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Physics Physics

physics Watch Physics

Friday 27 February

Instrumentation and Detectors

Design of a high voltage delivery system for noble liquid time projection chambers

R. Saldanha, L. Pagani, E. Angelico et al. (+129 more)

Noble liquid time projection chambers (TPCs) are a leading technology in the detection of ionizing radiation, particularly in applications such as accelerator neutrino physics, dark matter detection, and neutrinoless double beta decay. This paper addresses the design considerations for implementing stable high voltage (HV) systems within large noble liquid TPCs, with a focus on the nEXO experiment. Utilizing insights from prior HV research and experimental investigations, we outline factors influencing HV stability and discuss design choices to improve stability and prevent electrical discharges. A novel HV delivery system concept is presented, tailored for the nEXO TPC, which incorporates these design considerations while also meeting the stringent radiopurity requirements of the nEXO neutrinoless double beta decay search. These design considerations and their specific implementation towards a HV delivery system offer guidance to future experiments applying high voltage in noble liquid environments.
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All authors R. Saldanha, L. Pagani, E. Angelico, E. P. Bernard, B. Chana, S. Delaquis, R. DeVoe, M. Elbeltagi, S. Ferrara, D. Goeldi, R. Gornea, A. Odian, G. S. Ortega, C. T. Overman, L. Placzek, P. C. Rowson, K. Skarpaas, F. Spadoni, P. Acharya, A. Amy, A. Anker, I. J. Arnquist, A. Atencio, J. Bane, V. Belov, T. Bhatta, A. Bolotnikov, J. Breslin, P. A. Breur, E. Brown, T. Brunner, B. Burnell, E. Caden G. F. Cao, L. Q. Cao, D. Cesmecioglu, D. Chernyak, M. Chiu, R. Collister, M. Marquis, T. Daniels, L. Darroch, M. L. di Vacri, Y. Y. Ding, M. J. Dolinski, B. Eckert, A. Emara, N. Fatemighomi, W. Fairbank, B. Foust, D. Gallacher, N. Gallice, A. Gaur, W. Gillis, F. Girard, G. Gratta, C. A. Hardy, S. Hedges, E. Hein, J. D. Holt, A. Iverson, X. S. Jiang, A. Karelin, D. Keblbeck, A. Kuchenkov, K. S. Kumar, A. Larson, M. B. Latif, S. Lavoie, K. G. Leach, B. G. Lenardo, K. K. H. Leung, H. Lewis, G. Li, X. Li, Z. Li, C. Licciardi, R. Lindsay, R. MacLellan, S. Majidi, J. Masbou, M. Medina-Peregrina, S. Mngonyama, B. Mong, D. C. Moore, K. Ni, I. Nitu, A. Nolan, S. C. Nowicki, J. C. Nzobadila Ondze, J. L. Orrell, A. Pena-Perez, H. Peltz Smalley, A. Piepke, A. Pocar, E. Raguzin, R. Rai, H. Rasiwala, D. Ray, S. Rescia, F. Retiere, G. Richardson, V. Riot, N. Rocco, R. Ross, S. Sangiorgio, S. Sekula, T. Shetty, L. Si, V. Stekhanov, X. L. Sun, S. Thibado, T. Totev, S. Triambak, R. H. M. Tsang, O. A. Tyuka, E. van Bruggen, M. Vidal, S. Viel, Q. D. Wang, M. Watts, W. Wei, M. Wehrfritz, L. J. Wen, S. Wilde, M. Worcester, X. M. Wu, H. Xu, H. B. Yang, L. Yang, M. Yu, O. Zeldovich, J. Zhao

Instrumentation and Detectors

Characterization of UV optical components for photon detector calibration in liquid argon TPCs

B. Behera, M. Bilal Azam, Z. Djurcic et al. (+13 more)

Large liquid argon time projection chambers (LArTPCs) require stable and well-characterized delivery of ultraviolet (UV) light for in situ calibration of photosensors at cryogenic temperatures. This article reports bench-top and cryogenic measurements of the optical components used in a UV light calibration system, including multi-mode fused-silica fibers, SMA-to-SMA connectors, optical fiber feedthroughs, and light-diffuser assemblies. Light loss in several fiber types and SMA connectors was measured across wavelengths from \qtyrange{275}{970}{nm}. In addition, light-loss measurements of the tested fibers after several liquid-nitrogen thermal cycles showed no statistically significant degradation relative to baseline measurements, and high-rate pulsed exposure (30-90 million pulses from a \qty{275}{nm} LED) likewise showed no measurable aging in jacketed fibers. A compact, palm-sized, 3D-printed PEEK diffuser housing with stacked UV-grade fused-silica diffusers yields Lambertian emission and the most uniform angular distribution. Optical components exhibiting improved UV transmission were deployed successfully in multiple DUNE small- and large-scale prototypes, demonstrating reliable operation of UV light calibration systems. These findings inform component selection and calibration procedures for achieving reliable, uniform UV light delivery in large-scale cryogenic detectors such as DUNE.
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All authors B. Behera, M. Bilal Azam, Z. Djurcic, A. Heindel, I. Helgeson, T. Hyden, D. Leon Silverio, S. Magill, D. A. Martinez Caicedo, M. Oberling, K. Pickner, A. Rafique, J. Rodríguez Rondon, D. Torres Muñoz, C. Winkers, L. Xia

Thursday 26 February

Medical Physics

Crossover Frequency as a Model-Independent Viscoelastic Constant for Soft Tissue Biomechanics

