General Physics
Topological Structure of Infrared QCD
J. Gamboa
We investigate the infrared structure of QCD within the adiabatic approximation, where soft
gluon configurations evolve slowly compared to the fermionic modes. In this formulation, the functional space of gauge connections replaces spacetime as the natural arena for the theory, and the long-distance behavior is encoded in quantized Berry phases associated with the infrared clouds. Our results suggest that the infrared sector of QCD exhibits features reminiscent of a topological phase, similar to those encountered in condensed-matter systems, where topological protection replaces dynamical confinement at low energies. In this
geometric framework, color-neutral composites such as
quark–
gluon and
gluon–
gluon clouds arise as topological bound states described by functional holonomies. Illustrative applications to hadronic excitations are discussed within this approach, including mesonic and baryonic examples. This perspective provides a unified picture of infrared dressing and topological quantization, establishing a natural bridge between non-Abelian gauge theory, adiabatic Berry phases, and the
topology of the space of gauge configurations.
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General Physics
Seokcheon Lee
Recent analyses of the Master Type Ia
supernova (SN Ia) sample have revealed a mild
redshift dependence in the inferred local Hubble parameter, often expressed as tilde{H}_0(z) = H_0 (1+z)^{-\alpha}, where \alpha quantifies possible departures from the standard cosmological time dilation relation. In this work, we show that such an empirical scaling can be interpreted as a purely
geometric effect arising from a small, gauge-dependent normalization of
cosmic time within the Robertson-Walker metric. This interpretation naturally unifies the observed
redshift evolution of tilde{H}_0(z) and the corresponding deviation in SN Ia light-curve durations under a single
geometric time-normalization framework. We demonstrate that this mapping leaves all background distances–linked to the Hubble radius in the general-relativistic frame–unchanged, while the apparent
evolution in SN Ia luminosity distances arises from the
redshift dependence of the Chandrasekhar mass. The result provides a unified and observationally consistent explanation of the mild Hubble-tension trend as a manifestation of the
geometric structure of
cosmic time rather than a modification of the expansion dynamics.
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Instrumentation and Detectors · Cross-listed from cs.LG
wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation
Benjamin Hawks, Jason Weitz, Dmitri Demler et al. (+13 more)
As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a
benchmark for ML accelerator resource and latency estimation, and its corresponding initial
dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The
benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the
dataset. Additionally, we introduce GNN- and
transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test
dataset.
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All authors Benjamin Hawks, Jason Weitz, Dmitri Demler, Karla Tame-Narvaez, Dennis Plotnikov, Mohammad Mehdi Rahimifar, Hamza Ezzaoui Rahali, Audrey C. Therrien, Donovan Sproule, Elham E Khoda, Keegan A. Smith, Russell Marroquin, Giuseppe Di Guglielmo, Nhan Tran, Javier Duarte, Vladimir Loncar
Computational Physics · Cross-listed from cs.LG
An MLCommons Scientific Benchmarks Ontology
Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair et al. (+6 more)
Scientific machine learning research spans diverse domains and data modalities, yet existing
benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology,
climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science
Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate
benchmarks and frameworks into a single taxonomy of scientific, application, and system-level
benchmarks. New
benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality
benchmarks, enabling stakeholders to select
benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science
Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/
benchmark/
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All authors Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair, Marco Colombo, Shivaram Venkataraman, Rutwik Jain, Yiwei Jiang, Nhan Tran, Geoffrey Fox
Chemical Physics
Gas Mixture Diffusion and Distribution in the Porous ZIF-90 Framework
Ashok Yacham, Tarak K. Patra and Jithin John Varghese
Understanding how gas mixtures diffuse and distribute within porous frameworks is central to designing advanced separation and storage materials. Here, we investigate the transport and spatial distribution of binary gas mixtures in a porous metal organic framework, viz., ZIF-90, using molecular simulations. We perform grand canonical Monte Carlo (GCMC) simulations to examine the competitive adsorption of carbon dioxide and nitrogen from a binary gas mixture in ZIF-90, while molecular dynamics (MD) simulations are conducted to investigate the transport behavior of the adsorbed molecules within the framework. These integrated simulations reveal that the framework
topology and pore chemistry jointly dictate
diffusion pathways and preferential occupancy of gas species, underscoring their intrinsic interdependence. Competitive adsorption leads to distinct spatial partitioning within the pores, which in turn modulates mixture diffusivity inside the porous medium compared to their bulk properties. Our results provide molecular-level insight into how ZIF-90 accommodates and separates gas mixtures, offering design principles for optimizing metal-organic frameworks in energy and environmental applications.
