. This study demonstrates the potential of a practical, brain-optimized PET system to achieve superior resolution and sensitivity for brain imaging. The findings offer valuable insights into optimal detector configurations, supporting the development of next-generation high-performance brain PET technologies.
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All authors Yuemeng Feng, Lisa Blackberg, Arkadiusz Sitek, Hamid Sabet