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Physics Physics

physics Watch Physics

Friday 20 March

Data Analysis and Statistics · Cross-listed from cs.LG

From Data to Laws: Neural Discovery of Conservation Laws Without False Positives

Rahul D Ray

Conservation laws are fundamental to understanding dynamical systems, but discovering them from data remains challenging due to parameter variation, non-polynomial invariants, local minima, and false positives on chaotic systems. We introduce NGCG, a neural-symbolic pipeline that decouples dynamics learning from invariant discovery and systematically addresses these challenges. A multi-restart variance minimiser learns a near-constant latent representation; system-specific symbolic extraction (polynomial Lasso, log-basis Lasso, explicit PDE candidates, and PySR) yields closed-form expressions; a strict constancy gate and diversity filter eliminate spurious laws. On a benchmark of nine diverse systems including Hamiltonian and dissipative ODEs, chaos, and PDEs, NGCG achieves consistent discovery (DR=1.0, FDR=0.0, F1=1.0) on all four systems with true conservation laws, with constancy two to three orders of magnitude lower than the best baseline. It is the only method that succeeds on the Lotka–Volterra system, and it correctly outputs no law on all five systems without invariants. Extensive experiments demonstrate robustness to noise ($\sigma = 0.1$), sample efficiency (50–100 trajectories), insensitivity to hyperparameters, and runtime under one minute per system. A Pareto analysis shows that the method provides a range of candidate expressions, allowing users to trade complexity for constancy. NGCG achieves strong performance relative to prior methods for data-driven conservation-law discovery, combining high accuracy with interpretability.
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Atmospheric and Oceanic Physics

Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia

Nurjanna Joko Trilaksono, Sandy Hardian Susanto Herho, I Putu Ferry Wistika et al. (+3 more)

This study evaluates the effectiveness of three-dimensional variational (3D-Var) data assimilation coupled with a Rapid Update Cycle (RUC) framework for improving short-range precipitation forecasts over the Indonesian Maritime Continent (IMC). We employ the Weather Research and Forecasting (WRF) model and its data assimilation component (WRFDA) to assimilate surface observations from Automatic Weather Stations (AWS) at cycling intervals of 1, 3, 6, and 12 hours. Our test case is a heavy rainfall event on 7 July 2023 in Bali Province, during which accumulated precipitation exceeded 193 mm.day$^{-1}$. The 1-hour cycling interval yields the lowest root-mean-square error (RMSE) for both 2-meter temperature (0.0-0.3$\,^\circ$C) and hourly precipitation (1.295 mm.h$^{-1}$), corresponding to reductions of roughly 75% and 57%, respectively, relative to non-assimilated forecasts. Frequent cycling constrains initial-condition errors and captures mesoscale convective evolution, as confirmed by improved spatial agreement with radar reflectivity observations. These results demonstrate that high-frequency assimilation cycling offers clear advantages for nowcasting in tropical maritime environments.
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All authors Nurjanna Joko Trilaksono, Sandy Hardian Susanto Herho, I Putu Ferry Wistika, Faiz Rohman Fajary, Rusmawan Suwarman, Dasapta Erwin Irawan

Physics and Society · Cross-listed from astro-ph.IM

Astrophysics Research Organizations in the 21st Century: Database and Comparative Dashboards

Michael J. Kurtz, Carlolyn S. Grant, Matthew R. Templeton et al. (+1 more)

As many research papers in astronomy have been written since the beginning of the 21st century as had been written previously. This exponential growth has been accompanied by substantial changes in the structure of astrophysics research, which organizations perform it and where they are located. Using data from the Smithsonian/NASA Astrophysics Data System/Science Explorer (ADS/SciX) we have obtained an article number and citation based set of metrics as a function of the institutional affiliation of the first author; nearly every organization which has produced recent astronomy research is included. We use these data to examine changes in where astronomy research is being done. We demonstrate how to create custom rankings for the organizations. We develop a dashboard of key performance indicators (KPI) to examine the relative and absolute changes in the research performance for each of the 1949 organizations which have produced at least one first authored, refereed astronomy journal article since 1997. We also present KPI dashboards for 65 countries and three regions.
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All authors Michael J. Kurtz, Carlolyn S. Grant, Matthew R. Templeton, The ADS/SciX Team

Chemical Physics

More converged, less accurate? Reassessing standard choices for ab initio water using machine learning potentials

Hubert Beck and Ondrej Marsalek

Accurately simulating the properties of liquid water remains a central challenge in molecular simulations. In this work, we use machine learning potentials to investigate how the convergence settings of electronic structure calculations impact the predicted structural and dynamical properties of simulated water and ice. We evaluate the true performance of several reference methods in classical and path-integral molecular dynamics. When we compare a popular, computationally pragmatic revPBE0-D3 setup against a highly converged one, our results reveal that its widely reported experimental agreement degrades. Applying the same highly converged settings to the $\mathrm{\omega}$B97X-rV functional, we find an improved agreement with experimental results. MP2 with a triple-$\zeta$ basis set commonly used for liquid water shows poor performance, which is indicative of insufficient convergence. These findings underscore the need for fully converged reference calculations when evaluating the fundamental accuracy of electronic structure methods and developing reliable models for aqueous systems.
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Atomic Physics · Cross-listed from cond-mat.quant-gas