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Fluid Dynamics
Rate dependency of capillary heterogeneity trapping for CO2 storage
Catrin Harris, Samuel Krevor, Ann H. Muggeridge et al. (+1 more)
In this paper, we experimentally quantify and analytically model rate dependent capillary heterogeneity trapping. Capillary heterogeneity trapping enhances non-wetting fluid trapping beyond pore-scale residual trapping through the isolation of non-wetting phase upstream of heterogeneities in the continuum capillary pressure characteristics. Whilst residual trapping is largely insensitive to the range of flow regimes prevalent in engineered reservoir settings, continuum theory anticipates that capillary heterogeneity trapping will be more sensitive to the balance of viscous and capillary forces that occur. We perform steady-state drainage and imbibition multiphase flow experiments at varying flow rate on a layered Bentheimer sample with in-situ medical X-ray CT scanning to quantify saturation. Saturation discontinuities are observed upstream of capillary pressure barriers as a result of capillary pressure discontinuities, trapping the non-wetting phase at a saturation greater than pore-scale residual trapping alone. We confirm the flow rate dependence predicted by theory whereby the relationship between the initial and residual saturations approach a 1:1 dependence as flow rate is decreased. We develop a one-dimensional analytical model to quantify the proportion of capillary heterogeneity trapping in the system and the dimensionless trapping length scale, which agrees with the experimental data and allows for rapid estimates of trapping up to the field-scale.
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All authors Catrin Harris, Samuel Krevor, Ann H. Muggeridge, Samuel J. Jackson
Fluid Dynamics
Unstable drainage dynamics during multiphase flow across capillary heterogeneities
Catrin Harris, Sam Krevor, Ann H Muggeridge et al. (+2 more)
We use novel, fast 4D Synchrotron X-ray imaging with large field-of-view to reveal pore- and macro-scale drainage dynamics during gas-brine flow through a layered sandstone rock sample. We show that a single centimetre-scale layer, similar in pore size distribution to the surrounding rock but with reduced connectivity, temporarily inhibits and redirects gas flow, acting as a capillary barrier. Subtle variations in gas invasion upstream of the barrier lead to different downstream migration pathways over repeated experiments, resulting in unstable and unpredictable drainage behaviour, with breakthrough times varying by up to a factor of four. The results show that heterogeneity in pore-scale connectivity can amplify variability in macroscopic flow, challenging deterministic assumptions in existing continuum models. By linking structural heterogeneity to flow instability, this work underscores the need for probabilistic modelling approaches in multiphase flow and highlights broader implications for managing fluid transport in natural and engineered porous systems.
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All authors Catrin Harris, Sam Krevor, Ann H Muggeridge, Michael Camilleri, Samuel J. Jackson
Chemical Physics
Non-resonant SABRE provides a new and versatile hyperpolarization approach for magnetic resonance
Loren L. Smith and Warren S. Warren
Hyperpolarization approaches in
magnetic resonance overcome the sensitivity limitations imposed by thermal magnetization and play an important role in a very wide range of modern applications. One of the newer strategies, variants of what is generically called SABRE, uses para-hydrogen to form hydrides on transition metal catalysts, followed by reversible exchange to polarize target molecules in solution, and has produced large signal enhancements (approx. 10^4) on hundreds of different molecules, cheaply and rapidly. Most commonly, the sample is kept in a constant field, matched to make the hydride scalar coupling comparable to the frequency difference between hydride
protons and target
protons (approx. 6.5 mT) or hydride
protons and target heteronuclei (approx. 0.5 {\mu}T). Here we demonstrate a different strategy, applicable to a wide range of target molecules, that produces field-independent
spin order in the target molecules that is efficiently converted to magnetization. The observed signal is even independent of field direction, hence significant polarization can be achieved in a sample on a lab bench with no field control at all. We show this signal arises from creation of two-
spin order in the target molecules, and discuss multiple ways this strategy should expand SABRE generality and efficiency. We also show that, in many cases, the standard assumption in low-field SABRE of a starting state with only singlet polarization leads to incorrect results.
