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Physics Physics

physics Watch Physics

Wednesday 26 August

Chemical Physics

Benchmark of Multi-Channel Dyson Equation and Algebraic Diagrammatic Construction Methods for molecules

Mike Keizer, Stefano Paggi, J. Arjan Berger et al. (+2 more)

The Dyson-algebraic diagrammatic construction (ADC) and the multi-channel Dyson equation (MCDE) formalisms explicitly leverage multi-particle channels to formulate correlated theories of the single-particle Green's function that produce positive semi-definite spectral functions by construction. While the MCDE is strictly rooted in the Dyson formalism, most ADC calculations are performed in the non-Dyson (nD) framework that decouples electron attachment and detachment sectors. We benchmark the Dyson-ADC(2)-X [that is equivalent to the (3,1)-MCDE] and ADC(3) approximations on a set of 58 ionization potentials of 23 small molecules for which near-full configuration interaction reference data exist. Comparison of Dyson- to nD-ADC(3) reveals deviations of the order of 0.1 eV between both methods, calling into question the reliability of the nD approximation. We show that Dyson-ADC gives similar accuracy for first IPs as for semi-valence and semi-core transitions. Finally, we also benchmark the screened (3,1)-MCDE that screens all ladder interactions, and show that it improves over its unscreened counterpart.
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All authors Mike Keizer, Stefano Paggi, J. Arjan Berger, Pina Romaniello, Arno Förster

Geophysics

GeoFormer: Geometry-Aware Transformer and its application to 5D First-Arrival Picking

Tianxiang Gao and Jianwei Ma

We propose GeoFormer, a Geometry-Aware Transformer architecture specifically designed for prestack seismic data. Unlike Vision Transformer, whose tokens are extracted from 2D patches and primarily encode visual patterns, GeoFormer is designed for prestack seismic data by explicitly incorporating acquisition geometry. Each seismic trace is represented by a 5D unit consisting of the waveform and four source-receiver coordinates, from which two geometric attributes are derived: offset and relative elevation, where relative elevation is the receiver elevation minus the source elevation. GeoFormer therefore performs trace-level tokenization, where each token combines the waveform with these geometric attributes. To exploit these geometric attributes, GeoFormer introduces three geometry injection mechanisms operating at different levels of the Transformer pipeline. At the token level, GeomMLP replaces the classification token with a per-trace geometric representation derived from offset and elevation. At the normalization level, GeomAdaLN replaces uniform layer normalization with geometry-conditioned feature modulation. At the attention level, GeomAttnBias injects a parameter-free physical prior that geometrically proximate traces should attend more to each other. We validate GeoFormer on first-arrival picking, a representative seismic processing task that relies heavily on acquisition geometry. Experiments on four field datasets demonstrate that GeoFormer outperforms Vision Transformer and two task-specific baselines. Crucially, GeoFormer maintains robust picking accuracy under strong noise because its trace-level tokens encode geometric attributes that provide a physical prior independent of waveform quality. Ablation studies confirm that GeomMLP, GeomAdaLN, and GeomAttnBias each contribute to GeoFormer's picking accuracy.
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Computational Physics

A unified gas-kinetic wave-particle method for multiscale gas-mixture flow with an elementary chemical reaction

Junzhe Cao, Yufeng Wei, Wenpei Long et al. (+2 more)

Hypersonic flows in the near space often couple continuum-rarefied multiscale effect with finite-rate chemistry. This paper extends the UGKWP method to multiscale gas mixture flows with a single elementary reaction. In the UGKWP method, hydrodynamic waves are employed to describe near-equilibrium distribution functions, and numerical particles are used for the evolution of nonequilibrium ones. The adaptive conversion between waves and particles, guided by the characteristic integral solution, together with the introduction of dt into the flux as an observation scale, has enabled the UGKWP method to succeed in many multiscale problems involving complex physics. In this work, rather than relying on a comprehensive reactive kinetic model for the entire distribution function, chemical source terms are first evaluated at the macroscopic level and then incorporated into the wave-particle update, while free-transport particles are kept chemically inactive in the monatomic setting considered here. This approach leverages the modeling advantages of wave-particle decoupling, facilitating extension to more complex chemical reactions. Moreover, an approximate extension of an advanced multispecies kinetic model is developed in this work for multispecies effect with species number larger than two. The present UGKWP method is assessed for the Zeldovich-type reaction O2+N=NO+O through hypersonic cylinder flows over a wide Knudsen number range, covering chemically inert, forward exothermic, forward endothermic and dE=0 conditions, and through shock structures with hot upstream/downstream equilibrium states. Agreement with DSMC is obtained for gas mixture flow fields, species mole fractions and wall quantities. A three-dimensional side jet flow over a blunt cone is further simulated to demonstrate the three-dimensional capability of the present code.
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All authors Junzhe Cao, Yufeng Wei, Wenpei Long, Chengwen Zhong, Kun Xu

Biological Physics · Cross-listed from quant-ph

Quantum motility-induced phase separation

Laurin Brunner, Ricard Alert, Reyhaneh Khasseh et al. (+1 more)

