Data Analysis and Statistics
Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration
Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer et al. (+4 more)
Earth system science is producing increasingly large, high-dimensional
datasets from both physics-based and AI-driven models. While
embedding-based representations make these data searchable and serve as foundational building blocks for AI-driven discovery engines, nearest neighbors in latent spaces are not automatically scientifically meaningful. They may reflect real meteorological structures, or simply artifacts of preprocessing, geography, or model bias. Researchers therefore need visual tools to inspect latent space organization, trace search results back to physical evidence, and evaluate candidate representations against one another. We present an open source visual analytics workbench designed to support this provenance-aware scientific
retrieval workflow. The system links distinct
embedding experiments to shared source data, metadata, spatial contexts, and model configurations. It enables interactive
retrieval strategy design by allowing users to issue image-level and localized patch-level queries, apply multi-constraint filters, and inspect analogs through familiar meteorological views. This facilitates a discovery loop where scientists characterize a phenomenon in a well-understood
dataset and use its latent signature to probe larger archives. While we demonstrate the workbench through a tropical cyclone
retrieval scenario using a
vision foundation model (DINOv3) on ERA5 data, the framework is model-agnostic and designed to integrate with other
embedding architectures in the future. Finally, we evaluate its out-of-core
retrieval backend, demonstrating that interactive visual search over tens of millions of
embeddings is highly scalable on commodity hardware.
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All authors Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer, David John Gagne, John Schreck, John Clyne, Charlie Becker
Atomic Physics
Separation of even-even from even-odd isotopes using ultrafast lasers
Jacob Levitt
We propose a
laser isotope separation mechanism in which selectivity arises from
nuclear spin rather than isotope shifts, enabling the use of broadband ultrafast
lasers. A Ramsey pulse sequence is applied to paramagnetic molecular isotopologues possessing two electronic states coupled by a dipole transition. For even-even isotopologues (
nuclear spin $I = 0$), each electronic state is a single level and the time-reversed sequence returns all population to the ground state exactly. For even-odd isotopologues ($I > 0$), the hyperfine interaction splits each state into multiple levels with coupling amplitudes set by Wigner $6j$ symbols; incommensurate phase
evolution during the dark interval prevents the echo from closing, trapping a fraction $P_m$ of the population in the excited
manifold. In the impulsive limit ($\Omega \gg A_{\rm HF}$), $P_m$ depends only on the angular momentum
quantum numbers $(J_g, J_m, I)$ and is independent of
laser intensity or bandwidth. Density
matrix simulations confirm $P_m = 0$ for $I = 0$ and $P_m \approx 0.23$-$0.47$ for $I > 0$ across representative systems including ${}^{235}$U, ${}^{87}$Sr, and ${}^{57}$Fe. Under realistic collisional conditions, single-pass enrichment exceeding 90% from natural feed is achievable without cascading.
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Fluid Dynamics
An ALE-Consistent Graph Neural Operator-Transformer Framework for Fluid-Structure Interaction
Shihang Zhao, Martín Saravia, Haokui Jiang et al. (+2 more)
We propose an arbitrary Lagrangian-Eulerian (ALE)-consistent machine learning framework for long-term fluid-structure interaction (FSI) prediction on deforming unstructured meshes. Specifically, the fluid dynamics are modeled by a surrogate that combines a graph neural operator (GNO) with a
vision Transformer (ViT) for spatiotemporal prediction, while a lightweight long short-term
memory (LSTM) network predicts structural kinematics at the interface. The two surrogates are coupled through a standard partitioned procedure. Most importantly, kinematic compatibility at the moving interface is enforced via an ALE-consistent boundary-correction step that updates the fluid-side interface velocity with the predicted structural velocity at each coupling update, thereby improving near-interface accuracy and long-term rollout stability. To mitigate autoregressive error accumulation, a two-stage training strategy is adopted, consisting of single-step supervised pretraining followed by long-term autoregressive fine-tuning. The proposed framework is validated on the
benchmark problem of a flexible beam vibration in the wake of a cylinder. Results demonstrate accurate phase-consistent predictions over long rollouts and robust generalization under inlet-profile variations in both interpolation and extrapolation settings. Systematic ablation studies further assess the respective contributions of the ViT module, ALE-consistent boundary correction, and long-term training to predictive accuracy and rollout robustness.
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All authors Shihang Zhao, Martín Saravia, Haokui Jiang, Zhiyang Xue, Shunxiang Cao
Biological Physics
Statistical mechanics for Scrabble predicts strategy, entropy and language
Olivier Witteveen and Marianne Bauer
The crossword-like patterns of tiles in Scrabble form connected graphs of occupied sites on a square
lattice. We find the most structureless description that reproduces means and covariances observed in real Scrabble games by adapting a maximum
entropy approach to connected graphs. This pairwise model captures the data well, and predicts word-length statistics and
geometric features of the Scrabble graphs correctly; in addition, the parameters of this model are interpretable and allow us to understand Scrabble playing strategies. Using this pairwise model, we calculate
entropy differences and distinguishability of Scrabble graphs across
languages, without having access to the letters on the tiles. Notably, we find that the
entropy is predicted better by strategic gameplay – such as word length on the board – than lexicon size. Finally, we find that we can use the pairwise model to correctly assign Scrabble graphs to
languages, avoiding explicit feature selection and at relatively low computational cost.