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Fluid Dynamics
Train yourself: self-compressing reduced-order models of turbulent flows
Ian Addison-Smith, Igor A. Maia, Benjamin Herrmann et al. (+1 more)
Reduced-order models (ROMs) of turbulent flows based on Galerkin projection often require many degrees of freedom to resolve the dynamics of the
turbulence, or simulation data to obtain an optimal modal basis. However, obtaining simulation data is computationally expensive, and the amount of data required to obtain a converged modal basis can increase this cost. Using the linearized Navier-Stokes equations, one can achieve spatial modes through the controllability and observability Gramians, which can yield a ROM without prior simulation data. In this work, we propose a self-compression of a ROM based on controllability modes, where the time series of the modal coefficients are leveraged to reduce the dimension of the ROM. In the self-compressed ROM (SCROM), we can maintain accurate first- and second-order statistics with respect to the DNS simulation, but in a further reduced dimension. The SCROM recovers spatial structures equivalent to proper orthogonal decomposition (POD) without relying on any simulation data, recombining spatial modes from linearized equations. This method leads to a novel ROM that can represent
turbulence statistics in a data-free approach in a further reduced state space.
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All authors Ian Addison-Smith, Igor A. Maia, Benjamin Herrmann, Andre V. G. Cavalieri
Instrumentation and Detectors
Impact of the lead factor of neutron irradiation on the magnetic properties of RPV steels
Sebastián Passanante, Dafne Goijman, M. R. Neyra Astudillo et al. (+5 more)
Materials used in
nuclear reactors are constantly exposed to the effects of
neutron irradiation, which leads to changes in their mechanical properties. In particular, the steels employed in reactor pressure vessels experience a reduction in the ductile-to brittle transition temperature. Given that the pressure vessel is a non-redundant component, understanding this phenomenon is of significant interest. In this work, we focus on studying the effects of accelerated irradiation by maintaining constant
neutron fluence while varying
neutron flux, which results in different lead factors. This approach enables the extrapolation of accelerated test results to real operational conditions of the reactor pressure vessels. In our study, we analyzed irradiated steels using three
magnetic techniques. Each technique responds differently to the microstructural changes induced by irradiation, allowing for a better characterization of its effects on the material. Through DC magnetometry, the analysis of minor loops shows that the remanence, coercivity, and saturation of the steel depend not only on the fluence with which the material was irradiated but also on the irradiation rate, which means, more specifically, on the lead factor. The AC susceptibility technique shows that the response of the irradiated steel to the applied
magnetic field increases with fluence but also with the lead factor. For the real part of XAC, there is an increase with the lead factor, while the imaginary component of XAC grows with the size of the nanoprecipitates. Finally, Barkhausen noise measurements show a clear increase in the RMS of the signal with the lead factor.