Laura Ruhland, Jing Guo, Ingolf Sack et al. (+1 more)

Magnetic resonance elastography (MRE) and related elastography techniques are emerging as quantitative diagnostic tools for assessing tissue microstructure and pathology. To determine descriptive parameters of the tissues' properties, a frequency-dependent viscoelastic material model is required, which is calibrated to the measured response in a parameter identification process. However, the selection of this model and the fitting strategy is challenging, since it may influence the identified viscoelastic parameters notably. Here, we address this limitation by proposing the crossover frequency (fc, defined as the frequency at which storage and loss moduli intersect G'(fc) = G”(fc)) as a model-independent viscoelastic constant for soft tissues. Fresh porcine specimens of the corona radiata, the putamen, the thalamus, and the liver were investigated using tabletop MRE and the frequency-dependent viscoelasticity was characterized with a fractional Kelvin-Voigt model. By validating the crossover frequency against the viscoelastic parameters, we demonstrated that the crossover frequency accurately reflects the viscoelastic behavior, independent of the material model or the fitting strategy. Across all samples, fc distinguished brain regions and separated brain from liver tissue by median frequencies of 85Hz (95% CI: 69-269Hz) in the corona radiata, 423Hz (95% CI: 316-575Hz) in the putamen, 426Hz (95% CI: 302-601Hz) in the thalamus and 1174Hz (95% CI: 1074-1300Hz) in the liver (p<0.001). These results suggest that crossover frequencies capture distinct viscoelastic fingerprints without requiring viscoelastic model selection. The crossover frequency may therefore serve as a practical, model-independent biomaterial constant to improve comparability of viscoelastic measurements across elastography studies.
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All authors Laura Ruhland, Jing Guo, Ingolf Sack, Kai Willner

Chemical Physics

Drifting to Boltzmann: Million-Fold Acceleration in Boltzmann Sampling with Force-Guided Drifting

Pipi Hu

Sampling molecular conformations from the Boltzmann distribution is essential for computational chemistry, but iterative diffusion methods are prohibitively slow. Drifting Models offer one-step generation, yet their equilibrium matches the training distribution, which may deviate from the true Boltzmann distribution due to sampling bias. We introduce Drifting Models to molecular conformation generation for the first time, establishing a theoretical bridge via the Drifting Score Identity: for Gaussian kernels, the drifting field's attraction equals a kernel-weighted average of any distribution's score function. Substituting molecular force labels – which directly encode the Boltzmann score – yields the Drifting Force Identity and decomposes the field into standard drift plus a Boltzmann correction. We further discover a striking phenomenon unique to molecular systems: force incorporation's effectiveness reverses across representations. In coordinate space, Force-Interpolated Drifting (FI) dominates by blending physical force directions with data displacements. In distance feature space, Force-Aligned Kernel (FK) achieves superior accuracy by modifying only kernel weights, thereby preserving the manifold of geometrically valid molecules. On MD17 Ethanol, both approaches achieve one-step generation with over 1000x speedup relative to recent score-matching methods with Boltzmann guiding, providing more than million-fold acceleration over traditional molecular dynamics, while ensuring perfect structural validity and distributional accuracy rivaling multi-step methods.
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Chemical Physics

Chemical Reaction Engineering and Catalysis: AI/ML Workflows and Self-Driving Laboratories

Rigoberto Advincula and Jihua Chen

Chemical reaction engineering is key to industrial might and sustainable chemistry. This will be enabled using smart, efficient catalysts or catalysis ecosystems. This is possible with advanced artificial intelligence and machine learning (AI/ML) workflows that need to be employed as agentic AI projects. The fundamentals of catalysis need to be emphasized. A strong focus on catalyst design, mechanistic studies, reaction engineering, and scale-up must use ML-driven workflows, along with high-throughput experimentation (HTE) and an autonomous, self-driving laboratory (SDL). Laboratory experience and data-driven approaches are valuable when working together to accelerate this development. Parametrize and create a virtuous circle for data-driven discovery across heterogeneous, homogeneous, and biocatalysts to enable utility in many chemical process industries as agentic AI tasks. This article builds the case for discovery science in catalysis and continuous improvement in chemical reaction engineering with this new ecosystem.
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Physics Education

Perfect score on IPhO 2025 theory by Gemini agent

Yichen Huang

The International Physics Olympiad (IPhO) is the world's most prestigious and renowned physics competition for pre-university students. IPhO problems require complex reasoning based on deep understanding of physical principles in a standard general physics curriculum. On IPhO 2025 theory problems, while gold medal performance by AI models was reported previously, it falls behind the best human contestant. Here we build a simple agent with Gemini 3.1 Pro Preview. We run it five times and it achieved a perfect score every time. However, data contamination could occur because Gemini 3.1 Pro Preview was released after the competition.
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Geophysics

Prediction of Extreme Events in Multiscale Simulations of Geophysical Turbulence using Reinforcement Learning

Yifei Guan, Lucas Amoudruz, Sergey Litvinov et al. (+4 more)

Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing excessive diffusion that dampens extremes. Artificial intelligence has gained attention for closure modeling, but the prediction of extreme events remains challenging. Supervised offline learning needs abundant high-fidelity training data and can lead to instabilities. Online learning algorithms are emerging as an alternative, but reliance on differentiable numerical solvers or scalable optimizers hinders broad use. Here, we introduce SMARL to develop closures for canonical prototypes of atmospheric/oceanic turbulence, using only the enstrophy spectrum, estimated from a few high-fidelity samples, as reward. This reward ensures that the model captures the cascades of scales in these simulations. These online-learned closures enable stable simulations, with up to five orders of magnitude fewer degrees of freedom, that reproduce high-fidelity simulation statistics and capture in particular extremes. We interpret the closures by analyzing the SMARL policy and demonstrate generalization to other flows. The results highlight SMARL as a potent tool for developing closures capable of capturing extremes in atmospheric/oceanic flows, opening new capabilities for effective climate modeling.
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All authors Yifei Guan, Lucas Amoudruz, Sergey Litvinov, Karan Jakhar, Rambod Mojgani, Petros Koumoutsakos, Pedram Hassanzadeh

Physics Education

Physics Education under the Application of Artificial Intelligence: Bibliometric Analysis Based on Web of Science Core Library (2021-2025)

Chengtian Liang, Yike Qian, Yixuan Lin et al. (+1 more)

The rapid development of artificial intelligence technology is driving the transformation of physics education from traditional models to intelligent and data-driven approaches. To explore the evolution and cutting-edge hotspots in this field, this study conducted a systematic bibliometric analysis of 138 core literature published between 2021 and 2025 using VOSViewer and CiteSpace, based on the Web of Science Core Collection database. Research shows that the number of related publications will increase exponentially from 2023, with the United States, China, and Germany being the main research forces. The research focus has rapidly evolved from early machine learning assisted data analysis to the application of generative AI in teaching, the integration of physics information neural networks in computational physics courses, and the exploration of intelligent medical physics education. At present, this field is in the early stages of explosive growth and exhibits significant interdisciplinary characteristics. Future research should focus on building an adaptive learning ecosystem, reconstructing evaluation systems, and cultivating students' AI ethics and physical intuition.
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All authors Chengtian Liang, Yike Qian, Yixuan Lin, Yu Wang

Computational Physics · Cross-listed from cs.MS

Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks

Axel Larsson, Minji Kim, Chris Vales et al. (+4 more)

Hyper-reduction methods have gained increasing attention for their potential to accelerate reduced order models for nonlinear systems, yet their comparative accuracy and computational efficiency are not well understood. Motivated by this gap, we evaluate a range of hyper-reduction techniques for nonlinear finite element models across benchmark problems of varying complexity, assessing the inevitable tradeoff between accuracy and speedup. More specifically, we consider interpolation methods based on the gappy proper orthogonal decomposition as well as the empirical quadrature procedure (EQP), and apply them to the hyper-reduction of problems in nonlinear diffusion, nonlinear elasticity and Lagrangian hydrodynamics. Our numerical results are generated using the open source libROM, Laghos and MFEM numerical libraries. Our findings reveal that the comparative performance between hyper-reduction methods depends on both the problem and the choice of time integration method. The EQP method generally achieves lower relative errors than interpolation methods and is more efficient in terms of quadrature point usage, resulting in a lower wall time for the nonlinear diffusion and elasticity problems. However, its online computational cost is observed to be relatively high for Lagrangian hydrodynamics problems. Conversely, interpolation methods exhibit greater variability, especially with respect to the use of different time integration methods in the Lagrangian hydrodynamics problems. The presented results underscore the need for problem specific method selection to balance accuracy and efficiency, while also offering useful guidance for future comparisons and refinements of hyper-reduction techniques.
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All authors Axel Larsson, Minji Kim, Chris Vales, Sigrid Adriaenssens, Dylan Matthew Copeland, Youngsoo Choi, Siu Wun Cheung

Fluid Dynamics

Clustering the Flow: A Data-Driven Framework for Pattern Discovery in Fluid Dynamics

Juan Angel Martin, Eva Muñoz, Himanshu Dave et al. (+2 more)

Clustering techniques offer a powerful framework for analyzing complex flow dynamics and reducing computational costs in large-scale simulations. In this work, we propose a novel clustering-based approach using Vector Quantization Principal Component Analysis (VQPCA) to identify structural sensitivity zones, namely the regions where the fluid flow is more receptive to changes. To the authors knowledge, this is the first application of VQPCA to a fluid dynamics problem for the identification of flow patterns and dynamically relevant regions. As a fully data-driven technique, it does not rely on adjoint methods; therefore, this approach has the advantage of having low computational cost, since it depends exclusively on data from the direct problem. The VQPCA technique demonstrates its ability to extract dominant flow features by clustering the flow field into regions characterized by their intrinsic dynamics. To assess the validity of this method, it is used to investigate the wake behind a circular cylinder, revealing similarities to previously established structural sensitivity regions. The robustness of the approach is further assessed through validation and calibration in different operating conditions in this flow scenario. As an extension of the analysis, we address the complex dynamics of two planar synthetic jets, where the clustering insights can lead to develop flow control strategies. These results highlight the potential of clustering-based methods as practical and effective tools to analyze and optimize fluid flows.
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All authors Juan Angel Martin, Eva Muñoz, Himanshu Dave, Alessandro Parente, Soledad Le Clainche

Atmospheric and Oceanic Physics

Monte Carlo Simulations of Secondary Cosmic-Ray Variations in Atmospheric Electric Fields : Implications for Long Duration Electron and Gamma-ray Emissions from Thunderclouds