Motility-induced phase separation (MIPS) describes a central clustering phenomenon in active matter systems where particles spontaneously separate into dense and dilute phases even in the absence of interparticle attractive forces. Recent theoretical and experimental efforts have taken the first steps to extend active matter concepts to the quantum level. However, whether a genuine quantum analog of MIPS exists and how quantum coherence would compete or cooperate with the dissipative self-propulsion has remained open so far. Here, we provide evidence that quantum MIPS can occur in a model of active hard-core bosons in one dimension. Our model yields superlinear number fluctuations characteristic of MIPS, leading to microphase separation with large but finite cluster size. Adding nearest-neighbor repulsive interactions, we find numerical evidence for restoring genuine phase separation with a divergent correlation length. Crucially, the clustered steady states maintain a long-distance quantum coherence, revealing a genuinely quantum feature with no classical counterpart. These results provide a foundation for exploring quantum MIPS and suggest that the coherence-activity interplay can generate new types of nonequilibrium quantum states.
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All authors Laurin Brunner, Ricard Alert, Reyhaneh Khasseh, Markus Heyl

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

Nuclearity of Copper Clusters on hBN/SiC Heterostructure Modulates Molecular Adsorption

Reza Khakpour, Arsalan Hashemi, Xiaoya Chang et al. (+3 more)

Defect engineering can transform inert two-dimensional (2D) materials into chemically active and electronically tunable platforms by creating anchoring sites for metal atoms and clusters. Nevertheless, precise control over the formation, thermodynamic and kinetic stability, electronic structure, and chemical reactivity of metal species confined at these defect sites remains a challenge. Here, we use density functional theory (DFT) calculations assisted by machine-learning molecular dynamics (MLMD) simulations to elucidate the stability, electronic structure, and reactivity of Cu clusters anchored at boron vacancies (VB) in hBN/SiC heterostructures. Systematic variation of the Cu-to-vacancy ratio reveals a transition from isolated Cu atoms to multiatom Cu clusters at VB sites, with cluster growth reshaping the stability, electronic structure, and surface reactivity. Our results show that a single VB defect can be passivated by three Cu atoms, which compensate the local charge deficiency and stabilize the defect through Cu-N coordination. Capturing further Cu introduces localized midgap states that could influence the reactivity of the Cu-decorated defect sites. We probe the response of the Cu-decorated surface to chemically relevant gases CO, H2, O2, N2, H2S, and CO2, revealing implications for surface reactivity and stability. The calculations show pronounced cluster-size-dependent reactivity of Cu clusters at VB sites, with CO forming strong Cu-C bonds and O2 undergoing enhanced adsorption and molecular activation. Overall, this work identifies defect-engineered hBN/SiC as a versatile 2D platform for stabilizing Cu clusters and tuning gas-surface reactivity. By correlating Cu nuclearity at VB sites with electronic structure, molecular activation, and environmental robustness, our findings provide design guidelines for nuclearity-dependent metal functionalization of 2D heterostructures.
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All authors Reza Khakpour, Arsalan Hashemi, Xiaoya Chang, Nima Ghafari Cherati, Mikko Karttunen, Tapio Ala-Nissila

Computational Physics · Cross-listed from q-bio.QM

Propensity Straight-Through Gradients for Discrete Stochastic Systems

Jose M. G. Vilar and Leonor Saiz

We introduce the propensity straight-through (PST) estimator for gradient-based learning through exact simulations of continuous-time Markov chains (CTMCs). These models describe discrete stochastic dynamics across physics, chemistry, and biology, but their categorical event choices prevent ordinary differentiation. PST preserves exact forward trajectories and differentiates, in the backward pass, the normalized reaction propensities the simulator already computes. The affine state update makes this rule exact for the one-step conditional-mean sensitivity. We derive the per-step discrepancy for one-step sensitivities composed along nonlinear trajectories in closed form, show that it vanishes for affine downstream dependence, and bound it quadratically in the stoichiometric jump size. Across reversible dimerization, a genetic oscillator, a 50-task repressilator suite, and experimental patch-clamp recordings, PST recovers parameters to within 0.06-1.7% and converges 2.4-3.0-fold faster than the Gumbel-Softmax straight-through estimator at matched computational cost. At deep-learning scale, PST trains all 203,796 parameters of a stochastic reaction network using exact discrete events from initialization. By differentiating an exact conditional mean, PST eliminates reaction-selection temperature tuning and provides a scalable route to learning through exact stochastic dynamics.
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Chemical Physics · Cross-listed from cond-mat.soft

Closed-form hard-sphere thermodynamics under nanoscale confinement: from equations of state to unmixing forces

Jose M. G. Vilar and Leonor Saiz

Equations of state accurately describe hard-sphere thermodynamics but are generally considered to fail under nanoscale confinement. We show that this failure is geometric rather than thermodynamic: a closed-form, parameter-free mapping from apparent to effective packing fraction restores the accuracy of the Carnahan-Starling equation of state and, with it, the entire analytical thermodynamic framework, including free energies, wall pressures, chemical potentials, and the large-cavity surface response. Applied to overlapping anchored hard-sphere droplets, a geometry inherent to biomolecular force generation, it yields unmixing free energies and full distance-dependent force profiles in quantitative agreement with simulations from the dilute regime to above the bulk freezing transition.
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Instrumentation and Detectors

Development and Commissioning of the Cryogenic Target Detectors for the Technical Run of the NUCLEUS Experiment