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All authors Sebastián Passanante, Dafne Goijman, M. R. Neyra Astudillo, Carlos D. Anello, Rodolfo Kempf, Julián Milano, Martín Gómez, Joaquín Sacanell
Computational Physics
Accelerating metamaterial topology optimization using deep super-resolution networks
Ajendra Singh, Shubham Saurabh, Abhinav Gupta et al. (+1 more)
Designing metamaterials for extreme mechanical behavior involves the optimal selection of design parameters. However, identifying these optimal parameters through
topology optimization (TO) across a large parametric space requires extensive computational resources. To address this challenge, we propose a novel deep learning framework for metamaterial
topology optimization using an enhanced deep super-resolution (EDSR) approach. Generating low-resolution
topologies significantly reduces computational cost compared to high-resolution designs. Therefore, an EDSR network is trained to learn the mapping between low- and high-resolution metamaterial
topologies. The training
dataset is generated using solid isotropic material with penalization (SIMP)-based TO. We demonstrate the proposed approach for the design of mechanical metamaterials targeting objectives such as maximization of bulk modulus, shear modulus, and elastic modulus, and minimization of Poisson's ratio. Quantitative assessments -including (i) pixel value error, (ii) objective function error, (iii) intersection over union, and (iv) volume fraction error -validate the accuracy of the EDSR-based TO. Our framework predicts high-resolution
topologies of size $192 \times 192$ from optimized low-resolution
topologies of size $48 \times 48$. Once trained, the proposed network predicts these high-resolution
topologies with only $5-7\%$ of the computational cost required by conventional SIMP-based TO at the same resolution. Moreover, by adding upscale blocks, the framework can generate smoother, higher-resolution
topologies suitable for 3D printing. This approach offers a scalable and efficient solution with strong potential for multidisciplinary metamaterial design applications.
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All authors Ajendra Singh, Shubham Saurabh, Abhinav Gupta, Rajib Chowdhury
Geophysics · Cross-listed from astro-ph.SR
Comparative Analysis of 10 - 50 MeV Solar Proton Events at Lagrange Point 1 and the Geostationary Orbit
Aatiya Ali and Viacheslav Sadykov
Solar proton events (SPEs) pose radiation hazards, disrupt technology, and impact operations on Earth and in space, making continuous monitoring essential. We compare 10-50 MeV
proton flux measurements from SOHO/EPHIN at Lagrange Point 1 (L1) with those from NOAA/GOES in geostationary orbit (GEO) during
Solar Cycle 23 and most of Cycle 24. We identify 83 >=10 pfu SPEs observed at both locations and classify them into S1-S4 categories (comparable to NOAA's
solar radiation storm scales). EPHIN detected earlier onsets and longer durations across all categories, along with earlier peaks and ends for S1-S3, while GOES recorded slightly earlier peak and end times for S4. S1 median timing offsets (EPHIN relative to GOES) were -20 +/- 50 min (onsets), -1.00 +/- 1.42 hr (peaks), and -1.08 +/- 2.21 hr (ends), with similar trends for S2-S3 and near-simultaneity for S4 (peaks ~ -0.17 +/- 1.62 hr; ends ~ +0.04 +/- 3.33 hr). Flux comparisons show that EPHIN measurements modestly exceed GOES for S1 (median ratios ~1.11 for peaks and ~1.06 for fluence) and are lower than GOES for stronger events (peaks ~0.97 +/- 0.29, 0.84 +/- 0.21; fluence ~0.84 +/- 0.16, 0.75 +/- 0.16 for S2-S3). The EPHIN-to-GOES peak flux and fluence ratios reach 0.16 +/- 0.03 and 0.29 +/- 0.07, respectively, for S4 events, originating from contamination of lower-energy GOES channels. Correlation analyses show no significant flux dependence on geomagnetic indices, field strength, or spacecraft position, suggesting minimal near-Earth modulation of >=10 MeV
proton access at GEO. These results highlight systematic differences in how SPEs manifest at L1 versus GEO and offer practical guidance for
forecasting beyond Earth's magnetosphere, supporting mission planning for near-Earth and cislunar exploration, including Artemis.
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Optics
Joint control of coherent transmission, reflection, and absorption
Shiyu Li, Dongha Kim, Shanhui Fan et al. (+1 more)
Controlling multiple wave properties simultaneously poses a key challenge in coherent control of wave transport. We present a theory for joint coherent control of transmission, reflection, and absorption in linear systems. We prove that the numerical range provides the mathematical structure governing achievable responses, and reveal non-abelian effects due to non-commutativity between transmission, reflection, and absorption
matrices. We provide an
algorithm to achieve arbitrary target responses. Our results establish a theoretical foundation for joint coherent control of waves.