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Physics and Society
Simpson's paradox explains the ubiquity of nonlinear, threshold, and complex contagions
Laurent Hébert-Dufresne, Antoine Allard, Jean-Gabriel Young et al. (+2 more)
Complex contagions describe systems where the probability or rate of contagious transmission is a nonlinear function of the exposure to contagious
agents. These models were first studied theoretically but have since been used to capture effects such as nonconformism, social reinforcement or peer pressure in empirical data. However, recent studies have shown that local correlations (e.g., group structure or temporal burstiness) and heterogeneity (e.g., diversity of parameters or
covariates) can give the illusion of nonlinear effects even when the dynamics is actually linear. We briefly review these studies to inform a new model and explanation for these effective models of complex contagions. We find global threshold dynamics and superlinear complex contagions even in populations where
agents are distributed across social groups described solely by linear or even sublinear contagions. This effect can be understood as a manifestation of Simpson's paradox. Incidence data from heterogeneous groups can look superlinear once averaged over all groups, since the sampling of groups represented at high incidence is biased towards those with stronger local transmission. We then define what we call a Simpson's contagion: a contagion process that looks superlinear when observed over an entire population, but is mechanistically linear or even sublinear in all of its subgroups. By exploring these Simpson's contagions over mathematical case studies, our work contributes to the growing body of literature on the ubiquity of threshold and complex contagions as effective models, and our results stress the pitfall of model selection that ignores correlations and heterogeneity in populations.
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All authors Laurent Hébert-Dufresne, Antoine Allard, Jean-Gabriel Young, William H. W. Thompson, Guillaume St-Onge
Computational Physics
Unsupervised Learning of Quantum Phase Transitions for Bose-Hubbard lattice systems
Bihui Zhu
Characterizing
quantum many-body phase structure is a major goal for
quantum simulation. Here, we employ an unsupervised learning approach based on
diffusion maps to learn phase transitions in bosonic
lattice systems described by Bose-Hubbard type models, which can be realized in ultracold atoms and related
quantum simulation platforms. We demonstrate that this approach identifies phase structure across distinct settings without prior knowledge of order parameters or handcrafted observables, including ground-state transitions involving
symmetry-protected topological phases and nonequilibrium regimes distinguishing ergodic and many-body localized behavior. Our results indicate that the approach has the potential for direct application to experimentally accessible measurement data for learning
quantum phases in current
quantum simulators.
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Plasma Physics · Cross-listed from astro-ph.SR
Magnetic loops in the solar transition region
Zhenghua Huang
Transition region (TR) loops are arcade-like features in the
solar transition region, with temperatures roughly between $2\times10^4$ K and $6\times10^5$ K. They are a fundamental building block of TR, which are results of the coupling between the
magnetic field and the TR
plasma. Their dynamics is closely related to the transport of energy and mass through the TR. Studies on this class of loops since the launch of the Interface Region Imaging Spectrograph (IRIS) have revealed that they are distinct from coronal loops. Observations have revealed that they are associated with many small-scale dynamic phenomena in the TR, from which one can infer the physics behind the energy and mass transfer in a magnetically confined environment at TR temperature. This review summarises the observational results of TR loops, showing their morphology, dynamics,
plasma parameters, their relationship with flux emergence, their heating properties, and their implication in the heating of the
solar atmosphere. This class of
magnetic loops is much less well understood than their coronal counterparts. This review also concludes with several critical questions that need to be answered in the coming era with more advanced observational techniques and more precise and realistic simulations.
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Physics and Society
Optimal network structure for collective performance with strategic information sharing
Ye Wang, Andrea Civilini, Anzhi Sheng et al. (+3 more)
Information sharing between individuals is crucial to improve performance in collective tasks. However, in a competitive world, individuals may be reluctant to share information with the others, and it is still unclear how the presence of strategic behaviors affects the collective performance of a group. In this study, we introduce an evolutionary game modeling the dynamics of individual behaviors in a collective estimation task. The individuals are organized in a network and have to guess the distribution of ball colors in a box. Each of them samples a given number of balls and can strategically decide whether to share or not this information with its neighbors. We develop a framework that allows to investigate analytically how the collective performance depends on the network structure. We find that the optimal network results from a trade-off between the sharing rate and the way the information is integrated in the network. We further reveal that there exists an intermediate average degree for each type of network maximizing the collective performance. In addition to the uniform case, we consider the case of non-homogeneous allocations of the number of individual samples, showing that the largest collective performance is obtained when the number of ball extracted by an individual is inversely proportional to its degree.
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All authors Ye Wang, Andrea Civilini, Anzhi Sheng, Xiaojie Chen, Long Wang, Vito Latora
Optics
Combined spatially and temporally multiplexed photonic reservoir computer with a diffractively coupled VCSEL-array
Joshua Robertson, Moritz Pfluger, Ingo Fischer et al. (+2 more)
We report and analyse the classification performance of an experimental hybrid spatio-temporal photonic reservoir computer based upon a free-space VCSEL array. We demonstrate experimentally the enhancement of spatial-only reservoir operation, featuring the diffractive coupling of
lasers in an external cavity, by exploiting up to 88 virtual nodes with time multiplexing. We analyse the dependance of performance on the spatial and virtual node number, and achieve an improvement for both spatial- and temporal-only reservoirs with a reduced test error of 0.026 in a classification task. Further, given the high bandwidth of the non-linear
laser transformation, we demonstrate the expansion of a 12 spatial node network to a 968 node network, operating at an input time of 17.6ns, maintaining high processing speed and improving network scalability and performance.