N. Schermer, H. Abele, G. Angloher et al. (+57 more)

The NUCLEUS experiment aims to study coherent elastic neutrino-nucleus scattering (CE$\nu$NS) of reactor electron antineutrinos at the Chooz nuclear power plant in France. In this work, the cryogenic target-detector module for the NUCLEUS Technical Run was developed and commissioned at the Technical University of Munich. The module comprises four gram-scale CaWO$_4$ detectors, each equipped with two Transition Edge Sensors (TES), providing a total target mass of 6.96 g. We present the design, integration, detector characterization, and X-ray-based energy calibration of these detectors. Across six characterized detectors, a mean baseline resolution of $\overline{\sigma}_{\mathrm{BL}} = (2.79 \pm 0.60)\,\mathrm{eV}$ was achieved. The best-performing detector reached $\sigma_{\mathrm{BL}} = (2.16 \pm 0.02_{\mathrm{stat}})\,\mathrm{eV}$, surpassing the design goal by a factor of two and representing a state-of-the-art result for a cryogenic CaWO$_4$ detector. The target detector module was successfully operated simultaneously with the surrounding Cryogenic Outer Veto, which consists of six kg-scale Ge detectors for background discrimination, showing no measurable cross-talk. These results demonstrate the readiness of the target-detector system for the NUCLEUS Technical Run at Chooz and mark a key milestone in the development of cryogenic detectors for reactor-CE$\nu$NS measurements.
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All authors N. Schermer, H. Abele, G. Angloher, B. Arnold, M. Atzori Corona, A. Bento, E. Bossio, F. Buchsteiner, J. Burkhart, F. Cappella, M. Cappelli, N. Casali, R. Cerulli, A. Cruciani, G. Del Castello, S. Dorer, A. Erhart, M. Friedl, S. Fichtinger, V.M. Ghete, M. Giammei, J. Hakenmüller, D. Hauff, M. Hock, F. Jeanneau, E. Jericha, M. Kaznacheeva, H. Kluck, A. Langenkämper, T. Lasserre, D. Lhuillier, M. Mancuso, B. Mauri, A. Mazzolari, L. McCallin, H. Neyrial, C. Nones, L. Oberauer, L. Peters, F. Petricca, W. Potzel, F. Pröbst, F. Reindl, M. Romagnoni, J. Rothe, J. Schieck, S. Schönert, C. Schwertner, L. Scola, L. Stodolsky, A. Schröder, R. Strauss, R. Thalmeier, C. Tomei, L. Valla, M. Vignati, M. Vivier, A. Wallach, P. Wasser, L. Wienke

Physics and Society

Giant strongly biconnected components of directed networks: a generating function approach

Minsoo Yang, Reinhard Laubenbacher and Byungjoon Min

Strongly connected components (SCCs) characterize modular structure in directed networks but are fragile to single node failures. We study strongly biconnected components (SBCs), which are the set of nodes in which every node pair remains mutually reachable after the removal of any single node, as a more robust notion of connectivity. Using a generating function formalism, we derive the size of the giant SBC and analyze its percolation behavior under random node and link removal. We show that the giant SBC emerges at the same threshold as the giant SCC but grows more slowly due to stricter connectivity requirements. We also applied our theoretical framework to real-world biological networks including gene regulatory networks and neural connectome. Our framework provides insight into the interplay between connectivity, redundancy, and robustness in complex directed systems.
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Physics and Society

$(k,n)$-core percolation on hypergraphs with anchor nodes

Hoseung Jang, Byungjoon Min and Ginestra Bianconi

Hypergraphs describe higher-order interactions that involve more than a pair of nodes. A characteristic feature of hypergraphs is that their robustness can be strongly affected by the different roles of the nodes. Indeed, some nodes might be essential for a hyperedge's function, while others might not be. The loss of a single essential node completely destroys the hyperedge it belongs to, while the loss of a non-essential node has a buffering effect, inducing the hyperedge to simply reduce its size. In order to capture this phenomenology, we formulate a comprehensive theoretical framework for $(k,n)$-core percolation models on hypergraphs, where each node of a hyperedge is an anchor with probability $\theta$, and a hyperedge fails if an anchor node fails. Hypergraph $(k,n)$-core percolation problems can be classified as first-neighbor and second-neighbor problems, indicating that in the pruning process the connectivity is ensured only by the state of the first neighbors or the second neighbors, respectively. We derive self-consistency equations for first-neighbor and second-neighbor (node- and hyperedge-based) pruning processes, and obtain the size of the giant $(k,n)$-core. We obtain the phase diagram, including continuous and discontinuous transitions, and confirm our theory on random hypergraphs using numerical simulations. The results show how the heterogeneity of the nodes' functional roles and the extended range of the interactions affect the robustness of higher-order networks.
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Atomic Physics

Robust watt-level continuous-wave deep-ultraviolet lasers near 230 nm

J. Cai, M. Stoepper, P. Agarwal et al. (+20 more)

Continuous-wave (CW) deep-ultraviolet (DUV) lasers near 230~nm enable laser cooling of AlF, Cd, and Zn, but second-harmonic generation below 237~nm relies in practice on beta-barium borate (BBO), whose walk-off and UV-induced degradation hinder sustained operation. We demonstrate compact, affordable VECSEL-based systems informed by four years of operating 14 DUV cavities in 12 laser systems across six European laboratories. External LBO cavities produce nearly 4~W at 463~nm with 94% cavity efficiency. We compare spherically and elliptically focused Brewster-cut BBO cavities with a normal-incidence AR-coated design. The AR-coated cavity delivers the highest power and efficiency, reaching 1.0~W at 51% cavity and 44% external efficiency; the spherical Brewster cavity reaches 700~mW and maintains constant circulating power over 70~h, while elliptical focusing reduces peak intensity sixfold and improves beam quality, albeit with greater alignment sensitivity. Collaboration-designed DUV optics, AlF spectroscopy, and Cd trapping validate the system.
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All authors J. Cai, M. Stoepper, P. Agarwal, L. Palánki, C. Alarcón-Robledo, W. W. W. Liu, P. Kukreja, R. Ferstl, H. Foltas, L. Möller, D. J. Brown, C. J. H. Rich, M. Chiarotti, T. Uusitalo, R. Thomas, J. Domarkas, M. Guina, J.-P. Penttinen, S. Stellmer, N. Poli, S. C. Wright, S. Hannig, S. Truppe