Interference of a chain of Bose condensates in the Pitaevskii-Gross approximation

I. N. Mosaki and A. V. Turlapov

A long chain of Bose condensates freely expands and interferes after being released from an optical lattice. The interference fringes are well resolved both in the case of equal phases of the condensates and in the case of fluctuating phases. In the second case the positions of the fringes also fluctuate. The spectrum of the spatial density distribution, however, is reproducible despite the fluctuations. Moreover two types of peaks are distinguishable in the spectrum. The first type arises due to the phase fluctuations, the second type is associated with the coherence between the condensates. In the framework of the Pitaevskii-Gross equation we calculate the interference of the condensates and compare the calculation with experiment [Phys. Rev. Lett. 122, 090403 (2019)]. The calculation reproduces the positions of the spectrum peaks, including the dependence on the interparticle interaction. The calculated heights of the peaks, however, in some cases differ with the experimental ones.
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Atomic Physics · Cross-listed from cond-mat.quant-gas

Order in the interference of a long chain of Bose condensates with unrestricted phases

Vasiliy Makhalov and Andrey Turlapov

For a long periodic chain of Bose condensates prepared in the free space, the subsequent evolution and interference dramatically depend on the difference between the phases of the adjacent and more distant condensates. If the phases are equal, the initial periodic density distribution reappears at later times, which is known as the Talbot effect. For randomly-related phases, we have found that a spatial order also appears in the interference, while the evolution of the fringes differs with the Talbot effect qualitatively. Even a small phase disorder is sufficient for qualitatively altering the interference, though maybe at long evolution times. This effect may be used for measuring the amount of coherence between adjacent condensates and the correlation length along the chain.
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Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

From the Stochastic Embedding Sufficiency Theorem to a Superspace Diffusion Framework

Carolina Garcia, Lucía Perea Durán, Agnese Venezia et al. (+1 more)

A generalisation of Takens' delay-coordinate embedding theorem to stochastic systems, the Stochastic Embedding Sufficiency Theorem, is an inverse methodology enabling non-parametric recovery of both drift and diffusion fields from scalar time series without prior assumptions about the governing physics. A blind protocol using only time series data is applied to nine domains: classical mechanics, statistical mechanics, nuclear physics, quantum mechanics, chemical kinetics, electromagnetism, relativistic quantum mechanics, quantum harmonic oscillator dynamics, and quantum electrodynamics. Fundamental constants (the Boltzmann constant, the Planck constant, the speed of light, the Fano factor, and the Van Kampen scaling exponent) emerge in both drift and diffusion channels without prior specification. The recovered diffusion coefficients, viewed across domains, constitute an empirical pattern, the $\sigma$-continuum, in which $k_B$, $\hbar$, and $c$ play structurally distinct roles. The Gravitational Diffusion Theorem, derived from the fluctuation-dissipation theorem, massless mode structure of linearised gravity, and gravitational self-coupling via the equivalence principle, determines the gravitational diffusion coefficient as one Planck length per square root of Planck time. Four canonical axioms formalise the framework, within which the noise character, drift, covariance operator, and fluctuation amplitude are uniquely determined by theorem, yielding the superspace diffusion hypothesis: $\mathrm{d}g_{ij} = \mathcal{D}_{ij}[g]\,\mathrm{d}\tau + \ell_P\,\mathrm{d}W_{ij}$ where all coefficients are non-parametric, first-principles consequences of the axioms. An implication of the hypothesis is that coarse-graining of the superspace Fokker-Planck equation via Mori-Zwanzig projection yields predictions for galactic-scale gravitational acceleration testable against kinematic data.
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All authors Carolina Garcia, Lucía Perea Durán, Agnese Venezia, Alex Conradie

Chemical Physics

Analytic Gradients and Geometry Optimization for Orbital-Optimized Pair Coupled Cluster Doubles

Saman Behjou, Iulia Emilia Brumboiu and Katharina Boguslawski

We introduce a reusable geometry-optimization engine in PyBEST for analytic, gradient-driven molecular structure optimization, with particular emphasis on orbital-optimized pair coupled-cluster doubles (OOpCCD/AP1roG). The engine interfaces PyBEST with the geomeTRIC optimizer, combining analytic electronic-structure gradients from PyBEST with the translation-rotation-internal coordinate (TRIC) framework, step control, and convergence machinery provided by geomeTRIC. Specifically, we present the first implementation of analytic OOpCCD nuclear gradients within a Lagrangian formalism. Our approach and implementation are generally applicable to any seniority-zero wavefunctions that feature orbital optimization and allow for the evaluation of response one- and two-particle reduced density matrices. Owing to the seniority-zero structure of pCCD and the orbital stationarity of the optimized reference, the resulting gradient equations are compact, minimizing the storage of the full two-particle reduced density matrix, and avoiding finite-difference differentiation of wavefunction parameters. Validation on representative closed-shell systems shows that the OOpCCD-based PyBEST-geomeTRIC workflow converges robustly and reproduces reference equilibrium geometries and energies within tight tolerances. Most importantly, OOpCCD produces structural parameters that deviate by approximately 0.02 Å (0.01 Å) for bond lengths or less than 1$^\circ$ for bond angles from CCSD(F12c)(T*) (MP2) reference structures.
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Physics Education

Exploring the potential of ChatGPT for feedback and evaluation in experimental physics