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All authors Joshua Robertson, Moritz Pfluger, Ingo Fischer, Miguel Soriano, Antonio Hurtado
Optics · Cross-listed from q-bio.NC
Functional Connectivity-Guided Band Selection for Motor Imagery Brain-Computer Interfaces
Natália Araújo do Carmo and Aarthy Nagarajan
Reliable control in motor imagery
brain-computer interfaces (MI-BCIs) requires the precise decoding of user-specific neural rhythms, which vary significantly across individuals. The Common Spatial Pattern (CSP)
algorithm is a cornerstone of MI-BCI decoding, yet its performance depends strongly on the spectral range of the input
EEG data. Although Filter Bank CSP (FBCSP) extends this as a data-driven decoding framework, its frequency sub-bands are predefined rather than selected using subject-specific physiological criteria. This paper presents a proof-of-concept study of static functional connectivity (FC)-guided band selection for MI-BCI, demonstrated using a conventional FBCSP-based pipeline. The proposed method identifies the most discriminative spectral bands by calculating phase-based connectivity across four sensorimotor channels using wPLI, PLV, and PLI. Nine bands in a 4-40 Hz filter bank are ranked by the effect size of their hemispheric coupling differences and pruned to the top K bands for feature extraction and classification via FBCSP and a Support Vector Regressor. This framework was tested for K values ranging from 1 to 8 across the BCI Competition IV-2a (n = 9) and OpenBMI (n = 54)
datasets. Performance was benchmarked against standard nine-band FBCSP and random ablation to determine the minimum number of bands (K*) required to maintain accuracy within a 2% baseline equivalence zone. Results show FC-guided selection can outperform random ablation and achieve near-baseline performance while reducing required CSP fits by 22.2% to 77.8%. PLV enables the most aggressive dimensionality reduction by prioritizing the {\mu} and low-e̱ta} ranges, while wPLI demonstrates superior inter-session robustness by mitigating volume conduction. These findings establish FC-guided selection as a principled and interpretable alternative to heuristic filter bank designs.
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Computational Physics · Cross-listed from cs.CE
Reconstruction of glymphatic transport fields from subject-specific imaging data, with particular emphasis on cerebrospinal fluid flow and tracer conservation
A. Derya Bakiler, Michael J. Johnson, Michael R.A. Abdelmalik et al. (+5 more)
The reconstruction of physically valid transport fields from subject-specific imaging data is a fundamental challenge in image-based computational modeling due to measurement noise, modeling uncertainties and discretization errors. Without a methodology to construct models that faithfully reflect the underlying physics, mechanistic understanding of complex biological systems is inherently limited. In this work, we address this challenge in the glymphatic system, the
brain's waste-clearance network, where cerebrospinal fluid (CSF) is transported through perivascular spaces into the
brain parenchyma to facilitate metabolic waste removal. We introduce a computational framework for the high-fidelity reconstruction of subject-specific glymphatic transport fields from spatiotemporal imaging data. The formulation utilizes an advection-
diffusion model with a velocity decomposition that imposes mass conservation, enabling the recovery of solenoidal (divergence-free) velocity fields through the solution of a constrained inverse problem. The system is discretized using immersed isogeometric analysis with quadratic B-spline basis functions, providing smooth, high-continuity solutions and inherent regularization of imaging noise. We demonstrate the framework's utility by using contrast-enhanced
magnetic resonance imaging of tracer transport in a mouse
brain, obtaining spatially varying estimates of CSF velocity, diffusivity, and clearance parameters. Forward simulations using the recovered fields show close agreement with experimental observations, validating the framework's ability to characterize complex transport dynamics while preserving physical integrity. This approach provides a generalizable methodology for the robust inference of physically consistent transport fields from imperfect imaging data, with broad applicability to the image-guided modeling of biological and engineering systems.
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All authors A. Derya Bakiler, Michael J. Johnson, Michael R.A. Abdelmalik, Frimpong A. Baidoo, Andrew Badachhape, Ananth V. Annapragada, Thomas J.R. Hughes, Shaolie S. Hossain
Atmospheric and Oceanic Physics
Leveraging Climate Services to Build Climate Resilient Power Systems
Laurent Dubus, Alberto Troccoli, Aron zuiker et al. (+1 more)
We explore the crucial interplay between
climate change and power system planning, highlighting the urgent need to systematically integrate
climate information into energy system studies.
Climate change impacts the energy sector on multiple fronts. Short-term weather variability drives daily and seasonal fluctuations in supply and demand. Long-term trends and increased frequency of extremes pose risks to infrastructure performance, asset lifetimes, and system adequacy. Representing compound events and spatial correlations across borders is a complex challenge, and uncertainties persist due to uncertainties from different models, scenarios, and downscaling methodologies. The Pan-European
Climate Database (PECD4.2), developed in partnership between ENTSO-E and C3S, marks a change in how energy system planning is conducted. The PECD4.2 integrates historical reanalysis and six
climate models across four SSP's, providing harmonised, openly available
datasets tailored for power system studies. The physical conversion models for wind and
solar energy better reflect technological progression than machine learning methods trained on historical data, improving robustness under changing future conditions. Despite these advances, challenges remain. Particularly in hydropower modelling and the lack of public harmonised energy
datasets that are required to train these models. Complex processing chains from raw
climate data to actionable insights and the lack of standardized integration of
climate information lengthen lead times for energy-sector adoption. This leads to diverging approaches and variable consideration of
climate risks. Closer, more generalised collaboration and communication between
climate service providers and energy stakeholders are therefore necessary, as are the development of user-friendly tools for data manipulation and analysis and robust feedback loops.