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All authors Shiyu Li, Dongha Kim, Shanhui Fan, Cheng Guo
Atmospheric and Oceanic Physics
Geographic variability in reanalysis wind speed biases: A high-resolution bias correction approach for UK wind energy
Yan Wang, Simon C. Warder, Ellyess F. Benmoufok et al. (+4 more)
Reanalysis
datasets have become indispensable tools for wind resource assessment and wind power simulation, offering long-term and spatially continuous wind fields across large regions. However, they inherently contain systematic wind speed biases arising from various factors, including simplified physical parameterizations, observational uncertainties, and limited spatial resolution. Among these, low spatial resolution poses a particular challenge for capturing local variability accurately. Whereas prevailing industry practice generally relies on either no bias correction or coarse, nationally uniform adjustments, we extend and thoroughly analyse a recently proposed spatially resolved, cluster-based bias correction framework. This approach is designed to better account for local heterogeneity and is applied to 319 wind farms across the United Kingdom to evaluate its effectiveness. Results show that this method reduced monthly wind power simulation errors by more than 32% compared to the uncorrected ERA5 reanalysis
dataset. The method is further applied to the MERRA-2
dataset for comparative evaluation, demonstrating its effectiveness and robustness for different reanalysis products. In contrast to prior studies, which rarely quantify the influence of topography on reanalysis biases, this research presents a detailed spatial mapping of bias correction factors across the UK. The analysis reveals that for wind energy applications, ERA5 wind speed errors exhibit strong spatial variability, with the most significant underestimations in the Scottish Highlands and mountainous areas of Wales. These findings highlight the importance of explicitly accounting for geographic variability when correcting reanalysis wind speeds, and provide new insights into region-specific bias patterns relevant for high-resolution wind energy modelling.
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All authors Yan Wang, Simon C. Warder, Ellyess F. Benmoufok, Andrew Wynn, Oliver R. H. Buxton, Iain Staffell, Matthew D. Piggott
Atmospheric and Oceanic Physics · Cross-listed from cs.CV
Global 3D Reconstruction of Clouds & Tropical Cyclones
Shirin Ermis, Cesar Aybar, Lilli Freischem et al. (+7 more)
Accurate
forecasting of tropical cyclones (TCs) remains challenging due to limited satellite observations probing TC structure and difficulties in resolving cloud properties involved in TC intensification. Recent research has demonstrated the capabilities of machine learning methods for 3D cloud reconstruction from satellite observations. However, existing approaches have been restricted to regions where TCs are uncommon, and are poorly validated for intense storms. We introduce a new framework, based on a pre-training–fine-tuning pipeline, that learns from multiple satellites with global coverage to translate 2D satellite imagery into 3D cloud maps of relevant cloud properties. We apply our model to a custom-built TC
dataset to evaluate performance in the most challenging and relevant conditions. We show that we can - for the first time - create global instantaneous 3D cloud maps and accurately reconstruct the 3D structure of intense storms. Our model not only extends available satellite observations but also provides estimates when observations are missing entirely. This is crucial for advancing our understanding of TC intensification and improving forecasts.
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All authors Shirin Ermis, Cesar Aybar, Lilli Freischem, Stella Girtsou, Kyriaki-Margarita Bintsi, Emiliano Diaz Salas-Porras, Michael Eisinger, William Jones, Anna Jungbluth, Benoit Tremblay
Atomic Physics · Cross-listed from quant-ph
Real-time magnetic field noise correction using trapped-ion monitor qubits
Kyle DeBry, Agustin Valdes-Martinez, David Reens et al. (+2 more)
We demonstrate a trapped-ion protocol in which a nearby, dedicated "monitor"
qubit tracks
magnetic-field drifts in real time without interrupting data-
qubit operations. Using two $^{40}\mathrm{Ca}^+$ ions and the optical–metastable–ground architecture, we encode the data
qubit in the ground-state
manifold and the monitor
qubit in a metastable-state
manifold to achieve spectral separation. The monitor
qubit senses common
magnetic fluctuations during data-
qubit experiments, enabling feedforward corrections to the
qubit-control drives. Under applied
magnetic noise with a realistic spectrum ($1/f^{2}$), the protocol maintains coherence and, when compared with interleaved calibration, it extends usable data-
qubit probe times by up to a factor of ${\sim}\sqrt{2}$ and doubles the experimental duty cycle. These results establish monitor
qubits as a scalable tool for real-time recalibration in
quantum information processors.