Harufumi Tsuchiya

Monte Carlo simulations were conducted using the Particle and Heavy Ion Transport code System (PHITS) to investigate the role of secondary cosmic rays in the generation of long-duration bursts from thunderclouds and to clarify the conditions of the electric field region responsible for particle acceleration. The simulations utilized realistic secondary cosmic-ray spectra, including gamma rays, electrons, positrons, and muons, as input. The simulation results indicate that gamma rays provide the dominant supply of seed electrons for long-duration bursts, regardless of the geometry or strength of the electric field region. They also reveal the structure and strength of the electric field region required to produce gamma rays exceeding several tens of MeV, which have so far been detected only by high-altitude observations. Furthermore, the fluxes of long-duration bursts estimated from the simulation results were compared with observational data to constrain the properties of the electric field region. In particular, the comparison with measurements at Yangbajing, located at an altitude of 4.3~km, helps narrow down the possible range of electric field strengths and configurations.
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Medical Physics

Automated Dose-Based Anatomic Region Classification of Radiotherapy Treatment for Big Data Applications

Justin Hink, Yasin Abdulkadir, Jack Neylon et al. (+1 more)

Curation is a significant barrier to using 'big data' radiotherapy planning databases of 100,000+ patients. Anatomic site stratification is essential for downstream analyses, but current methods rely on inconsistent plan labels or target nomenclature, which is unreliable for multi-institutional data. We developed software to automate labeling by inferring anatomic regions directly from dose-volume overlap with deep-learning segmentations, eliminating metadata reliance. The software processes DICOM files in bulk, utilizing deep learning to segment 118 structures (organs, glands, and bones) categorized into six regions: Cranial, Head and Neck, Pelvis, Abdomen, Thorax, Extremity. The 85% and 50% isodose lines are converted to structures to compute organ-specific dose-overlap metrics. Plans are assigned ranked regional labels based on these intersections. The algorithm was refined using 109 expert-labeled cases and validated on 100 consecutive clinical plans. On the 100-plan test dataset, the algorithm achieved 91% Exact Accuracy (matching all expert labels and order), 94% Top-2 Accuracy (matching the top two expert regions regardless of order), and 95% Top-1 Accuracy (matching the primary expert label). The automated workflow demonstrated high accuracy and robustness. The 95% Top-1 Accuracy is particularly significant, as it enables reliable querying of plans based on the primary treatment site. Detailed analysis of the few mismatched cases showed most were treated areas at the border between anatomic regions and were ambiguous between these two regions in a common-sense interpretation. This algorithm provides a scalable, standardized solution for curating the large, multi-institutional datasets required for 'big data' in radiotherapy and provides an important complement to text-based approaches.
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All authors Justin Hink, Yasin Abdulkadir, Jack Neylon, James Lamb

Fluid Dynamics

Multiple states of turbulence at vanishing inertia

Ziyin Lu and Björn Hof

Based on everyday experience fluid flows tend to be ordered and quiescent if inertial forces are low and held in check by viscosity. This intuition spectacularly fails in the case of complex macromolecular fluids like polymer melts, paints and biofluids. In such cases elastic fluid properties can drive turbulent motions at moderate and even vanishing Reynolds numbers. By studying viscoelastic flows in curved pipes we demonstrate that this low inertia phenomenology results from the competition of two hydrodynamic instabilities and respectively from the co-existence and interdependence of two distinct turbulent states. Unexpectedly the established categories of elastic and elasto-inertial turbulence (ET and EIT) fail to demarcate the actual turbulent states, fundamentally changing the perception of this phenomenon a century after its discovery.
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Medical Physics

Automated Extraction of Unstructured Post-SBRT Toxicity Data from Radiology Reports Using Large Language Models

Justin Pijanowski, Yakout Mezgueldi, Alan Lee et al. (+3 more)

We evaluated the viability of using a Large Language Model (LLM) to extract patient-specific specific toxicity and progression outcomes from unstructured radiology reports. We retrospectively extracted 160 follow-up CT and PET/CT electronic medical record notes for patients treated with lung stereotactic body radiotherapy (SBRT) at our institution from January 2017 through December 2023. Using the Llama 3.3-70-B-Instruct LLM, we engineered prompts to extract four clinical endpoints from each radiology report: locoregional progression, distant progression, radiation-induced fibrosis, and radiation-induced rib fractures. Progression endpoints were classified as yes, no, or maybe, while fibrosis and rib fractures were binary (yes or no). Ground truth labels were defined using two-grader consensus for the 60-note training set, used for prompt development, and a three-grader majority vote for the 100-note test set. LLM performance was evaluated using sensitivity, specificity, and accuracy. As detailed by our evaluation metrics, the strong performance of our methods demonstrates the viability of using prompt-engineered LLMs to extract radiation-toxicities and progression classification from radiology reports.
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All authors Justin Pijanowski, Yakout Mezgueldi, Alan Lee, Drew Moghanaki, Ricky R. Savjani, James Lamb

History and Philosophy of Physics · Cross-listed from quant-ph

Trajectory of Probabilities, Probability on Trajectories, and the Stochastic-Quantum Correspondence

Győző Egri, Marton Gomori, Balazs Gyenis et al. (+1 more)