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All authors Laurent Dubus, Alberto Troccoli, Aron zuiker, Laurens Stoop
Optics · Cross-listed from astro-ph.IM
Beyond Bragg-Mirrors for Gravitational Wave Telescopes: A Fabrication Tolerant Hybrid Metasurface-Bragg Mirror Design
Christian Kranhold, Mika Gaedtke, Markus Walther et al. (+3 more)
Coating thermal noise in high-reflectivity test-mass mirrors is a major limitation for future gravitational-wave detectors, especially in the 10–300 Hz band. ET-Pathfinder therefore requires mirror coatings that combine very high reflectance at 1.55 micrometer with low thermal noise under cryogenic conditions. Conventional dielectric Bragg mirrors provide high reflectance but require thick coatings, whereas metasurface mirrors can reduce coating-related noise but are limited by fabrication tolerances and line-edge roughness. We present a hybrid metasurface–Bragg mirror concept tailored to ET-Pathfinder. The design combines a fabrication-tolerant one-layer metasurface, an anti-resonant Fabry–Perot spacer, and a reduced dielectric Bragg stack. Optical performance is evaluated using full-wave electromagnetic simulations, while fabrication robustness is assessed with a truncated-Gaussian Monte Carlo analysis. Line-edge roughness is included as a systematic edge-smoothing effect. The resulting reflectance distributions are used to determine the minimum Bragg-stack support required to meet system-level specifications. The ideal metasurface exceeds 99.999% reflectance. When fabrication uncertainties and line-edge roughness are included, the metasurface reflectance is limited to about 99.9% at the 95% yield level. The remaining transmission can be compensated by a supporting Bragg stack with as few as seven layer pairs. For this configuration, the hybrid mirror achieves a total thermal displacement noise about one order of magnitude below the projected ET-Pathfinder coating-noise budget. These results show that fabrication-limited metasurface reflectance can be compensated within a hybrid architecture, enabling reduced coating thickness and thermal noise for next-generation gravitational-wave detectors.
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All authors Christian Kranhold, Mika Gaedtke, Markus Walther, Falk Eilenberger, Stefanie Kroker, Thomas Siefke
Geophysics
D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
Andreas Dombos, Junyang Gou and Benedikt Soja
The
Gravity Recovery and
Climate Experiment (GRACE) and GRACE Follow-On missions provide monthly terrestrial water storage anomaly (TWSA) estimates for monitoring large-scale water storage change. The monthly temporal resolution of official products limits the analysis of high-frequency hydrological events, while existing daily GRACE products often have reduced spatial resolution due to sparse groundtrack coverage and required smoothing and regularization. This study introduces D-SHIFT (Daily Spatial High-Resolution Inference via Feature Transformation), a deep learning-based framework for generating daily, high-resolution TWSA fields from daily spherical harmonic coefficient (SHC) solutions. The model is trained in the monthly domain by using low-resolution daily solutions and other auxiliary features as inputs, while targeting on monthly mascon products. The model is then applied to daily SHC inputs to generate products with similar spatial resolution of monthly products. Monthly validation against mascon products gives a global mean root mean square error of about 2.3cm, with good correlation and explained variance agreement. Daily analyses show that D-SHIFT produces spatially coherent day-to-day fields and improves basin-scale trend and seasonality estimates compared with low-resolution SHC. The basin-area double-difference analysis indicates that these gains are most relevant for spatially localized signals affected by smoothing and leakage. In Greenland, D-SHIFT better reproduces coastal mass-loss patterns and gives a basin-mean trend of -10.5cm/yr, close to the CSR Monthly value of -12.0cm/yr.
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Instrumentation and Detectors
EQSANS-CLI: A natural-language, agent-ready command-line tool for small-angle neutron scattering data reduction at EQ-SANS
Changwoo Do
Small-angle
neutron scattering (SANS) data reduction at user facilities follows a largely repetitive workflow. Runs are first classified in the catalog and matched to transmission, background, and empty-beam references within the same instrument configuration. The data are then reduced, placed on an absolute scale using a standard, and stitched across configurations. Although these steps are individually well understood, they remain weakly connected, producing a coordination burden that scales with the number of runs and configurations. This paper describes EQSANS-CLI, a command-line tool that organizes this workflow into a coherent, scriptable, and
agent-addressable system. The design rests on four principles: a shared command-handler layer that all input paths converge on; a persistent working table that holds every reduction decision as editable rows; two input surfaces (an interactive terminal and a headless JSON mode) that compile to the same handler entry point; and a status-driven re-reduction model that treats parameter changes as first-class events. An /autopilot command chains the full pipeline from the IPTS number to stitched $I(Q)$ curves in one invocation. A Slack bot demonstrates that the headless interface, together with a single skill document loaded into an external
agent's system
prompt, is sufficient to drive complete reductions by natural
language from a mobile device. The architecture is intentionally minimal on the
agent side: the CLI is the authoritative executor, and the
agent's only job is to translate human intent into commands on a stable contract.