Plasma Physics

Ion-acoustic eigenmodes in a helical magnetic mirror

Ivan Chernoshtanov

Plasma rotation together with corrugation of magnetic field can result in coupling acoustic waves with different azimuthal wavenumbers and formation of eigenmodes with discrete frequency spectrum and zero longitudinal group velocity in a helical magnetic mirror. Such eigenmodes can be destabilized by plasma rotation as well as resonant interaction with trapped ions. Equations describing these waves in the linear approximation are derived and results of numerical solving the equations are discussed. Probably these eigenmodes may drive anomalous ions scattering which is needed for effective suppressing flow of rarefied plasma through a helical mirror.
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Fluid Dynamics

An SPH–mesh Coupling for Vesicle Dynamics in Shear Flow

Kuiliang Wang, Xinwei Cai, Ting Ye et al. (+2 more)

We present a novel computational framework that couples smoothed particle hydrodynamics~(SPH) with a triangulated membrane mesh to simulate the dynamics of vesicles suspended in fluids. A novel interface-tracking approach enforces membrane impermeability naturally, without resorting to non-physical constraints such as particle reflection or bounce-back boundary conditions. The membrane model incorporates four distinct bending energy formulations, namely the minimal model, the spontaneous curvature (SC) model, the bilayer couple (BC) model, and the area difference elasticity (ADE) model, providing a versatile tool for diverse biophysical scenarios. The framework is rigorously validated against equilibrium shapes and tank-treading motion of a vesicle, demonstrating excellent agreement with previous theoretical and numerical studies. A systematic investigation into the effects of each bending model on the vesicle's inclination angle, revolution frequency, and morphology in shear flow reveals key physical insights. Notably, spontaneous curvature has a negligible effect on steady-state orientation but profoundly alters rotational dynamics at low reduced volumes through the emergence of dumbbell-like shapes with deep constrictions. In contrast, the BC and ADE models induce characteristic asymmetric and stomatocyte morphologies. Our results establish the proposed SPH–mesh coupling as an accurate and robust tool for exploring the complex, shape-dependent dynamics of vesicles in fluid flows.
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All authors Kuiliang Wang, Xinwei Cai, Ting Ye, Xuejin Li, Xin Bian

Optics

Generalizing Thermal Transport in High-Contrast Metamaterials through Interfacial Fresnel Reflection

Seung Hyeon Ham, Yu Min Kim, In Hyeok Choi et al. (+1 more)

The rapid growth of generative AI has intensified the need for efficient heat dissipation in large-scale data centers. To control heat flow, thermal metamaterials with layered structures have been widely used, which impart the anisotropic properties of thermal conductivities. However, the conventional effective medium approximation (EMA) often fails to provide accurate predictions in systems with a high thermal conductivity contrast between adjacent layers embedded in a background medium. Here, we generalize the EMA by introducing two corrective coefficients that extend its validity to regimes where the conventional EMA was previously inapplicable, i.e., high-contrast thermal metamaterials with the background medium. Notably, one of these coefficients that we proposed has the same mathematical form as the Fresnel reflection coefficient in optics. This allows us to interpret the "reflection-like" behavior of heat flow as it penetrates adjacent layers with high thermal contrast. Our findings suggest that heat diffusion, traditionally viewed as a purely dissipative process, can be understood intuitively through the framework of ray optics.
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All authors Seung Hyeon Ham, Yu Min Kim, In Hyeok Choi, Jeong Woo Han

Chemical Physics

Battery Recycling: Mechanistic Modelling of LiCoO$_2$ Leaching with Coupled Diffusion-Reaction Kinetics and Film Passivation

Uddipta Sarma and Ganesh Madabattula

We developed a new mechanistic model for lithium and cobalt recovery during acidic-reductive leaching from LiCoO$_2$. The system considers HCl as an acid and H$_2$O$_2$ as a reducing agent. The model tracks conversion of LCO, the particle radius, the acid, H$_2$O$_2$, formation-dissolution dynamics of Co$_3$O$_4$ film, the film thickness, recovery of lithium and cobalt, and moles of O$_2$. We introduced the film passivation effects for reduced recovery in the absence of H$_2$O$_2$. We validated the model against the experimental data of recovered Li and Co reported in literature at three acid concentrations (0.5 M, 1.5 M, and 2.5 M) and four H$_2$O$_2$ concentrations (0%, 0.2%, 0.4%, and 0.6% (v/v)). The model works reasonably well at 0.5 M and 1.5 M HCl for the 0% and 0.6% concentrations of H$_2$O$_2$. At 2.5 M HCl, the model over-predicts the data in the presence of H$_2$O$_2$, while it works well at 0% H$_2$O$_2$. We suggest pathways for further improvements in the model. The comprehensive mechanistic modelling framework for the leaching with a full list of the equations, the parameters, and the variables, reported for the first time, can be extended to other cathode chemistries and acid-reductive leaching systems.
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Medical Physics · Cross-listed from cs.CV