Marcos Abreu, Álvaro Suárez, Cecilia Stari et al. (+1 more)

This study explores how generative artificial intelligence, specifically ChatGPT, can assist in the evaluation of laboratory reports in Experimental Physics. Two interaction modalities were implemented: an automated API-based evaluation and a customized ChatGPT configuration designed to emulate instructor feedback. The analysis focused on two complementary dimensions-formal and structural integrity, and technical accuracy and conceptual depth. Findings indicate that ChatGPT provides consistent feedback on organization, clarity, and adherence to scientific conventions, while its evaluation of technical reasoning and interpretation of experimental data remains less reliable. Each modality exhibited distinctive limitations, particularly in processing graphical and mathematical information. The study contributes to understanding how the use of AI in evaluating laboratory reports can inform feedback practices in experimental physics, highlighting the importance of teacher supervision to ensure the validity of physical reasoning and the accurate interpretation of experimental results.
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All authors Marcos Abreu, Álvaro Suárez, Cecilia Stari, Arturo C. Marti

Optics · Cross-listed from cond-mat.mes-hall

Non-Hermitian Disordered Systems

Kohei Kawabata and Shinsei Ryu

Non-Hermitian disordered systems have emerged as a central arena in modern physics, with ramifications spanning condensed matter, quantum, statistical, and high energy contexts. The same principles also underlie phenomena beyond physics, such as network science, complex systems, and biophysics, where dissipation, nonreciprocity, and stochasticity are ubiquitous. Here, we review the physics and mathematics of non-Hermitian disordered systems, with particular emphasis on non-Hermitian random matrix theory. We begin by presenting the 38-fold symmetry classification of non-Hermitian systems, contrasting it with the 10-fold way for Hermitian systems. After introducing the classic Ginibre ensembles of non-Hermitian random matrices, we survey various diagnostics for complex-spectral statistics and distinct universality classes realized by symmetry. As a key application to physics, we discuss how non-Hermitian random matrix theory characterizes chaos and integrability in open quantum systems. We then turn to the criticality due to the interplay of disorder and non-Hermiticity, including Anderson transitions in the Hatano-Nelson model and its higher-dimensional extensions. We also discuss the effective field theory description of non-Hermitian disordered systems in terms of nonlinear sigma models.
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Chemical Physics · Cross-listed from cond-mat.mtrl-sci

A chemical language model for reticular materials design

Dhruv Menon, Vivek Singh, Xu Chen et al. (+7 more)

Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven linker design and iterative experimentation. As a result, researchers explore only a small fraction of the vast chemical space accessible to reticular materials, limiting the systematic discovery of frameworks with targeted properties. Here, we introduce Nexerra-R1, a building-block chemical language model that enables inverse design in reticular chemistry through the targeted generation of organic linkers. Rather than generating complete frameworks directly, Nexerra-R1 operates at the level of molecular building blocks, preserving the modular logic that underpins reticular synthesis. The model supports both unconstrained generation of low-connectivity linkers and scaffold-constrained design of symmetric multidentate motifs compatible with predefined nodes and topologies. We further combine linker generation with flow-guided distributional targeting to steer the generative process toward application-relevant objectives while maintaining chemical validity and assembly feasibility. The generated linkers are subsequently assembled into three-dimensional frameworks and are structurally optimized to produce candidate materials compatible with experimental synthesis. Using Nexerra-R1, we validate this strategy by rediscovering known MOFs and by proposing the experimental synthesis of a previously unreported framework, CU-525, generated entirely in silico. Together, these results establish a general inverse-design paradigm for reticular materials in which controllable chemical language modelling enables the direct translation from computational design to synthesizable frameworks.
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All authors Dhruv Menon, Vivek Singh, Xu Chen, Mohammad Reza Alizadeh Kiapi, Ivan Zyuzin, Hamish W. Macleod, Nakul Rampal, William Shepard, Omar M. Yaghi, David Fairen-Jimenez