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Chemical Physics · Cross-listed from cs.LG
Knowing when to trust machine-learned interatomic potentials
Shams Mehdi, Ilkwon Cho and Olexandr Isayev
Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These methods scale unfavorably with foundation-scale MLIPs, and their member-disagreement signals correlate weakly with per-molecule prediction error. Here we probe the frozen per-atom representations of a pretrained MLIP with a compact discriminative classifier, recasting MLIP uncertainty quantification as selective classification rather than error regression. The resulting method, PROBE (Post-hoc Reliability frOm Backbone
Embeddings), produces a per-prediction reliability probability that monotonically tracks actual error without modification to the underlying model. Across large held-out evaluation sets and two structurally distinct MLIP architectures, PROBE outperforms ensemble disagreement as a binary reliability signal, which strengthens with the expressiveness of the backbone representation, implying a favorable scaling trajectory toward foundation-scale MLIPs. Multi-head self-
attention additionally yields per-atom importance maps, providing chemically interpretable diagnostics at no additional computational cost. PROBE is post-hoc and architecture-agnostic, and is directly deployable on any MLIP that exposes per-atom representations.
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Plasma Physics
Theory for the power threshold of the L-H transition in tokamaks
Brenno De Lucca, Paolo Ricci, Benoit Labit et al. (+3 more)
Flux-driven two-fluid simulations in a diverted tokamak exhibit a confinement transition when a critical heating threshold is exceeded, as in experimental observations of the L-H transition. The simulations show that electromagnetic drift-wave
turbulence spontaneously generates a sheared $E\times B$ flow responsible for transport suppression in the edge. The toroidal-field asymmetry of the transition, here reproduced in simulations for the first time, arises from time-reversal
symmetry breaking by finite collisionality, as demonstrated by a quasilinear calculation of the turbulent momentum flux. Scaling laws are analytically derived for the L-H power threshold in the high- and low-density branches, for the density minimum, and for the minimum power, all matching or surpassing the accuracy of existing empirical scalings.
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All authors Brenno De Lucca, Paolo Ricci, Benoit Labit, Davide Mancini, Louis Stenger, Zeno Tecchiolli
Atmospheric and Oceanic Physics
The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems
Christopher David Roberts, Sarah Keeley, Kristian Mogensen et al. (+2 more)
The oceans play a fundamental role in Earth's
climate system, redistributing heat and influencing global and regional
climate variability and predictability across weather and
climate timescales. The benefits of ocean-atmosphere coupling for initialised predictions depend on the balance between improvements associated with more realistic air-sea interactions and dynamics, and degradations arising from the development of systematic biases at the coupling interface. Here, we draw on recent developments in modelling and data assimilation at ECMWF to revisit the role of ocean-atmosphere coupling in subseasonal predictions. In particular, we evaluate the impact of ocean-atmosphere coupling in 46-day reforecasts produced with the ECMWF Integrated
Forecasting System (IFS) and explore the potential for improvements through increased horizontal resolution and a better representation of the ocean mesoscale. We find that ocean-atmosphere coupling significantly enhances ensemble forecast skill in the tropics, with positive effects increasing at longer lead times. In particular, Madden-Julian Oscillation (MJO) forecasts are substantially improved, with forecast skill extended by approximately 5 days compared to the uncoupled configuration. In contrast, ocean-atmosphere coupling has a more limited impact on the extratropical atmosphere at subseasonal timescales, with marginal impacts on the predictability of major tropospheric and stratospheric circulation indices. Finally, we present selected results from an experimental eddy-rich coupled configuration of the IFS, with a horizontal ocean resolution of approximately 8 km. We find that a better-resolved representation of the ocean mesoscale has a limited impact on atmospheric forecasts at subseasonal lead times, which suggests that many of the known deficiencies of the eddy-permitting reference configuration are mitigated by accurate initialisation.
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All authors Christopher David Roberts, Sarah Keeley, Kristian Mogensen, Charles Pelletier, Hao Zuo
Optics
Suppressing Plasmonic Heating in Aqueous Environments with Hexagonal Boron Nitride
Martina Russo, Roland van der Vegt, Bohai Liu et al. (+5 more)
Optical heating of plasmonic nanostructures is a critical challenge in nanoscale systems. Although plasmonic effects enable enhanced optical functionalities, the associated temperature rise can degrade performance in heat-sensitive applications such as biosensing, nanophotonics, and microelectronics. Conventional cooling strategies fail at these scales due to limited heat transport and high interfacial thermal resistance, motivating the integration of advanced materials for thermal management. Here, we investigate hexagonal boron nitride (hBN) thin flakes as heat spreaders to mitigate plasmonic heating of gold nanospheres immobilized on hBN deposited on glass and surrounded by water. Using finite-element simulations, we quantify the influence of hBN thickness, in-plane thermal conductivity, and interfacial thermal conductance on cooling efficiency. Complementary experiments employ cross-grating wavefront microscopy (CGM) for nanothermometry to map the temperature around optically heated gold nanoparticles and quantify the cooling effect of hBN. We extend the application of CGM for rapid, non-invasive, and all-optical characterization of non-absorbing 2D materials. Our results reveal a strong thickness dependence, where heat dissipation in thin flakes is limited by the heat capacity of hBN and in thick flakes by interfacial thermal conductance. Including hBN, we obtain a reduction in temperature rise by up to 60% compared to glass. In addition, the presence of two main heat dissipation pathways emerges: a direct one from the nanoparticle to the hBN and an indirect one from the particle via water to the hBN. This combined simulation-experiment framework offers a versatile approach to improve thermal management in plasmonic systems and beyond, establishing design guidelines for integrating 2D materials into thermally sensitive platforms such as biosensors and integrated
circuits.