PAGS: Autofocusing Photoacoustic Tomography via Speed-of-Sound-Adaptive Gaussian Splatting

Jiarui Ge, Jintao Ma, Bangxu Fan et al. (+4 more)

Photoacoustic computed tomography (PACT) combines optical absorption contrast with acoustic detection for high-resolution deep-tissue imaging. A persistent challenge is that unknown speed-of-sound (SoS) heterogeneity changes acoustic time-of-flight, causing defocusing artifacts when reconstruction assumes a uniform SoS. Existing SoS-adaptive methods either rely on calibrated acoustic priors or optimize dense physical medium models, which becomes expensive and difficult to scale in 3D. We propose PAGS, a differentiable framework for blind autofocusing PACT via speed-of-sound-adaptive Gaussian splatting. PAGS represents the initial pressure field with sparse Gaussian photoacoustic (PA) sources and replaces explicit medium recovery with a compact anisotropic path-averaged SoS (ASoS) field parameterized by spherical harmonic probes. This latent propagation field directly controls source-to-transducer arrival-time alignment, while an analytic Gaussian acoustic projection maps the source representation to transducer signals efficiently. The resulting closed-loop signal-domain optimization jointly updates the Gaussian PA source parameters and the ASoS field from measured data, without calibrated SoS priors. Experiments on simulated and physical phantom data demonstrate improved reconstruction sharpness under heterogeneous acoustic media, robustness to sparse-view sampling, and computational benefits from the analytic Gaussian projection.
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All authors Jiarui Ge, Jintao Ma, Bangxu Fan, Jinyan Zhang, Xiaokang Yang, Shuai Na, Xiaoyun Yuan

Fluid Dynamics

Physics-Guided Generative Surrogates for Parametric Rarefied Flows with Neural-Field Auto-Decoders: A Pipeline-Level Study of Flow Matching and Diffusion

Yiming Qi, Guan Zhang, Xu Wang et al. (+2 more)

We present a conditional latent generative framework for parametric rarefied flows that separates neural-field representation, latent transport, and frozen physics adaptation. Neural-field auto-decoders compress discrete-velocity cavity solutions and direct simulation Monte Carlo cylinder solutions into shared coordinate decoders. Train-only principal-component charts support conditional flow matching (FM) and diffusion without a deterministic condition-to-latent backbone, and structured low-rank adapters correct selected decoder outputs while the upstream pipeline remains frozen. On two steady benchmarks, the frozen pipelines interpolate out-of-sample conditions with cavity kinetic relative $L_1$ errors at the $10^{-5}$ level and cylinder per-field area-weighted RMSEs of 0.038 (density), 0.041 (temperature), and below 0.01 (velocities). For the cavity, physics adaptation reduces the matched-grid Bhatnagar–Gross–Krook diagnostic by 28.65% while preserving field accuracy; for the cylinder, the analytic wall map enforces no-penetration exactly and, jointly with the learned FM adapter, reduces the inlet violation to 0.277 and the global mass-balance ratio to 0.963 of the frozen values with negligible field-error change. A five-seed controlled comparison with deterministic condition-to-chart multilayer perceptrons shows that, although the generative pipelines do not surpass the compact MLP in point accuracy on these single-valued steady problems, the results validate sampling-based conditional transport on the shared representation as an effective steady surrogate, with a natural route to multivalued or stochastic solution families.
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All authors Yiming Qi, Guan Zhang, Xu Wang, Yonghao Zhang, Tianbai Xiao

Computational Physics

Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials

Youri Blom, Sofia Dutto, Alexandru Costache et al. (+7 more)

To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions strongly affect both performance and optimal system design, a dedicated PV-specific climate classification can be of great use. In this work, we develop a climate classification framework tailored to PV applications using a variety of machine learning (ML) techniques. Building on previous studies, our approach incorporates both energy yield, and for the first time, also the module lifetime with climate dependent degradation. We generate an interpolated dataset containing twelve input features and two target variables (i.e. energy yield and module lifetime). Feature importance analysis shows that annual global horizontal irradiation and ambient temperature are the most influential predictors. The most accurate regression model achieves root mean square errors (RMSE) of 0.007 MWh for energy yield and 1.5 years for lifetime prediction. The calculated feature importance scores are then integrated into a hierarchical clustering framework, resulting in 6 primary climate clusters (Tropical, Desert, Continental, Temperate, Boreal, and Polar) and 15 corresponding subclusters. Our analysis shows that the low temperature continental climate offers the highest discounted lifetime energy yield. These results can support a wide range of applications, including PV module optimization, system siting decisions, and comparative performance studies.
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All authors Youri Blom, Sofia Dutto, Alexandru Costache, Rowan Richie, Ruben Pelsser, Wesley Berger, Jing Sun, Rudi Santbergen, Olindo Isabella, Malte Ruben Vogt

Plasma Physics

A generalized energy-consistent finite difference scheme for 10-moment magnetohydrodynamics