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All authors Kyle DeBry, Agustin Valdes-Martinez, David Reens, Colin D. Bruzewicz, John Chiaverini
Fluid Dynamics · Cross-listed from astro-ph.SR
Secondary small-scale dynamics of a Rayleigh-Taylor unstable solar prominence
Madhurjya Changmai, Jack M. Jenkins and Rony Keppens
Quiescent
solar prominences show distinct small-scale dynamics in observations. Their internal density contrasts with the surrounding corona make them susceptible to Rayleigh-Taylor (RT) instabilities, leading to vertically structured prominence morphologies when observed at the
solar limb. As a result, prominences develop bubbles and plumes, along with secondary Kelvin-Helmholtz (KH) roll-ups along their edges. Recent observations also suggest
magnetic reconnection events within the RT-driven turbulent flows. We perform high-resolution 2.5D resistive magnetohydrodynamic simulations using the open-source MPI-AMRVAC code, reaching a spatial resolution of $\sim 11.7$ km in a 2D domain of size 30 Mm$\times$30 Mm and evolving the system for approximately 10 minutes of
solar time. A dense,
magnetic pressure supported prominence serves as the initial state, which becomes unstable at the prominence-corona interface. The resulting interaction between RT and KH instabilities leads to the formation of current sheets and localized reconnection events. The reconnection-driven
outflows form energetic jets that enhance energy transport and dissipation across the prominence. We analyze our high-resolution prominence simulation using synthetic images of the broadband SDO/AIA 094, 171, and 193 Å and narrowband H$\alpha$ filters, to compare the developing fine-scale structures with their observational counterparts. Most secondary instabilities emerge in the hotter coronal regions surrounding the cooler prominence core. While our simulated features match observed scales, speeds, and duration, the simulated activity remains concentrated in hot, surrounding coronal
plasma rather then the cool prominence material, implying that key physical ingredients may be missing. Future 3D studies in more realistic
magnetic configurations are required to address these limitations.
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Instrumentation and Detectors
Hardware-Accelerated GNN-based Hit Filtering for the Belle II Level-1 Trigger
Greta Heine, Fabio Mayer, Marc Neu et al. (+2 more)
We present a hardware-accelerated hit filtering system employing Graph Neural Networks (GNNs) on Field-Programmable Gate Arrays (FPGAs) for the Belle II Level-1 Trigger. The GNN exploits spatial and temporal relationships among sense wire hits and is optimized for high-throughput hardware operation via quantization,
pruning, and static graph-building. Sector-wise spatial parallelization permits scaling to full-detector coverage, satisfying stringent latency and throughput requirements. At a sustained throughput of 31.804 MHz, the system processes sense wire data in real-time and achieves detector-level background suppression with a measured latency of 632.4 ns while utilizing 35.65% of Look-Up Tables (LUTs), and 29.75% of Flip-Flops, with zero Digital Signal Processing (DSP) usage, as demonstrated in a prototype implementation for a single sector on an AMD Ultrascale XVCU190. Offline validation using Belle II data yields a background hit rejection of 83% while maintaining 95% signal hit efficiency. This work establishes hit-level GNN-based filtering on FPGAs as a scalable low-latency solution for real-time data reduction in high-luminosity
collider conditions.