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All authors Martina Russo, Roland van der Vegt, Bohai Liu, Sam Beijers, Sara Salera, Guillaume Baffou, Klaas-Jan Tielrooij, Peter Zijlstra
Applied Physics
High-pressure magnetic transition in iron observed via diamond quantum sensing
Kouki Yamamoto, Kenshin Uriu, Ryotaro Suda et al. (+5 more)
Diamond
quantum sensors offer high precision and spatial resolution as
magnetic probes, making them promising for a wide range of applications. While diamond anvil
cells (DACs) can generate extremely high pressures, techniques for magnetometry under such conditions remain limited. By fabricating an ensemble of NV centers directly on the anvil diamond surface, we enable precise
magnetic measurements under high pressure. In this work, we employ this NV ensemble to image the stray
magnetic field of iron up to 30 GPa, enabling the observation of the
magnetic transition ($\alpha$-$\varepsilon$ transition) in iron.
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All authors Kouki Yamamoto, Kenshin Uriu, Ryotaro Suda, Misaki Sasaki, Mari Einaga, Katsuya Shimizu, Kento Sasaki, Kensuke Kobayashi
Optics
Plasmon Induced Delocalized Second-Harmonic Generation Towards Buried-Interface Spectroscopy
Alan R. Bowman, Sergejs Boroviks, Omer Can Karaman et al. (+3 more)
Second-harmonic generation microscopy is a powerful technique capable of probing local
crystal symmetries and electric fields at interfaces. However, it often suffers from weak signal strength and is difficult to understand in multilayer systems where many materials can give competing signal contributions. In this work we present direct observation of delocalized, surface plasmon polariton-mediated second-harmonic generation on gold monocrystalline surfaces and structures. We generate second-harmonic light up to 35 um from the excitation spot and, excitingly, we obtain signal from atomically flat surfaces without a fundamental excitation beam present in the same region. We reveal that this process arises from the interaction of two counter-propagating surface plasmon polaritons, which we believe to be the first observation of this process at the microscale. This signal has the same polarisation dependence as localised second-harmonic generation and is emitted in a collimated beam travelling perpendicular to the sample surface. In part due to local electric field enhancements, we were able to observe these signals on a CMOS camera with 1 s exposure and no gain using an industrial-grade pulsed
laser. Our results enable wide area multilayer samples to be probed using a single excitation beam, with applications including in energy, catalysis and single particle surface sensing.
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All authors Alan R. Bowman, Sergejs Boroviks, Omer Can Karaman, Laura M. Herz, Olivier J.F. Martin, Giulia Tagliabue
Chemical Physics
High-resolution ro-vibrational and rotational spectroscopy of the open-shell, linear CCH$^+$ ion ($^3\Pi$)
Kim Steenbakkers, Weslley G.D.P. Silva, Oskar Asvany et al. (+5 more)
In this work, we report on the high-resolution infrared spectrum of CCH$^+$ ($^3\Pi$) recorded in the range $3066-3184$~cm$^{-1}$ by means of leak-out spectroscopy. This spectral range covers the fundamental of the CH stretching mode and a highly excited bending vibrational mode. Based on this data (385 ro-vibrational lines), accurate spectroscopic descriptions of the ground and the two vibrationally excited states of CCH$^+$ were obtained. Besides the band origins,
spin-orbit coupling constants, rotational constants, centrifugal distortion constants and $\Lambda$-doubling constants for the ground and excited vibrational states have been derived. This effective
Hamiltonian analysis allowed a search for pure rotational lines of CCH$^+$ in its electronic and vibrational ground state using a two-color millimeterwave - infrared scheme. We observed all rotational transitions from $J^{\prime\prime} = 2$ up to $J^{\prime\prime} = 6$ within the $\Omega = 2$ lowest energy fine structure component with resolved hyperfine splittings. This data has already guided the first detection of CCH$^+$ in space toward the Orion Bar photo-dissociation region, and has the potential to support further astronomical searches for CCH$^+$ either through
radio or infrared spectroscopy, for example with the James Webb Space
Telescope.
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All authors Kim Steenbakkers, Weslley G.D.P. Silva, Oskar Asvany, Gerrit C. Groenenboom, Pavol Jusko, Britta Redlich, Sandra Brünken, Stephan Schlemmer
Computational Physics
MuDirac 1.3.0: A Sustainable Software Tool for Calculating Ground State Nuclear Properties Using Muonic X-Ray Measurements
Leandro Liborio, Milan Kumar, Subindev Devadasan et al. (+4 more)
The
nuclear charge radius is one of the most fundamental quantities of the atomic
nucleus. It can be deduced from a combination of experimental measurements of muonicX-raytransitionenergieswithmodellingofthoseX-raytransitionenergies. In thisworkwepresentMuDirac (1.3.0), whichisanopen, publiclyavailable, sustainable and computationally efficient software tool that will be at put the disposal of the negative
muon community. With MuDirac (1.3.0), the community will be able to accurately and efficiently estimate
nuclear properties, such as the
nuclear charge radius, by assuming a 2-parameter
Fermi distribution of the
nuclear charge.