Instrumentation and Detectors · Cross-listed from astro-ph.IM

Advanced Virgo Plus for O5 – Design Report Overview

F. Acernese, A. Agapito, D. Agarwal et al. (+578 more)

This document presents an overview of the design, implementation, and expected performance of the Advanced Virgo Plus (AdV+) upgrades in view of the O5 observing run. Following the experience gained during the O4 commissioning and operations, the Virgo Collaboration has revised the upgrade strategy to address limitations associated with marginally stable recycling cavities. The O5 upgrade program combines elements from the original AdV+ Phase II project with new design solutions, including the implementation of stable recycling cavities, a major modification to the central interferometer layout, and a comprehensive renewal of critical subsystems. The planned upgrades are organized in two steps, targeting progressive improvements in operational stability, noise reduction, and detector sensitivity. Key developments include new vacuum infrastructures, suspensions, mirrors, optical configurations, quantum noise reduction systems, and high-power laser technologies. The resulting configuration is expected to significantly enhance the interferometer performance, enabling a substantial increase in astrophysical reach and scientific return during O5.
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All authors F. Acernese, A. Agapito, D. Agarwal, I.-L. Ahrend, L. Aiello, A. Ain, S. Albanesi, W. Ali, C. Alléné, A. Allocca, W. Amar, A. Amato, F. Amicucci, C. Amra, M. Andia, T. Andrić, S. Ansoldi, S. Antier, E. Z. Appavuravther, M. Arca Sedda, F. Arciprete, F. Armato, N. Arnaud, L. Asprea, M. Assiduo, S. Assis de Souza Melo, P. Astone, F. Attadio, F. Aubin, G. Avallone, S. Babak, S. Bagnasco, T. Baka, G. Balbi, G. Baldi, N. Baldicchi, G. Ballardin, B. Banerjee, M. Baratti, P. Barneo, F. Barone, M. Barsuglia, D. Barta, A. Basti, M. Bawaj, M. Bazzan, F. Beirnaert, M. Bejger, D. Belardinelli, C. Bellani, L. Bellizzi, D. Beltran-Martinez, I. Bentara, S. Bera, S. Bernuzzi, D. Bersanetti, T. Bertheas, A. Bertolini, G. Bevilacqua, V. Biancalana, A. Bianchi, F. Bianchi, A. Binetti, S. Bini, S. Biot, M. Bitossi, M.-A. Bizouard, G. Boileau, M. Boldrini, A. Bolliand, R. Bondarescu, F. Bondu, R. Bonnand, N. Borghi, V. Boschi, Y. Bothra, A. Boudon, A. Bozzi, C. Bradaschia, M. Branchesi, T. Briant, A. Brillet, M. L. Brozzetti, G. Bruno, F. Bucci, O. Bulashenko, T. Bulik, H. J. Bulten, R. Buscicchio, D. Buskulic, C. Buy, R. Cabrita, G. Cagnoli, E. Calloni, M. Canepa, G. Caneva Santoro, E. Capocasa, G. Capoccia, G. Capurri, G. Carapella, F. Carbognani, M. Carpinelli, G. Carullo, A. Casallas-Lagos, J. Casanueva Diaz, C. Casentini, R. Cavalieri, G. Cella, S. Cepic, P. Cerdá-Durán, E. Cesarini, W. Chaibi, E. Chassande-Mottin, S. Chaty, P. Chessa, F. Chiadini, A. Chincarini, M. L. Chiofalo, A. Chiummo, N. Christensen, G. Ciani, M. Cieślar, P. Ciecielag, M. Cifaldi, S. Clesse, F. Cleva, E. Coccia, E. Codazzo, P.-F. Cohadon, S. Colace, A. Colombo, L. Conti, I. Cordero-Carrión, S. Corezzi, L. A. Corubolo, A. Cozzumbo, J. R. Cudell, E. Cuoco, M. Cusinato, B. D'Angelo, S. D'Antonio, L. D'Onofrio, D. D'Urso, G. Dálya, S. Dall'Osso, T. Dal Canton, S. Dal Pra, S. Danilishin, V. Dattilo, A. Daumas, M. Davier, P. Davis, J. Degallaix, C. J. Delgado Mendez, S. Della Torre, W. Del Pozzo, A. Demagny, G. Demasi, A. Depasse, J. De Bolle, M. De Laurentis, F. De Lillo, F. De Marco, F. De Matteis, R. De Pietri, R. De Rosa, C. De Rossi, R. De Simone, C. Diaz, D. Diksha, J. Ding, M. Di Cesare, L. Di Fiore, M. Di Giovanni, T. Di Girolamo, S. Di Pace, I. Di Palma, D. Di Piero, F. Di Renzo, A. Domiciano De Souza, T. Dooney, O. Dorosh, M. Drago, M. Dubois, U. Dupletsa, H. Duval, P.-A. Duverne, M. Ebersold, H. Einsle, L. Errico, M. Esposito, F. Fabrizi, V. Fafone, M. Fays, E. Fenyvesi, T. Fernandes, S. Ferraiuolo, F. Fidecaro, P. Figura, A. Fiori, I. Fiori, R. Fittipaldi, V. Fiumara, R. Flaminio, J. A. Font, K. Franceschetti, F. Frappez, S. Frasca, F. Frasconi, A. Freise, O. Freitas, B. Gadre, S. Galaudage, B. Garaventa, J. García-Bellido, P. García Abia, J. Gargiulo, X. Garrido, F. Garufi, C. Gasbarra, F. Gautier, G. Gemme, A. Gennai, V. Gennari, Archisman Ghosh, S. Gkaitatzis, F. Glotin, S. Gomez Lopez, A. Goodwin-Jones, M. Gosselin, C. Gostiaux, R. Gouaty, A. Grado, M. Granata, V. Granata, G. Greco, A. C. Green, G. Grignani, C. Grimaud, D. Guerra, D. Guetta, G. M. Guidi, F. Gulminelli, Y. Guo, N. Gutierrez, M. Haney, S. Harikumar, T. Harmark, J. Harms, M. T. Hartman, B. Haskell, D. Hegde, H. Heitmann, G. Hemming, J. Heynen, S. Hild, D. Hofman, N. A. Holland, L. Honet, C. A. Hrishikesh, W.