The probabilistic description of the time evolution of a physical system can take two conceptually distinct forms: a trajectory of probabilities, which specifies how probabilities evolve over time, and a probability on trajectories, which assigns probabilities to possible histories. A lack of a clear distinction between these two probabilistic descriptions has given rise to a number of conceptual difficulties, particularly in recent analyses of stochastic-quantum correspondence. This paper provides a systematic account of their relationship. We define probability dynamics and stochastic process families together with a precise notion of implementation that connects the two descriptions. We show that implementations are generically non-unique, that every probability dynamics admits a Markovian implementation, and characterize when non-Markovian implementations are possible. We expose fallacies in common arguments for the linearity of probability dynamics based on the law of total probability and clarify the proper interpretation of “transition matrices” by distinguishing dynamics-level maps from the conditional probability matrices of implementing processes. We further introduce decomposability as the appropriate general notion of stepwise evolution for (possibly nonlinear) probability dynamics, relate it to divisibility in the linear case – showing that the two can come apart – and disentangle both notions from Markovianity and time-homogeneity. Finally, we connect these results to what we call statistical dynamics, in which linearity is indeed physically motivated, and contrast the framework with quantum mechanics.
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All authors Győző Egri, Marton Gomori, Balazs Gyenis, Gábor Hofer-Szabó

Biological Physics

Signals too small to sense: Physical and information-theoretic limits to induction-based magnetoreception in birds

Daniel R. Kattnig

A recent study [Science 2025, eaea6425] proposes that magnetoreception in pigeons may arise from electromagnetic induction within the semicircular canals of the inner ear. In this framework, motion through the geomagnetic field is suggested to generate an induced electromotive force that leads to ion redistribution in the endolymph, activation of voltage-gated calcium channels, and subsequent engagement of downstream neural circuits. In this work, we examine the physical plausibility of this mechanism using a toy model of the induction process combined with an information-theoretic analysis. We find that, under idealised assumptions, Faraday induction in the semicircular canals would not generate a signal of sufficient informational content to support the extraction of directional magnetic field information from the geomagnetic field. However, the model supports the possibility of inferences due to radio-frequency (RF) electromagnetic waves of a miniscule amplitude, thereby providing a potential rationalisation of their disruptive effect on avian compass navigation. We stress that our analysis does not call into question the experimental evidence for magnetically responsive pathways within the vestibular system of pigeons. Rather, it constrains the class of viable physical mechanisms, indicating that a functionally competent magnetosensory system likely relies on different sensing principles or, if induction-based, on a different sensing architecture, while highlighting induction as a potential interference pathway of RF electromagnetic fields.
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Optics

Ultrahigh continuous-wave intensities in high-NA optical cavities through suppression of the parametric oscillatory instability

L. Maisenbacher, A. Singh, I. M. Pope et al. (+1 more)

Ultrahigh continuous-wave intensities (>300 GW/cm$^2$) in high-NA optical cavities enable applications from phase-contrast electron microscopy to ultradeep dipole traps for molecules. However, the intensity can be limited by the parametric oscillatory instability (PI), where mirror vibrations scatter light from one cavity mode into another. We observe PI in a table-top Fabry-Pérot cavity, show that the mechanical modes are MHz-frequency bulk acoustic modes inside the mirrors, and measure their $Q$ factor. By using low-$Q$ mirrors, we achieve >500 GW/cm$^2$ intensities in an open, free-space cavity.
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All authors L. Maisenbacher, A. Singh, I. M. Pope, H. Müller

Medical Physics

Synthetic Data in MR Spectroscopy: Current Practices, Applications, and Considerations

John T. LaMaster, Aaron T. Gudmundson, Alireza Abaei et al. (+75 more)

The use of synthetic data has emerged as an essential tool in Magnetic Resonance Spectroscopy (MRS) research and applications, providing advantages for optimization of acquisition, software validation, deep learning applications, and enhanced reproducibility. Importantly, synthetic data addresses challenges of limited training data availability, particularly for clinical populations, and offers controlled solutions for investigating uncertainties and unexplained variance with in vivo data. This work provides a review and evaluation of current practices in the use and generation of synthetic data within the MRS field. Conducted by the MRS Synthetic Data Working Group under the Code & Data Sharing Committee of the MRS Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM), this manuscript encompasses existing literature, supplemented by collective experience and in-house methodologies.
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All authors John T. LaMaster, Aaron T. Gudmundson, Alireza Abaei, Seyma Alcicek, Arturo Alvarado, Ovidiu Andronesi, Tiffany K. Bell, Wolfgang Bogner, Hanna Bugler, Alexander R Craven, Cristina Cudalbu, Alma Davidson, Christopher W. Davies-Jenkins, Dinesh Deelchand, Richard A. E. Edden, Morteza Esmaeili, Candace C Fleischer, Abdelrahman Gad, Guglielmo Genovese, Saumya Gurbani, Ashley D. Harris, Pierre-Gilles Henry, Kay Chioma Igwe, Ajin Joy, Margarida Julià-Sapé, Hyeonjin Kim, Roland Kreis, Fan Lam, Karl Landheer, Bernard Lanz, Chu-Yu Lee, Clémence Ligneul, Julian P. Merkofer, Jack J. Miller, Jessie Mosso, Stanislav Motyka, Eloïse Mougel, Paul G. Mullins, Saipavitra Murali-Manohar, Chloé Najac, Shinichiro Nakajima, Georg Oeltzschner, Esin Ozturk-Isik, Marco Palombo, Ulrich Pilatus, Justyna Platek, Emma Van Praagh, Xiaobo Qu, Rudy Rizzo, Christopher T. Rodgers, Esau Poblador Rodriguez, Yeison Rodriguez, Manoj K Sammi, Dennis M. J. van de Sande, Manoj Kumar Sarma, Francesca Saviola, Anouk Schrantee, Amirmohammad Shamaei, Dunja Simicic, Brian J Soher, Nico Sollmann, Yulu Song, Jeffrey A Stanley, Bernhard Strasser, Antonia Susnjar, Kelley M. Swanberg, M. Albert Thomas, Ivan Tkáč, Zhangren Tu, Paul J. Weiser, Mark Widmaier, Martin Wilson, Christopher J. Wu, Lijing Xin, Helge J. Zöllner, İpek Özdemir, MRS Synthetic Data Working Group, Antonia Kaiser