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All authors Greta Heine, Fabio Mayer, Marc Neu, Jürgen Becker, Torben Ferber
Fluid Dynamics
Stresses and fluid flow in lamina cribrosa through anisotropic poroelasticty
Riccardo Cavuoto, Sofia Damian, Luca Deseri et al. (+5 more)
To investigate the mechanical correlations between intraocular pressure (IOP) variations and glaucoma, this study presents a linear transversely isotropic poroelastic model of the lamina cribrosa (LC) based on Reissner-Mindlin plate theory. A key feature of the proposed framework is its analytical tractability, which allows the governing poroelastic equations to be solved in closed form under appropriate mechanical and hydraulic boundary conditions. Within this setting, linearity is used to capture the reversible component of the tissue response, providing a baseline description of the coupled solid-fluid feedback on which more complex time-dependent phenomena, such as viscoelastic effects and remodelling, may build.The results indicate that both strain and stress measures (in the form of shear strain and deviatoric stress measures) peak in the peripheral region of the LC, which is currently suspected to be the initial site of glaucomatous damage. These quantities increase with IOP, suggesting a pressure-dependent mechanical insult to the retinal ganglion
cell (RGC) axons. In parallel, the model predicts a monotonic reduction in fluid content as IOP rises, which may contribute to ischemic phenomena and disc haemorrhages. The influence of material anisotropy was also examined, revealing that isotropic assumptions tend to overestimate the fluid content while underestimating shear strain. Given the current experimental challenges in measuring blood flow within the LC, the proposed model provides a valuable framework for exploring the coupled mechanical-hemodynamic behavior of the tissue and for inverse estimation of its mechanical parameters, such as the stiffness of the opening for the central retinal vessels.
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All authors Riccardo Cavuoto, Sofia Damian, Luca Deseri, Massimiliano Fraldi, Alon Harris, Brent Siesky, Alice Verticchio, Giovanna Guidoboni
Physics Education
CATS: Empowering the next generation of rocket scientists through educational flight computers
Jonas Binz, Nemanja Stojoski and Luca Jost
This paper presents an in-depth analysis of the Vega flight computer, and its corresponding ground station developed by CATS, a company producing open-source flight computers and tracking systems tailored for student-made rockets. These flight computers, designed to support rockets reaching altitudes of up to 30 km and possibly higher, play a crucial role in advancing educational rocketry and facilitating hands-on learning experiences in aerospace engineering. As the official sponsor of the European Rocketry Challenge (EuRoC), these flight computers have become integral to the competition, providing reliable and sophisticated telemetry and control capabilities that enhance both safety and performance. The paper delves into the technical specifications and educational impact of these systems, highlighting their contribution to the broader European rocketry programmes. Through comprehensive field data and case studies from the recent European Rocketry Challenge, this study underscores the potential of open-source flight computers to inspire the next generation of aerospace professionals.
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Instrumentation and Detectors
A prototype neutron-detector array for future deep-underground s-process studies
Thomas Chillery, David Rapagnani, Chemseddine Ananna et al. (+12 more)
We report a novel
neutron-detection approach employing an EJ-309
liquid scintillator surrounded by six 3He proportional counters. Tests were performed at the FRANZ facility of the Goethe-University Frankfurt using the 7Li(p,n0)7Be reaction, producing
neutrons across energies 50-720 keV. The scintillator's
neutron energy quenching is determined, and its
neutron/gamma-ray discrimination performance is evaluated. The lowest detectable
neutron energy is 163 keV. EJ-309 - 3He counter
neutron coincidences are compared with those from simulations. This array forms the prototype of a larger design, called scintillator-3He array for deep-underground experiments on the S-process, currently undergoing construction and testing for an upcoming deep-underground study of the 22Ne(alpha,n)25Mg reaction cross-section at the freshly-commissioned Bellotti Ion Beam facility of the INFN Laboratori Nazionali del Gran Sasso. This upcoming project is expected to achieve exceptionally low sensitivity for measuring the cross section at energies of interest for the astrophysical 'weak' and 'main' slow
neutron-capture processes.
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All authors Thomas Chillery, David Rapagnani, Chemseddine Ananna, Edoardo D'Amore, Gianluca Imbriani, Antonino di Leva, Daniela Mercogliano, Jakub Skowronski, Benjamin Brückner, Sophia Dellmann, Philipp Erbacher, Tanja Heftrich, René Reifarth, Mario Weigand, Andreas Best
Computational Physics · Cross-listed from astro-ph.CO
KGB-evolution: a relativistic $N$-body code for kinetic gravity braiding models
Ahmad Nouri-Zonoz, Farbod Hassani, Emilio Bellini et al. (+1 more)
We present KGB-
evolution, a relativistic $N$-body simulation code that extends the $k$-
evolution code by incorporating an effective field theory parameterization of kinetic
gravity braiding, while also including the $k$-essence model as a limiting case. As a first step, we implement the linearized dark energy stress-energy tensor and scalar field equations, providing the groundwork for a future full Horndeski theory extension. We validate KGB-
evolution by comparing its power spectra against linear predictions from hi$\_$class, finding excellent agreement on large scales at low
redshifts and over all scales at high
redshifts. We demonstrate that nonlinear growth of matter and metric perturbations on small scales drives the linearized dark energy field into a nonlinear clustering regime, which in turn feeds back on the growth of
cosmic structure. In contrast to the $k$-essence limit, a nonzero braiding considerably amplifies this backreaction, producing a significantly stronger alteration of structure formation in the kinetic
gravity braiding model.