Keita Akutagawa, Shinsuke Imada and Munehito Shoda

Pressure anisotropy and off-diagonal pressure stresses are ubiquitous and play important roles in collisionless/weakly collisional plasmas. The Chew-Goldberger-Low (CGL) MHD model is often used; however, it can lose hyperbolicity when the pressure anisotropy or plasma beta becomes large, making it hard to develop approximate Riemann solvers. An alternative approach is to use the 10-moment MHD equations, but their eigenmode analysis is also difficult, which similarly hinders the development of less-diffusive Riemann solvers. This paper presents a new energy-consistent finite difference scheme for 10-moment MHD designed to operate over a broad range of plasma beta. The proposed scheme extends the 10-moment MHD model using the energy-consistent finite-difference approach developed for conventional MHD. Nonlinear filtering is applied to all six independent components of the pressure tensor, and the kinetic and magnetic energies dissipated by the filtering are explicitly transferred to the diagonal pressure components under an equipartition assumption to maintain consistency with the total energy balance. The proposed scheme is validated against seven test problems in the isotropic limit, the gyrotropic limit, and without isotropization/gyrotropization. The results demonstrate the expected spatial convergence and total energy behavior, reproduce the linear growth rate, and yield pressure tensor structures qualitatively consistent with theoretical expectations and previous simulations, spanning plasma beta values from $10^{-10}$ to $10^{10}$. The proposed scheme provides a promising framework for large-scale simulations of collisionless plasmas across widely separated plasma beta regimes and opens a path toward applications such as solar wind turbulence and plasmoid-mediated reconnection.
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Optics

The double-indexed geometric phase for electromagnetics

Akhlesh Lakhtakia and Tom G. Mackay

The double-indexed geometric phase (DIGP) based on a family of Poincaré spinors was formulated to extend the Pancharatnam phase, commonly used to describe the evolution of the polarization state in an optical system relative to a reference polarization state. Analytical results establish the symmetries of the DIGP under reversal of the index $p\in\mathbb{R}$ for the latitude on the Poincaré sphere, the phase change induced by reversal of the index $q\in\mathbb{R}$ for the longitude on the same sphere, and the periodic dependence on $q$. For closed loops on the Poincaré sphere, the DIGP can be interpreted geometrically through a $p$-dependent mapping of both geographic coordinates and the associated solid angle subtended at the center of the sphere. Although the geometric phase is usually applied to compare two plane waves propagating in the same direction in free space, the DIGP is expected to contain information when comparing two non-co-propagating plane waves, as demonstrated by specular reflection by a planar thin film and far-zone scattering by a three-dimensional object. DIGP maps for specular reflection and transmission by thin films resolve Bragg phenomenons, Fabry–Pérot resonances, structural chirality, anisotropy, and defects with detail not evident in reflectance and transmittance maps. A map of direction-dependent DIGP for plane-wave scattering by a three-dimensional object may reveal strong polar and azimuthal structure absent from the map of differential scattering efficiency. Principal component analysis indicates that a collection of DIGP maps, all calculated from the same set of polarimetric measurements, form a coordinated data family rather than a collection of independent observables. The DIGP concept offers a complementary framework for polarization-state-resolved characterization and inverse scattering.
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Biological Physics

On the Role of Dispersion in One Model of Propagation of Elastic Excitations in Nerves

Alexander I.Kozlov

The nonlinear differential equation, which describes changes of membrane's density in the process of propagation of active potential along a nerve, has been studied. Depending on values of dispersion terms of that equation, forms and possible existence of exact periodic and solitary solutions were considered.
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Optics

Analog Time-Frequency Analysis and Frequency Measurement of Ultra-Wideband Microwave Signals via Dual-Comb Channelization

Taixia Shi, Wentao Ma, Fangzheng Zhang et al. (+1 more)

Frequency-to-time mapping (FTTM)-based photonics-assisted spectrum sensing enables wideband spectrum monitoring for applications such as cognitive radio and electronic warfare. However, FTTM-based photonics-assisted spectrum sensing approaches involve trade-offs among analysis bandwidth, temporal resolution, and frequency resolution. Although channelization has been introduced to alleviate these trade-offs, the limited number of available channels in previously reported systems constrains further improvements in overall performance. Here, we propose a photonics-assisted dual-comb channelization architecture for broadband frequency measurement and time-frequency analysis. The system employs a stepped-frequency pump based on stimulated Brillouin scattering (SBS) to generate an optical filter comb and adopts a single-laser design for the pump, probe, and reference paths to enhance frequency stability while reducing system complexity. Experimental results show a 48-GHz analysis bandwidth in the 16-channel configuration and 52 GHz in the 20-channel configuration, together with microsecond-scale temporal resolution and a frequency resolution better than 80 MHz. The proposed architecture therefore offers a practical and reconfigurable solution for real-time wideband spectrum sensing.
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All authors Taixia Shi, Wentao Ma, Fangzheng Zhang, Yang Chen

Accelerator Physics

Feasibility demonstration of a high-momentum, high-purity muon beamline at the J-PARC Hadron Experimental Facility

Kotaro Shirotori, Takaya Akaishi, Kazuya Aoki et al. (+16 more)