Fluid Dynamics

Neural ensemble Kalman filter: Data assimilation for compressible flows with shocks

Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman et al. (+4 more)

Data assimilation (DA) for compressible flows with shocks is challenging because many classical DA methods generate spurious oscillations and nonphysical features near uncertain shocks. We focus here on the ensemble Kalman filter (EnKF). We show that the poor performance of the EnKF may be attributed to the bimodal forecast distribution that can arise in the vicinity of an uncertain shock location; this violates the assumptions underpinning the EnKF, which assume a forecast which is close to Gaussian. To address this issue we introduce the new neural EnKF. The basic idea is to systematically embed neural function approximations within ensemble DA by mapping the forecast ensemble of shocked flows to the parameter space (weights and biases) of a deep neural network (NN) and to subsequently perform DA in that space. The nonlinear mapping encodes sharp and smooth flow features in an ensemble of NN parameters. Neural EnKF updates are therefore well-behaved only if the NN parameters vary smoothly within the neural representation of the forecast ensemble. We show that such a smooth variation of network parameters can be enforced via physics-informed transfer learning, and demonstrate that in so-doing the neural EnKF avoids the spurious oscillations and nonphysical features that plague the EnKF. The applicability of the neural EnKF is demonstrated through a series of systematic numerical experiments with the inviscid Burgers' equation, the Sod shock tube, and a two-dimensional blast wave.
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All authors Xu-Hui Zhou, Lorenzo Beronilla, Michael K. Sleeman, Hangchuan Hu, Matthias Morzfeld, Andrew M. Stuart, Tamer A. Zaki

Physics and Society · Cross-listed from cs.SI

Learning dynamics from online-offline systems of LLM agents

Moyi Tian, George Mohler, P. Jeffrey Brantingham et al. (+1 more)

Online information is increasingly linked to real-world instability, especially as automated accounts and LLM-based agents help spread and amplify news. In this work, we study how information spreads on networks of Large Language Models (LLMs) using mathematical models. We investigate how different types of offline events, along with the "personalities" assigned to the LLMs, affect the network dynamics of online information spread of the events among the LLMs. We introduce two models: 1) a stochastic agent-based network model and 2) a system of differential equations arising from a mean-field approximation to the agent-based model. We fit these models to simulations of the spread of armed-conflict news on social media, using LLM agents each with one of 32 personality trait profiles on k-regular random networks. Our results indicate that, despite the complexity of the news events, personalities, and LLM behaviors, the overall dynamics of the system are well described by a Susceptible-Infected (SI) type model with two transmission rates.
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All authors Moyi Tian, George Mohler, P. Jeffrey Brantingham, Nancy Rodríguez

Optics · Cross-listed from gr-qc

Polarisation Singularities of Gravitational Waves

Claire Rigouzzo, Sebastian Golat, Alex J. Vernon et al. (+3 more)

Departure from idealised plane waves gives rise to intricate geometric structures in wave fields. One such structure is the polarisation singularity, which emerges when multiple monochromatic waves interfere (such as would be the case for stochastic backgrounds), producing loci of purely circular or linear polarisation. In this work, we extend the theory of polarisation singularities to gravitational waves and higher spin fields. Building on the electromagnetic description, we formulate the gravitational analogue of polarisation singularities and show that they are generic features of gravitational waves. Their dimension, however, depends on the spin of the field. We illustrate these results with simulations of plane-wave interference and analyse the resulting singularity densities.
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All authors Claire Rigouzzo, Sebastian Golat, Alex J. Vernon, Kyan Louisia, Eugene Lim, Francisco J. Rodriguez-Fortuno

Atomic Physics · Cross-listed from hep-ph

Black hole scalar sirens in the Milky Way

Daniel Gavilan-Martin, Olivier Simon, Dhashin Krishna et al. (+3 more)

Hypothetical light scalar particles trigger the superradiant instability around spinning black holes (BHs), causing clouds of scalars to grow around the BH. In the presence of sufficiently strong particle self-interactions (characterized by the decay constant $f$), scalars are ejected from BH orbits, resulting in coherent, non-relativistic emissions that continuously carry away the BH's angular momentum. Parameters exist for which cloud growth is much faster, and scalar depletion is much slower, than the age of the Galaxy. This defines a distinct class of astrophysical sources of scalars, which we call BH scalar sirens – BHs that persistently emit scalars effectively forever. We compute the scalar background from the expected population of $N_\text{BH}\sim 10^{8}$ isolated stellar-mass BHs in the Milky Way, which are sirens for scalars in the mass range $10^{-13}$–$10^{-11}\,$eV and $f\lesssim 10^{14}$–$10^{9}\,$GeV. This provides a detection target independent of early-universe scalar production or cosmological initial conditions. The generated observable signals are up to two orders-of-magnitude larger than those expected from a misaligned cosmic scalar in this mass range. The energy spectrum of emitted scalars is distinctly broader and at higher velocities (up to $\sim 10^{-1}c$) than that of virialized dark matter, and encodes the mass and spin distributions of the BH population. While stellar-mass Milky Way BHs are our primary target, our framework extends to supermassive, intermediate-mass and light BHs. Given the difficulty of directly observing populations of isolated BHs, scalar emissions offer a novel probe of these otherwise invisible objects, highlighting the potential for joint discovery between scalars and BHs, and broadly motivating searches for scalars over many orders-of-magnitude in mass.
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All authors Daniel Gavilan-Martin, Olivier Simon, Dhashin Krishna, Derek F. Jackson Kimball, Dmitry Budker, Arne Wickenbrock