-F. Hsu, V. Hui, L. Iampieri, G. A. Iandolo, M. Ianni, G. Iannone, A. Ierardi, G. Iorio, P. Iosif, C. Jacquet, P.-E. Jacquet, T. Jacquot, J. Janquart, S. Jaraba, P. Jaranowski, G. Joubert, V. Juste, W. Kiendrebeogo, G. Koekoek, S. Koley, A. E. Koloniari, A. Koushik, A. Królak, E. Kraja, S. L. Kranzhoff, J. Kubisz, A. Lakhal, M. Lalleman, A. Lartaux-Vollard, L. Lavezzi, C. Lazarte, C. Lazzaro, P. Leaci, A. Lemaître, M. Lenti, M. Leonardi, M. Lequime, N. Leroy, N. Letendre, M. Lethuillier, S. Lexmond, M. Le Jean, T. G. F. Li, J.-P. Locquet, A. Longo, D. Lopez, M. Lopez Portilla, M. Lorenzini, V. Loriette, G. Losurdo, L. Lucchesi, D. Lumaca, L. Lunghini, A. Macquet, S. S. Madekar, S. Maenaut, R. Maggiore, M. Magnozzi, E. Majorana, N. Man, M. Mancarella, V. Mangano, M. Mantovani, M. Mapelli, S. Marchetti, C. Marinelli, F. Marion, S. Marsat, F. Martelli, M. Martinez, V. Martinez, A. Martini, L. Massaro, A. Masserot, S. Mastrogiovanni, G. Mastropasqua, T. Matcovich, L. Maurin, Q. Meijer, L. Mereni, G. Merino, M. Merzougui, B. Mestichelli, A. Miani, C. Michel, A. L. Miller, E. Milotti, V. Milotti, Y. Minenkov, Ll. M. Mir, L. Mirasola, C.-A. Miritescu, A. L. Mitchell, L. Mobilia, M. Montani, A. Moreso Serra, G. Morras, A. Moscatello, B. Mours, C. M. Mow-Lowry, L. Muccillo, F. Muciaccia, D. Nabari, S. Nadji, A. Nagar, D. Nanadoumgar-Lacroze, V. Napolano, I. Nardecchia, H. Narola, L. Naticchioni, L. Negri, A. Nemmani, S. Nissanke, F. Nocera, J. Novak, J. F. Nuño Siles, M. Oertel, G. Oganesyan, M. Orselli, A. Ouzriat, C. Périgois, L. Paiella, M. A. Palaia, P. P. Palma, C. Palomba, P. Palud, P. T. H. Pang, F. Pannarale, M. Panzeri, F. Paoletti, A. Paolone, L. Papalini, G. Papigkiotis, A. Paquis, A. Parisi, D. Pascucci, A. Pasqualetti, R. Passaquieti, D. Passuello, B. Patricelli, A. Perego, G. Perna, A. Perreca, J. Perret, S. Perriès, J. W. Perry, S. Peters, C. Petrillo, M. Piarulli, L. Piccari, M. Pichot, M. Piendibene, F. Piergiovanni, L. Pierini, G. Pierra, V. Pierro, M. Pietrzak, M. Pillas, L. Pinard, I. M. Pinto, M. Pinto, A. Placidi, E. Placidi, W. Plastino, R. Poggiani, E. Polini, J. Pomper, E. Porcelli, E. K. Porter, R. Poulton, M. Pracchia, T. Pradier, G. Principe, G. A. Prodi, P. Prosperi, P. Prosposito, P. Puppo, G. Quéméner, I. Rainho, A. Ramos-Buades, P. Rapagnani, M. Razzano, T. Regimbau, A. I. Renzini, B. Revenu, A. Revilla Peña, L. Ricca, F. Ricci, M. Ricci, A. Ricciardone, S. Rinaldi, F. Robinet, A. Rocchi, L. Rolland, R. Romano, A. Romero-Rodríguez, L. Rosa, D. Rosińska, S. Roy, D. Rozza, P. Ruggi, E. Ruiz Morales, P. Saffarieh, T. Sainrat, S. Sajith Menon, O. S. Salafia, L. Salconi, F. Salemi, M. Sallé, S. Salvador, A. Samajdar, N. Sanchis-Gual, F. Santoliquido, F. Sarandrea, A. Sasli, P. Sassi, B. Sassolas, S. Sayah, V. Scacco, S. Schmidt, K. Schouteden, M. Schulz, M. Scialpi, M. Seglar-Arroyo, J. W. Seo, V. Sequino, M. Serra, A. Sevrin, N. S. Shcheblanov, L. Silenzi, L. Silvestri, V. Sipala, L. Smith, S. Soares de Albuquerque Filho, V. Sordini, F. Sorrentino, F. Spada, V. Spagnuolo, P. Spinicelli, D. A. Steer, J. Steinlechner, S. Steinlechner, N. Stergioulas, P. Stevens, M. Suchenek, S. Sudhagar, J. Suresh, A. Svizzeretto, B. L. Swinkels, A. Syx, M. J. Szczepańczyk, P. Szewczyk, M. Tacca, M. Tagliazucchi, I. Takimoto Schmiegelow, N. Tamanini, E. N. Tapia San Martín, C. Taranto, A. Theodoropoulos, J. Tissino, P. Tiwari, E. Tofani, M. Toffano, A. Torres-Forné, I. Tosta e Melo, E. Tournefier, M. Trad Nery, A. Trapananti, R. Travaglini, F. Travasso, M. C. Tringali, G. Troian, A. Trovato, L. Trozzo, K. Turbang, M. Turconi, C. Turski, H. Ubach, M. Vacatello, M. Valentini, E. Vallejo-Pagès, S. Vallero, M. van Dael, E. Van den Bossche, J. F. J. van den Brand, C. Van Den Broeck, M. van der Kolk, M. van der Sluys, A. Van de Walle, J. van Dongen, H. van Haevermaet, J. V. van Heijningen, P. Van Hove, N. van Remortel, M. Vardaro, G. Vedovato, S. Venikoudis, P. Verdier, M. Vereecken, D. Verkindt, F. Vetrano, A. Veutro, A. Viceré, N. Villanueva Espinosa, J.-Y. Vinet, S. Viret, H. Vocca, M. Was, M. Wils, J. Woehler, I. C. F. Wong, T. Wouters, M. Wright, Z. Wu, N. Yadav, T. Zelenova, J.-P. Zendri, M. Zeoli, M. Zerrad, Y. Zhao, L. Zimmermann