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All authors Ahmad Nouri-Zonoz, Farbod Hassani, Emilio Bellini, Martin Kunz
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Nowcast3D: Reliable precipitation nowcasting via gray-box learning
Huaguan Chen, Wei Han, Haofei Sun et al. (+9 more)
Reliable nowcasting of extreme precipitation remains difficult because convective systems are strongly nonlinear, multiscale, and nonstationary in 3D. Radar is the backbone of nowcasting, yet existing methods struggle to predict extremes: physics-based extrapolation cannot capture growth and decay, deterministic learning tends to oversmooth and underestimate peaks, and purely generative models often lack physical consistency. Hybrid schemes help but are mostly limited to 2D composite reflectivity, collapsing the atmosphere into one layer and discarding vertical structure critical for height-dependent dynamics. We introduce Nowcast3D, a gray-box, fully 3D framework that works directly on volumetric radar reflectivity. The end-to-end model couples physically constrained neural operators (advection, local
diffusion, and microphysics) with a conditional
diffusion model to generate ensemble forecasts with quantified uncertainty. Trained on provincial-scale 3D volumes over a $10.24^\circ \times 10.24^\circ$ region and fine-tuned on a $2.56^\circ \times 2.56^\circ$ city region ($0.01^\circ \approx 1$ km), Nowcast3D provides near-real-time forecasts up to 3 h and outperforms competitive baselines in cross-region and temporal out-of-sample tests. It can also infer wind fields without labeled supervision, supporting physically plausible transport. In a nationwide blind evaluation by 160 meteorologists, Nowcast3D ranked first and was preferred in 57% of post-hoc assessments, surpassing the leading baseline (27%). These results highlight its reliability and operational value for extreme precipitation nowcasting.
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All authors Huaguan Chen, Wei Han, Haofei Sun, Ning Lin, Xingtao Song, Yunfan Yang, Jie Tian, Yang Liu, Ji-Rong Wen, Xiaoye Zhang, Xueshun Shen, Hao Sun
Optics · Cross-listed from cs.CV
Polarization-resolved imaging improves eye tracking
Mantas Žurauskas, Tom Bu, Sanaz Alali et al. (+10 more)
Polarization-resolved near-infrared imaging adds a useful optical contrast mechanism to eye tracking by measuring the polarization state of light reflected by ocular tissues in addition to its intensity. In this paper we demonstrate how this contrast can be used to enable eye tracking. Specifically, we demonstrate that a polarization-enabled eye tracking (PET) system composed of a polarization–filter–array camera paired with a linearly polarized near-infrared illuminator can reveal trackable features across the sclera and gaze-informative patterns on the cornea, largely absent in intensity-only images. Across a cohort of 346 participants, convolutional neural network based machine learning models trained on data from PET reduced the median 95th-percentile absolute gaze error by 10–16% relative to capacity-matched intensity baselines under nominal conditions and in the presence of eyelid occlusions, eye-relief changes, and pupil-size variation. These results link light–tissue polarization effects to practical gains in human–computer interaction and position PET as a simple, robust sensing modality for future wearable devices.
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All authors Mantas Žurauskas, Tom Bu, Sanaz Alali, Beyza Kalkanli, Derek Shi, Fernando Alamos, Gauresh Pandit, Christopher Mei, Ali Behrooz, Ramin Mirjalili, Dave Stronks, Alexander Fix, Dmitri Model