Optics

Temporal metamaterials with passive switching as impedance-matched absorbers

Suat Bar{\i}ş İplikçioğlu

Recent experiments on temporal reflection in transmission line metamaterials and theoretical treatments of dispersive time-varying media have established the fundamental role of modulation mechanisms on the interface conditions, underpinning the introduction of passive photonic time crystals with stable momentum band gaps. Drawing from these concepts, it is shown that temporal metamaterials with simultaneous passive permittivity and permeability switching exhibit wideband absorption with impedance-matching, effectively behaving as one-dimensional perfectly matched layers. Under the effective medium theory, the loss mechanism is attributed to the emergent effective electric and magnetic conductivities, which are used to derive an approximate matching condition for asynchronous modulation and to engineer lossy material properties. The proposed approach and its performance beyond the Rozanov bound are verified with semi-analytical calculations as well as full-wave simulations, and the limits on realizing a two-dimensional temporal perfectly matched layer are theoretically evaluated.
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Instrumentation and Detectors · Cross-listed from eess.SP

CubeSounder: Low SWaP-C 180 GHz Radiometer for Atmospheric Sensing Tested on High Altitude Balloons

Kyle D. Massingill, Tyler M. Karasinski, Sean Bryan et al. (+13 more)

Microwave sounding is the leading driver of global numerical weather forecasting, but is limited by the scalability of such instruments. With modern machining and commercial microwave components, it is now possible to design low size, weight, power, and cost (SWaP-C) microwave spectrometers while maintaining wide bandwidth performance. Here we report on the status of CubeSounder, a spectrometer tailored for water vapor radiometry that utilizes passive wave guide filter banks. After developing a prototype and high altitude balloon payload, we demonstrated CubeSounder on commercial stratospheric balloon flights. We report on our design process, especially the simulation and fabrication of the custom millimeter-wave filter banks. We also report the initial results of the data collected from the balloon flights.
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All authors Kyle D. Massingill, Tyler M. Karasinski, Sean Bryan, Michael Baricuatro, Daniel Bliss, Delondrae Carter, Walter Goodwin, Jonathan Greenfield, Christopher Groppi, Philip Mauskopf, Philip Rybak, Scott Smas, Roshni Suresh, Sage Tinlin, Bianca Wullen, Peter Wullen

Computational Physics

mrfmsim: A modular, extendable, and readable simulation package for magnetic resonance force microscopy experiments

Peter Sun, Corinne E. Isaac, Michael C. Boucher et al. (+3 more)

We present mrfmsim, an open-source Python package that facilitates the design, simulation, and analysis of magnetic resonance force microscopy (MRFM) experiments. MRFM is a scanning-probe technique that detects magnetic resonance from nanoscale ensembles of nuclear or electron spins with a force sensor. Because MRFM experiments are complex and operate at sensitivity limits, numerical simulation is essential for designing experiments and estimating per-spin sensitivity and imaging resolution from measured signals. In this paper, we highlight the challenges of developing MRFM simulations and show that software designed to simulate specific experiments only in a rapidly evolving experimental field can yield erroneous results. The mrfmsim package addresses these challenges by supporting post-definition customization without rewriting the internal model and by employing a plugin system for extending functionality. We show that the package's modular, extendable, and readable architecture improves reproducibility and accelerates development.
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All authors Peter Sun, Corinne E. Isaac, Michael C. Boucher, Eric W. Moore, Zhen Wang, John A. Marohn

Optics

Coherent Virtual Absorption in Dielectric Metasurfaces

Kaizad Rustomji, Nasim Mohammadi Estakhri and Nooshin M. Estakhri

Through temporal shaping of the excitation signal, the complex-frequency scattering zeros of a lossless structure can be accessed, enabling a storage-release mechanism referred to as coherent virtual absorption. Practical demonstrations of this mechanism, however, have been limited to simple configurations such as slabs and spheres, where analytical solutions allow accurate prediction of the complex-frequency scattering zeros. Here, we extend this concept into the realm of metasurfaces and demonstrate coherent virtual absorption in realistic and dispersive metasurface configurations. Through a combination of full-wave analysis and rational approximation, we present a practical scheme to identify suitable complex-frequency zeros and achieve coherent virtual absorption successfully. Our approach can be implemented in arbitrary metasurface configurations with any number of ports, providing a robust framework for optimized energy storage, memories, optical sensing, and modulation in practical photonic systems.
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Biological Physics

Discrete turn strategies emerge in information-limited navigation

Jose M. Betancourt, Matthew P. Leighton, Thierry Emonet et al. (+2 more)

Navigation up a smooth sensory gradient is one of the simplest behavioural tasks, and some organisms solve it by making continuous adjustments to their course. Bacteria instead employ a variety of discrete strategies, including run and tumble motion, direction reversals, and turns by specific angles. Here we ask what drives the choice of these strategies, framing the problem as maximising up-gradient speed with a given amount of sensory information per unit time. We find that, without directional information on which way to turn, behavioural strategies that take discrete actions perform better than gradual steering. As the amount of information is increased, we see a series of transitions between optimal strategies, including a shift from direction reversals to fully re-orienting tumbles. Among more complex re-orientation strategies, we show that discrete turn angles are best, and observe transitions in the number of angles employed by the optimal strategy. More broadly, such emergent simplicity in behaviour is a tractable example of a widespread phenomenon in which biology chooses a discrete solution, despite the underlying physics being continuous.
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All authors Jose M. Betancourt, Matthew P. Leighton, Thierry Emonet, Benjamin B. Machta, Michael C. Abbott