Chemical Physics

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

Nitesh Kumar, Jianwei Lai, Casey S. Mezerkor et al. (+5 more)

Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000 times faster than DFT. While previous MLIP training datasets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) dataset provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pre-trained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation-solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion-correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations.
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All authors Nitesh Kumar, Jianwei Lai, Casey S. Mezerkor, Jiaqi Wang, Kamila M. Wiaderek, J. David Bazak, Samuel M. Blau, Ethan J. Crumlin

Data Analysis and Statistics · Cross-listed from cond-mat.stat-mech

Detecting the 3D Ising model phase transition with a ground-state-trained autoencoder

Ahmed Abuali, David A. Clarke, Morten Hjorth-Jensen et al. (+3 more)

We develop a one-class, deep-learning framework to detect the phase transition and recover critical behavior of the 3D Ising model. A 3D convolutional neural network autoencoder (CAE) is trained on ground-state configurations only, without prior knowledge of the critical temperature, the Hamiltonian, or the order parameter. After training, the model is applied to Monte Carlo configurations across a wide temperature range and different lattice sizes. The mean-square reconstruction error is shown to be sensitive to the transition. Finite-size scaling of the peak location for the reconstruction error susceptibility yields the critical temperature $T_c=4.5128(58)$ and the correlation-length critical exponent $\nu=0.63(27)$, consistent with results from the literature. Our results show that a one-class CAE, trained on zero-temperature configurations only, can recover nontrivial critical behavior of the 3D Ising model.
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All authors Ahmed Abuali, David A. Clarke, Morten Hjorth-Jensen, Ioannis Konstantinidis, Claudia Ratti, Jianyi Yang

Optics

Low-complexity neural network equalization for long-haul coherent transmission with cascaded semiconductor optical amplifiers

S. Bogdanov, S. Sygletos, O. Sidelnikov et al. (+3 more)

In this letter, we numerically investigate a long-haul coherent data transmission system with a cascade of semiconductor optical amplifiers (SOAs). We exploit low-complexity neural networks that can be implemented in real time to compensate for the accumulated distortions induced by a cascade of SOAs. This equalization provides an order-of-magnitude reduction in bit error rate at low dispersion (in the O-band), whereas higher dispersion degrades performance.
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All authors S. Bogdanov, S. Sygletos, O. Sidelnikov, G. Gomes, M. Kamalian-Kopae, S. K. Turitsyn

Plasma Physics

Control of the bootstrap current in approximately quasi-axisymmetric magnetic fields

J.L. Velasco, I. Calvo and J. M. García-Regaña

Quasi-axisymmetric stellarators are the stellarator analogue of axisymmetric tokamaks, retaining many of their favorable confinement properties, their compactness and their relative coil simplicity, while avoiding their principal limitation, the need for an inductively driven plasma current. Despite these attractive physics properties, the development of quasi-axisymmetric configurations has been severely constrained by the absence of an experimentally validated divertor concept compatible with their large bootstrap current. In this Letter, approximately quasi-axisymmetric fields, complemented with piecewise omnigenous perturbations, are proposed as the basis for a new strategy towards a stellarator reactor that potentially achieves, simultaneously, simple coil geometries, tokamak-like confinement and, through tailoring of the bootstrap current, compatibility with an island divertor. Implications for attaining a high bootstrap current fraction in tokamak devices are also discussed.
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Computational Physics

Deep learning-based phase-field modelling of brittle fracture in anisotropic media

N. Plungė, P. Brommer, R. S. Edwards et al. (+1 more)

This work presents a variational physics-informed deep learning framework for phase-field modelling of brittle crack propagation in anisotropic media. Previous Deep Ritz Method (DRM) approaches have focused on second-order, isotropic phase-field fracture formulations. In contrast, the present work introduces, for the first time within a variational deep learning setting, a family of higher-order anisotropic phase-field models through a generalised crack density functional. The resulting fracture problem is solved by minimising the total energy using the DRM. The trial space is enriched with higher-order B-spline basis functions to represent higher-order gradients accurately and stably, thereby eliminating the need for conventional automatic differentiation. The methodology is assessed for isotropic, cubic, and orthotropic fracture surface energy densities. Numerical examples demonstrate direction-dependent crack growth in anisotropic cases, highlighting the capability of the method to accurately capture this behaviour.
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All authors N. Plungė, P. Brommer, R. S. Edwards, E. G. Kakouris

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

Micromagnetic Modeling of Surface Acoustic Wave Driven Dynamics: Interplay of Strain, Magnetorotation, and Magnetic Anisotropy

Florian Millo, Pauline Rovillain, Massimiliano Marangolo et al. (+1 more)