We have established and characterized a novel high-purity muon beam delivery scheme, designated as $\mu$20, at the high-momentum hadron beamline (B-line) of the J-PARC Hadron Experimental Facility. This scheme delivers tertiary muons produced via the in-flight backward decays of secondary pions in the straight section of the beamline, inherently suppressing parent hadron contamination. In a systematic performance evaluation, the muon beam achieved a standalone intensity of $1.7\times10{^3}$ particles/spill with a spatial spread (standard deviation, $\sigma$) of approximately 15 mm at the downstream detector position. A systematic attenuation analysis utilizing an iron absorber verified a high muon purity of 98.0$\pm$0.6% for the 3 GeV/$c$ muon beam mode. Crucially, this work marks the first successful feasibility study of a high-momentum and high-purity muon beamline operation at an experimental facility in Japan.
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All authors Kotaro Shirotori, Takaya Akaishi, Kazuya Aoki, Wen-Chen Chang, Ryotaro Honda, Yusuke Hori, Takatsugu Ishikawa, Nobuyuki Kobayashi, Ruri Kurasaki, Che-Sheng Lin, Yuhei Morino, Megumi Naruki, Hiroyuki Noumi, Kyoichiro Ozawa, Shinya Sawada, Ken Suzuki, Hitoshi Takahashi, Taiga Toda, Natsuki Tomida

Optics

Nanophotonic control of spatial information in scintillation detectors

Joshua Chen, Simo Pajovic, Seou Choi et al. (+9 more)

X-rays enable non-invasive imaging across medicine, security, materials science, and beyond, yet modern systems remain constrained by the need to resolve finer structures at lower radiation dose. Scintillators are the dominant materials for detecting X-rays, but face a longstanding compromise: thick scintillators absorb X-rays effectively, whereas optical photons generated throughout their volume spread before detection, degrading spatial information. Existing scintillator architectures largely try to preserve resolution by physically confining light using pixels, columnar crystals, or microstructured channels. Here, we show that high-resolution detection does not require the volumetric confinement of scintillation light. A metalens integrated directly with a bulk scintillator uses nanophotonic wavefront control to preferentially transfer high-spatial-frequency information from volumetrically generated scintillation light to the detector, while retaining the X-ray absorption of a thick scintillator. We experimentally recover fine spatial detail in X-ray images of inorganic and biological specimens. In a detector geometry relevant to computed tomography (CT), the experimentally validated model predicts a fivefold reduction in required X-ray dose and a 25-fold increase in resolution bandwidth relative to a state-of-the-art pixelated scintillator. These results establish wavefront engineering as a route to separating efficient X-ray absorption from optical image formation, with the potential for substantially higher-resolution, lower-dose CT.
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All authors Joshua Chen, Simo Pajovic, Seou Choi, Sachin Vaidya, William Michaels, Louis Martin-Monier, Christina M. Spägele, Steven E. Kooi, Juejun Hu, Rajiv Gupta, Charles Roques-Carmes, Marin Soljačić

Optics

Intrinsic Radial Landau Rainbow in Triaxial Strained Photonic Crystal

Guangti Lu and Satoshi Iwamoto

Localized resonances are essential for enhancing light matter interaction, controlling emission, and realizing compact optical resonators and lasers. Landau levels offer a distinct route to organizing photonic states through synthetic magnetic fields, but their high degeneracy does not define spectrally separated localized resonances. Here, we report the intrinsic lifting of degeneracy in photonic Landau levels in a triaxially strained photonic crystal, leading to the emergence of an intrinsic radial Landau rainbow. The triaxial deformation generates photonic Landau levels through a strain induced pseudomagnetic field, while an accompanying pseudoelectric field lifts the degeneracy of the Landau levels. As a result, each Landau level splits into an equally spaced frequency ladder of localized resonances. In zeroth Landau level, these modes exhibit intensity maxima that move progressively outward from the device center, establishing a radial frequency position mapping and Landau rainbow. Our results reveal an intrinsic fine structure of photonic Landau levels in a triaxial strained photonic crystal and provide a route to spatially ordered Landau level resonances.
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Computational Physics · Cross-listed from cond-mat.soft

Dynamical and conformational behavior of a polymer in a crowded solution

Setarehalsadat Changizrezaei and Colin Denniston

We investigate the structure and dynamics of a polymer in a fluid containing mobile spherical colloidal crowders of radius $R$. We compare and contrast the behavior with Langevin dynamics (LD) and lattice–Boltzmann molecular dynamics (LBMD), the latter incorporating long-range hydrodynamic interactions. Both the colloid size relative to the monomer radius $r$ and the volume fraction $\phi$ are varied to determine how crowding modifies polymer behavior. Increasing volume fraction induces polymer compaction, with the mechanism strongly dependent on the size ratio $R/r$. Small colloids primarily modify the short-wavelength polymer conformation, causing self-avoiding-walk-like behavior to persist to shorter length scales, whereas large colloids reduce the effective long-wavelength Flory exponent, indicating degraded solvent quality consistent with a confinement-blob picture. Polymer diffusion exhibits distinct behavior in LD and LBMD. In LD, diffusion decreases rapidly and depends strongly on $R/r$; a phenomenological scaling involving $\ln(1+R/r)$ captures this size dependence, and additional scaling with $R_g$ reduces scatter, indicating polymer-scale correlations induced by crowding. In contrast, LBMD diffusion follows an effective-medium-like exponential dependence on concentration, governed by hydrodynamic coupling. Rouse-mode analysis identifies three regimes: scaling breakdown at low volume fraction, Zimm-like behavior at intermediate density in both LD and LB, and at high density hydrodynamic screening in LB with confinement-dominated dynamics in LD.
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Tuesday 25 August

Geophysics

Higher–order gradient modeling of velocity dispersion in tight sandstones with implications to agroseismology

Rafael Abreu, Ashay Motghare, Chahana Nagesh et al. (+2 more)