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

Electromechanical Switching and Momentum-Selective Transport in Geometry-Defined Blue Phosphorus Homojunctions

Zewen Wu, Min Zhou, Yanxia Xing et al. (+1 more)

Developing intrinsic homojunctions without chemical heterogeneity remains a key challenge in future two - dimensional devices. Here, we report a geometry - defined metal–semiconductor–metal homojunction in bilayer blue phosphorus (BlueP) created by a localized bubble corrugation, without chemical doping or foreign - material interfaces. First - principles calculations show that enlarging the interlayer separation in the metallic A\(_1\)B\(_1\) - stacked BlueP bilayer opens a band gap, enabling a semiconducting barrier embedded between metallic segments. First - principles quantum - transport simulations reveal a crossover from ballistic to tunneling transport upon bubble formation. In the tunneling regime, transmission decreases exponentially with bubble width while remaining weakly sensitive to bubble height and bulging direction. The junction acts as an orientation - dependent \(k\) - space filter, producing transport anisotropy and momentum selectivity. Orbital - resolved scattering analysis shows that intralayer - bonding channels persist under deformation whereas interlayer - hybridized channels are quenched, and that \sigma - type bonding yields higher conductance than \pi - type bonding. These insights motivate two electromechanical device concepts: a mechanically switchable memory element with ON/OFF ratios up to 30 and a nanoscale sliding rheostat with reproducible exponential resistance tuning for Å - scale displacement sensing.
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All authors Zewen Wu, Min Zhou, Yanxia Xing, Xianghua Kong

Plasma Physics · Cross-listed from astro-ph.CO

Imprints of primordial magnetic fields in gravitational collapse during early structure formation

Jennifer Schober, Molly Abramson, Sayan Mandal et al. (+2 more)

Context. Primordial magnetic fields (PMFs) generated in the early Universe might have left observable imprints on present-day large-scale structure. However, the spatial scales on which primordial signatures are able to survive the nonlinear processes that accompany structure formation remain unclear. Aims. The aim of this study is to investigate the evolution of the statistical properties of PMFs during the onset of gravitational collapse. Methods. We performed a suite of high-resolution direct numerical simulations of isothermal self-gravitating, magnetized gas clouds. By varying the viscosity, we probed different Reynolds-number regimes and follow the coupled evolution of gravitational collapse and magnetohydrodynamic turbulence. Results. At sufficiently high Reynolds numbers, turbulence generated during collapse triggers the onset of a small-scale dynamo, which amplifies magnetic energy below the Jeans scale and modifies the magnetic energy spectrum significantly. The question of whether dynamo amplification dominates the magnetic field evolution is determined by the competition between the dynamo growth time and the free-fall time. Conclusions. Our results highlight the importance of resolving the Jeans scale and the associated turbulent inertial range in cosmological magnetohydrodynamic (MHD) simulations to accurately capture the interplay between gravitational compression and dynamo amplification and to assess which structures retain memory of primordial fields.
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All authors Jennifer Schober, Molly Abramson, Sayan Mandal, Salome Mtchedlidze, Tina Kahniashvili

Optics

Enhanced Interband Optical Nonlinearities from Coupled Quantum Wells

Rithvik Ramesh, Madeline Brown, Amberly Ricks et al. (+21 more)

The recent, rapid advances in nonlinear chipscale nanophotonics in the visible and near-infrared have been largely driven by manipulating the local dielectric environment proximate to decades-old workhorse bulk nonlinear optical materials, rather than increasing the inherent strength of their nonlinear response. While proposed decades ago, we demonstrate the first experimental realization of a new class of designer nonlinear materials that leverage the interband optical transition in asymmetric structures to provide strong second order susceptibility, $\chi^{(2)}$. Using simple AlGaAs/GaAs coupled quantum wells operating in the near-infrared as a prototype, we observed strong second harmonic generation enhancement of 1550 nm to 775 nm over bulk controls. Extracted $\chi^{(2)}$ values were as high as 2750 pm/V, which is $>$7x that of bulk GaAs. Furthermore, measured susceptibilities agreed well with quantum mechanical calculations of $\chi^{(2)}$ using layer profiles extracted from electron microscopy. Growth interruptions were employed to improve interfacial abruptness in response to electron microscopy characterization, resulting in increased $\chi^{(2)}$ toward the simulation predictions for ideal heterointerfaces. More complex layer designs showed predicted $\chi^{(2)}$ up to 7 nm/V. Such materials are anticipated to find myriad applications, including entangled photon generation at telecommunications wavelengths for chipscale quantum information processing.
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All authors Rithvik Ramesh, Madeline Brown, Amberly Ricks, Sedigheh Esfahani, Patrick Devaney, Kevin Wen, Moaz Waqar, Zarko Sakotic, Sander A. Mann, Teddy Hsieh, Alec M. Skipper, Qian Meng, Hyunseung Jung, Michele Cotrufo, Farbod Shafiei, Michael C. Downer, Sanjay Shakkottai, Mark Wistey, Igal Brener, Xiaoqing Pan, Andrea Alù, Daniel Wasserman, Jacob B. Khurgin, Seth R. Bank