We study the coupling mechanism of surface acoustic waves (SAW) with spin waves (SW) using micromagnetic analysis. The SAW magnetoacoustic excitation field is fully implemented, i.e., all strain and lattice-rotation terms are included. A realistic CoFeB film with a weak in-plane uniaxial anisotropy is considered. We investigate the conditions for efficient SAW–SW coupling, with particular emphasis on the case where the SAW propagates parallel to the external magnetic field, a configuration of special interest for magnonic applications. Remarkably, we find that the anisotropy orientation serves as a knob to tune the parallel resonant interaction. Overall, this work provides a unified and practical picture of SAW–SW coupling in thin magnetized films.
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All authors Florian Millo, Pauline Rovillain, Massimiliano Marangolo, Daniel Stoeffler

Instrumentation and Detectors

New method for clustering thresholds determination in Microstrip Silicon Detector

S. Mazzolani, I. Mattei and L. Servoli

The Microstrip Silicon Detector (MSD) is one of the subsystems of the FragmentatiOn Of Target (FOOT) experiment whose goal is to measure double differential nuclear fragmentation cross sections for applications in particle therapy and radioprotection in space. The MSD, composed of six 150 $\mu$m-thick silicon sensors arranged in three X-Y measuring planes, is part of the FOOT experiment tracking region. In this work, we propose a new method to set the MSD thresholds for the clustering algorithm independently of the other detectors of the experiment. The obtained values can be used as reference values from which to perform a threshold scan in order to evaluate the single-ion detection efficiency of each MSD sensor and to orient the clustering analysis using tracking information.}
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Data Analysis and Statistics

VecAmpFit: vectorized amplitude-analysis fitting library

K. Chilikin

A new library VecAmpFit for multidimensional amplitude analyses in high-energy physics has been developed for an ongoing amplitude analysis at Belle II experiment. It includes a fitter performing likelihood calculation and explicitly-vectorized subprograms for amplitude implementation. The fitter supports explicit gradient calculation and simultaneous fitting of multiple data sets.
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Computational Physics

Time-delay estimation using the Wigner-Ville distribution

L. de A. Gurgel, J. M. de Araújo, L. D. Machado et al. (+1 more)

Accurately calculating time delays between signals is pivotal in many modern physics applications. One approach to estimating these delays is computing the cross-spectrum in the time-frequency domain. Linear time-frequency representations, such as the continuous wavelet transform (CWT), are widely used to construct these cross-spectra. However, it is well known that the frequency resolution is inherently limited by the localized nature of the convolving wavelet. Moreover, the functional form of the CWT cross-spectrum is not a proper correlation measure and typically requires post-processing smoothing. Conversely, quadratic representations achieve joint time-frequency resolution approaching the Gabor-Heisenberg limit while also providing an adequate measure of similarity between the signals. Motivated by these advantages, we propose a time-delay estimation method based on the Wigner-Ville Distribution (WVD). Considering nonstationary signals arising from two typical wave-physics scenarios, we show that the WVD yields more accurate time-delay estimates with lower uncertainty, particularly in the most energetic frequency bands.
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All authors L. de A. Gurgel, J. M. de Araújo, L. D. Machado, P. D. S. de Lima

Chemical Physics

Occupancy Extrapolation: Reaching Many Excited Electronic States from Ground State Calculations

Yichen Fan and Weitao Yang

The $\Delta$SCF DFT approach defines the system energy as a function of orbital occupancy. Inspired by Landau Fermi liquid theory, we develop an occupancy extrapolation (OE) method that captures excited-state energies via a Taylor expansion of the energy with respect to occupation fluctuation from a reference state. OE retains the physics of $\Delta$SCF while offering a physical interpretation of excitation energies as sums of quasiparticle energies and their generalized screened interactions. It yields accurate valence, Rydberg, and charge-transfer excitation energies at $O(N^3)$ cost, avoids separate SCF calculations for each excited state, and enables efficient large-scale excited-state simulations from ground-state calculations.
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Atmospheric and Oceanic Physics

Uncertainty in wind and solar projections depends on global and regional climate models

Nina Effenberger and Reto Knutti

Ensembles of regional-global climate model combinations show substantial spread in projected wind and solar resources. Using 31 RCM-GCM pairs, we quantify the sources of this spread with a spatially and seasonally resolved variance decomposition, separating contributions from RCMs and GCMs. For both wind speed and solar radiation, RCMs dominate the variability in the absolute historical fields. In contrast, projected changes in wind speed are largely controlled by the driving GCMs, except in mountainous regions where RCM-induced variance becomes larger than that induced by GCMs. For solar radiation, contributions are strongly season-dependent, with RCMs dominating in summer and GCMs in winter. Our findings support that GCM and RCM variability together define the uncertainty of wind and solar climate projections. This provides guidance for designing climate model ensembles that better support uncertainty-aware energy system decisions under climate change.
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Instrumentation and Detectors

Design, construction, and initial operation of a 30-ton Water-based Liquid scintillator detector at Brookhaven National Laboratory