Modeling frequency-dependent shear-wave propagation in fluid-saturated porous rocks remains a challenge today because existing poroelastic theories generally rely a large number of constitutive parameters that are difficult to constrain from seismic observations. We propose a dynamically consistent higher-gradient continuum model that reproduces shear-wave dispersion using only three effective material parameters: the macroscopic shear velocity and two characteristic length scales governing higher-order elasticity and gradient inertia. We validate the model against laboratory measurements of glycerin-saturated tight sandstone over a wide range of effective pressures. The inversion reproduces the measured shear-wave dispersion, including positive, weak and negative dispersion regimes. We finally discuss the relationship between the proposed formulation and micropolar theories, the computational challenges and the potential of reduced-order generalized continuum models for seismic exploration, reservoir characterization, and emerging agroseismology applications.
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All authors Rafael Abreu, Ashay Motghare, Chahana Nagesh, Alireza Nadi, Hassam Khan

Physics and Society

When Edge Importance Disagrees: Resistance-Ranked versus Betweenness Pruning on Urban Road Networks

Jianru Shen

Sparsifying large road-network graphs by removing a fraction of their edges is a common way to reduce storage and accelerate spatial queries, but the choice of which edges to remove can quietly degrade the network that remains. We compare three edge-importance criteria for deciding what to prune, namely uniform random removal, low edge-betweenness removal, and low-effective-resistance removal, on 27 urban road networks drawn from OpenStreetMap that span a wide range of sizes and street-network morphologies. At edge-removal ratios from five to thirty percent we evaluate each criterion along three complementary axes: length-weighted routing utility, a topology-only measure given by the retention of the second-smallest eigenvalue of the normalized Laplacian of the largest connected component, and the fraction of an externally defined arterial backbone, taken from OpenStreetMap road classes, that each criterion destroys. The criteria disagree. Low-effective-resistance ranking, which targets electrically redundant edges, preserves connectivity well only below ten percent removal; beyond this level, retention falls below that of random removal in a growing majority of cities, and by thirty percent the method removes a larger share of the arterial backbone than random deletion does. Betweenness removal is consistently the best of the three on all three axes, at every removal ratio. Resistance-based redundancy is therefore not a safe proxy for routing importance when compressing real road networks, and a routing-oriented criterion is the more dependable choice.
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Plasma Physics

Physics-Informed Neural Operator Surrogate for 2D Magnetohydrodynamic Reconnection

Kushaal Kumar Pothula and Arunav Kumar

Direct numerical simulation of magnetic reconnection is limited by the scale separation of resistive magnetohydrodynamics. At large Lundquist numbers $S$ the current layer thins as $S^{-1/2}$, forcing fine grids and short time steps that make parameter scans prohibitively expensive. Deep learning neural operators offer an alternative by learning the map between function spaces rather than individual solutions, so that a single trained model returns the state for any parameter and time at inference cost. We have developed a Fourier Neural Operator (FNO) based Physics-Informed Neural Operator (PINO) surrogate for two-dimensional compressible, viscous, resistive reconnection in a wall bounded domain. We condition on the initial state, the Lundquist number, and a continuous query time. Predicting the magnetic flux function makes $\grad\!\cdot\!\bB=0$ exact, direct time queries remove autoregressive error accumulation, and parity-aware spectral differentiation matches the wall treatment of the reference solver. Trained across $10^{3}\le S\le2\times10^{5}$ with two values withheld, the surrogate recovers density, pressure, and guide field to better than $0.2\%$, velocity to $3.2$ to $3.9\%$, the flux function to $1.0\%$, and the spectral current density to $7.5\%$. It resolves the thin, narrow current sheet, Alfvénic jets, and spectral resolution beyond mode cutoff. Our surrogate reproduces the layer-averaged reconnection rate to within $3\%$ through $S=1.1\times10^{4}$, recovering a reconnection time exponent of $0.497$ consistent with Sweet-Parker scaling. When queried zero-shot on the $2049^{2}$ solver mesh, a full trajectory run is on the order of seconds on one GPU against several hours for the DNS, over two orders of magnitude faster, establishing learned operators as a practical route to surveying reconnection across parameter space.
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Atmospheric and Oceanic Physics

A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway

Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen et al. (+5 more)

Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in highly variable local wind conditions. In this case study, we evaluate FourCastNet3 (FCN3), GraphCast, and ECMWF High Resolution Forecast (HRES) for wind speed forecasting using multi-year station observations from Northern Norway, focusing on their relative performance, generalization beyond the training period, and performance under high-wind conditions. Our results show that HRES slightly outperforms FCN3 and GraphCast, with an overall RMSE of 2.89 $\mathrm{m\,s^{-1}}$, compared to 2.96 $\mathrm{m\,s^{-1}}$ for FCN3 and 2.94 $\mathrm{m\,s^{-1}}$ for GraphCast. Notably, the MLWP models maintain comparable performance beyond their respective training periods, with no clear evidence of noticeable degradation. FCN3 performs best under high-wind conditions, although all models substantially underestimate strong winds. Our findings suggest that MLWP has become competitive with NWP for local wind, but further refinements are still needed to capture complex terrain better.
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All authors Siyan Chen, Lars Uebbing, Eirik Mikal Samuelsen, Georgios Leontidis, Arnt-Børre Salberg, Sébastien Lefèvre, Robert Jenssen, Kristoffer Wickstrøm