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All authors Leandro Liborio, Milan Kumar, Subindev Devadasan, Philip Jones, Martin Plummer, Adrian Hillier, Albert Bartok
Medical Physics
Pre-CAT: A web-based, graphical user-interface toolbox for preclinical CEST-MRI data processing and analysis
Jonah Weigand-Whittier, Samuel Rubin, Cindy Ayala et al. (+6 more)
Purpose: As interest in CEST-MRI grows, particularly in the preclinical setting, the necessity for standardized and easy-to-use acquisition and data analysis pipelines has become apparent. While vendors have increasingly introduced support for CEST acquisitions on both clinical and preclinical hardware, image post-processing and analysis pipelines remain siloed based on privately developed code. We aim to develop an easy-to-use, open-source graphical user interface toolbox for preclinical CEST-MRI data analysis (Preclinical CEST-MRI Analysis Tool; Pre-CAT), supporting multiple acquisition types, organ systems, and CEST contrast mechanisms. Methods: Pre-CAT was developed in Python and utilizes the Numpy, Scipy, and Matplotlib libraries for data analysis and plotting. Inbuilt data processing steps include image loading, reconstruction, post-processing, and
segmentation. Pre-CAT also supports data analysis for QUESP, CEST-MRF, and field mapping experiments using consensus protocols and methods. Pre-CAT's web interface and GUI were developed using Streamlit, an open-source Python framework. Pre-CAT is hosted and accessible online and can be downloaded for local installation to complete the data analysis pipeline in roughly one minute using modern hardware. Results: Pre-CAT analysis pipelines for Z-spectroscopy, CEST-MRF, and quantitative CEST (QUESP/QUEST) are demonstrated. Conclusion: With the introduction of Pre-CAT, we aim to standardize the preclinical CEST-MRI data analysis pipeline, fostering collaboration across research sites and reducing methodological redundancy. Pre-CAT is open-source and relatively modular, encouraging the addition of new methods and protocols.
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All authors Jonah Weigand-Whittier, Samuel Rubin, Cindy Ayala, Mark Velasquez, Nikita Vladimirov, Hadas Avraham, Or Perlman, M. Roselle Abraham, Moriel H. Vandsburger
Computational Physics · Cross-listed from cs.LG
Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
Mengke Zhao, Guang-Xing Li, Duo Xu et al. (+1 more)
Complex physical systems, from supersonic
turbulence to the macroscopic structure of the universe, are governed by continuous multiscale dynamics. While modern machine learning architectures excel at mapping the high-dimensional observables of these systems, it remains unclear whether they internalize the governing physical laws or merely interpolate discrete statistical correlations. Standard Explainable AI (XAI) architectures, particularly perturbation-based and gradient-saliency methods, rely on pixel-wise perturbations, which generate unphysical artifacts and push inputs off the valid empirical distribution. To resolve this, we introduce a diagnostic framework driven by Constrained
Diffusion Decomposition (CDD), a
diffusion-based multiscale data decomposition
algorithm that enables physically constrained data generation and model evaluation via scale-aware modifications. Applying this framework to a Denoising
Diffusion Probabilistic Model (DDPM), we execute deterministic interventions directly within the continuous, CDD-based scale space. We demonstrate that under moderate physical perturbations, the unconstrained generative model exhibits localized structural freezing and non-linear instability rather than continuous PDE-like responses. The network fails to maintain cross-scale continuity, causing the generative trajectory to diverge when pushed into unseen physical states. By synthesizing a continuum of physically coherent states, this scale-informed methodology establishes a controlled test ground to evaluate algorithmic vulnerabilities, providing the rigorous physical constraints necessary for future architectures to respect the multiscale causality of the natural universe.
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All authors Mengke Zhao, Guang-Xing Li, Duo Xu, Keping Qiu
Fluid Dynamics · Cross-listed from cond-mat.soft
Pre-charging polymer surfaces enhances droplet mobility and electrification
Shuaijia Chen, Kenta Morita, Dumindu Dassanayaka et al. (+4 more)
Surface-bound electric charge on
polymer materials can strongly influence droplet behaviour and solid-
liquid charge transfer, but the mechanisms and the means to control these effects remain unclear. In this work, we systematically controlled the surface charge on
polymer surfaces, including polytetrafluoroethylene (PTFE) and Nylon-66, by first neutralising the surfaces with an anti-static ion blower and then applying charge using an ion gun. We find that droplets pick up pre-deposited surface ions during the first wetting of the surface, and that the transferred charge directly correlates with the deposited charge encountered by the wetted area for moderate deposited densities (|{\sigma}_d |<40 {\mu}C/m2) independent of material properties. We also demonstrate that the deposited charge reduces contact angle and increases contact-line mobility in a manner consistent with an increase in effective solid surface energy. For higher surface charge densities, we observe instabilities such as droplet splitting or detachment. This work demonstrates an effective approach to control solid-
liquid electrification, enabling amplification or suppression of surface charge and the directed manipulation of fluid motion on surfaces.
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All authors Shuaijia Chen, Kenta Morita, Dumindu Dassanayaka, Hans-Jürgen Butt, Peter C. Sherrell, Amanda V. Ellis, Joseph D. Berry
Applied Physics · Cross-listed from cond-mat.mtrl-sci
An Advanced Epitaxial Strategy Enabling Vertical GaN Devices on Silicon Wafers
Fumio Kawamura, Takeyoshi Onuma and Kazutaka Mitsuishi
While vertical GaN-on-silicon architectures promise a transformative leap in cost-effective power electronics and high-resolution micro-LEDs, their deployment remains bottlenecked by the high electrical resistance of conventional epitaxial buffer layers. Here, a universal and straightforward sputtering-based strategy is presented to realize high quality GaN epitaxial films on Si(111) substrates characterized by exceptionally low vertical resistance, ohmic behavior, and robust thermal stability. This technique centers on the in-situ formation of a sub nanometer (0.5 nm) silicide-based template via rapid thermal annealing method demonstrating unprecedented versatility across 25 different metallic species. Scanning transmission electron microscopy (STEM) reveals that a unique amorphous like interlayer (AL-IL) effectively accommodates
lattice mismatch and relaxes epitaxial strain. These AL-IL templates further serve as high performance platforms for metalorganic chemical vapor deposition (MOCVD) overgrowth, successfully bridging the gap between scalable, low-cost fabrication and device-grade vertical performance.
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Applied Physics
Fundamental Efficiency Limits of Transition-Metal Dichalcogenide Solar Cells with Carrier Multiplication and Hot-Carrier Effects
Seungwoo Lee
Detailed-balance limits for transition-metal dichalcogenide (TMD)
solar cells have been reported, but existing TMD-specific limits do not simultaneously resolve thickness-dependent optics, carrier multiplication (CM), hot-carrier (HC) extraction, and finite cooling leakage. Here, we develop a generalized detailed-balance theory that provides an upper-bound framework. The model combines energy- and thickness-dependent absorptance a(E,d),
exciton-resolved monolayer absorbance, an experimentally available CM
quantum-yield limit (eta_CM <= 0.97), and an endoreversible HC engine with ideal energy-selective contacts and finite heat-leak coefficient kappa. The framework shows that CM and HC draw on the same above-gap
photon-energy reservoir; therefore, CM does not raise the reversible HC thermodynamic limit. Instead, CM can protect finite-kappa performance only by shifting excess-energy utilization from a cooling-sensitive voltage channel into collected current. For optically thick TMDs under AM1.5G illumination, the SQ optimum lies near E_g = 1.3 eV, whereas the CM/HC-favored envelope shifts toward E_g = 1.0 eV with reversible efficiencies above 50%. For monolayer TMDs such as WSe2 (E_g = 1.63 eV), CM is essentially inactive because only about 3.7% of above-gap AM1.5G
photons satisfy E > 2E_g, giving an idealized short-
circuit-current gain of only about 0.6% before device nonidealities. Bulk-like TMDs can show large HC-related gains at d = 10-50 nm, but even kappa = 0.2 W m^-2 K^-1 implies about 100 W m^-2 heat leak for Delta T = 500 K. Thus, high-E_g monolayer TMDs are not promising one-sun CM candidates, whereas narrow-E_g, bulk-like TMD absorbers remain plausible beyond-SQ candidates only if energy-selective extraction and
phonon-engineered cooling suppression are realized together.
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Chemical Physics · Cross-listed from cond-mat.soft
Machine learning evaluation of structural descriptors for supercooled water
Kohei Yoshikawa, Kokoro Shikata, Kang Kim et al. (+1 more)
The anomalous behavior of
liquid water is widely associated with a
liquid-
liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these properties, motivating structural descriptors that characterize local molecular environments. These structural descriptors quantify features such as tetrahedral order, local density, and the separation between the first and second coordination shells; however, they have largely been proposed independently, with limited systematic comparison. Here we evaluate 16 previously proposed descriptors using a neural-network-based temperature classification framework, enabling an objective assessment of their ability to distinguish temperature-dependent structural changes in supercooled water. We further apply an explainable artificial intelligence method that identifies the structural features responsible for the model predictions. This approach reveals how different descriptors encode local structural information and establishes a data-driven framework for benchmarking structural descriptors in
liquid water.
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All authors Kohei Yoshikawa, Kokoro Shikata, Kang Kim, Nobuyuki Matubayasi
History and Philosophy of Physics · Cross-listed from quant-ph
Bell Correlations and Selection Bias
Huw Price
Methods of selecting samples from larger populations may produce bias, in either direction: inducing correlations between variables independent in the full population, or masking correlations between variables dependent in the full population. Here we propose a surprising application of these familiar ideas. We argue that they are relevant to puzzling correlations uncovered in
quantum theory by John Stewart Bell (Bell 1964). In the light of Bell's work and subsequent experiments it is widely believed that the
quantum world is 'nonlocal', in apparent tension with relativity. Many hold that the only alternative is to abandon 'realism', the view that there is an objective world independent of measurement. We propose instead that Bell's correlations are selection artefacts, in tension neither with relativity nor realism. In standard two-particle Bell experiments the relevant selection is a preselection, achieved by the preparation of the initial state. Again, selection bias via preselection is familiar elsewhere in science, but it doesn't seem to have been noticed that it is applicable in this case.
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