S. Andrade, A. Baldoni, D.F. Cowen et al. (+25 more)

Water-based Liquid Scintillator (WbLS) was proposed over a decade ago as a novel detector medium that might allow the separation and tuning of the relative ratio of the Cherenkov and scintillation signals. A detector employing this technology could support large-scale neutrino detection over both the GeV and MeV energy regimes, while its metal-loading capability could provide an effective means of neutron tagging. WbLS is attractive for two reasons. It can be deployed in very large detectors. It also allows in-situ tuning of the scintillator concentration, and hence the ratio of Cherenkov to scintillation light. Neither pure water Cherenkov detectors nor conventional liquid scintillator detectors offer this capability. At Brookhaven National Laboratory (BNL), two prototypes have been built for understanding WbLS properties and stability, with masses of 1-ton and 30-ton, respectively. We present here the 30-ton prototype detector design, installation, and initial operation including the real-time observation of a transient optical response during the staged scintillator injection. Results from the analysis of data collected in the two detectors will follow in separate publications.
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All authors S. Andrade, A. Baldoni, D.F. Cowen, R. Diaz Prerez, M.V. Diwan, S. Gokhale, S. Gwon, S. Hans, P. Hackspacher, J. Jerome, G. Lawley, G.D. Orebi Gann, P. Kumar, J. Park, C. Reyes, R. Rosero, N. Seberg, K. Siyeon, M. Smiley, R. Svoboda, N. Speece-Moyer, M. Vagins, B. Walsh, J.J. Wang, M. Wilking, G. Yang, D. Wooley, M. Yeh

Computational Physics

Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians

Yang Zhong, Xiwen Li, Xingao Gong et al. (+1 more)

Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition of the electrostatic energy, deriving a variationally consistent mapping from the electron density matrix to effective atomic charges. We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation. Benchmarks demonstrate that physics-based long-range corrections are essential: purely data-driven attention mechanisms fail to capture macroscopic electrostatic potentials. Benchmarks on polar ZnO slabs, CdSe/ZnS heterostructures, and GaN/AlN superlattices show two- to threefold error reductions and robust transferability to systems far beyond training sizes, eliminating the characteristic staircase artifacts that plague short-range models in the presence of built-in electric fields.
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All authors Yang Zhong, Xiwen Li, Xingao Gong, Hongjun Xiang

Optics

Plasmonics of non-noble metals

Michal Horák, Michael Foltýn, Viktor Bajo et al. (+2 more)

Localized surface plasmon resonances are self-sustained, collective oscillations of free electrons in metallic nanostructures. They have a wide range of applications. The most common plasmonic metals are noble metals, such as gold and silver. However, there are applications, such as surface-enhanced Raman spectroscopy, in which using non-noble metals is advantageous. This review summarizes the investigation of localized surface plasmons in non-noble metal nanoparticles, providing an overview of the plasmonic properties of non-noble metals. We cover the following metals: aluminium (Al), antimony (Sb), bismuth (Bi), chromium (Cr), copper (Cu), gallium (Ga), indium (In), lead (Pb), magnesium (Mg), molybdenum (Mo), nickel (Ni), potassium (K), selenium (Se), sodium (Na), tellurium (Te), tin (Sn), titanium (Ti), tungsten (W), and zinc (Zn). Our summary therefore compares the plasmonic properties of non-noble metals and briefly introduces their potential to the readers.
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All authors Michal Horák, Michael Foltýn, Viktor Bajo, Petr Dub, Tomáš Šikola

Fluid Dynamics

Cavitation by phase shift of focused shock waves inside a droplet

Samuele Fiorini, Guillaume T. Bokman, Anunay Prasanna et al. (+6 more)

Localized cavitation in liquids and soft tissues, typically initiated by the rarefaction phase of high-amplitude ultrasound waves, is leveraged in several biomedical applications such as ablation techniques and drug delivery with vaporizing agents. However, safety considerations aimed at avoiding unwanted bubble activity outside the targeted region pose a limit to the maximum allowed peak rarefaction pressure, which on the other hand can hinder the therapeutic efficacy of these techniques. This study shows that a purely compressive shock wave can generate localized, negative pressure and initiate cavitation inside a sub-millimetric perfluorohexane droplet, without requiring any externally applied rarefaction wave. The Gouy phase shift is identified as the physical mechanism responsible for the conversion of positive pressure into tension during shock focusing, and its occurrence is demonstrated through numerical simulations and direct experimental measurements. Comparison of the regions affected by cavitation, visualized in-situ by means of high-speed x-ray phase-contrast imaging, with prediction from Classical Nucleation Theory suggests homogeneous nucleation as the underlying mechanism behind bubble formation. The presented findings offer valuable insights into the physics of shock wave propagation which can inspire the development of novel acoustic driving strategies for cavitation generation, facilitating the reduction of negative pressures outside the target region and improving the safety and precision of biomedical treatments.
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All authors Samuele Fiorini, Guillaume T. Bokman, Anunay Prasanna, Stefanos Nikolaou, Sayaka Ichihara, Bratislav Lukić, Alexander Rack, Yoshiyuki Tagawa, Outi Supponen

Atmospheric and Oceanic Physics

High-resolution vertical wind and turbulence measurements with quadcopter uncrewed aerial systems: wind tunnel calibration and field validation

Johannes Kistner, Julian Jüchter and Norman Wildmann

The SWUF-3D drone fleet is used in the atmospheric boundary layer (ABL) for in situ turbulence measurements of complex flows, such as in mountainous terrain or wind turbine wakes. Previous calibrations for measuring vertical wind speed $w$ using the drones' avionics data were performed on field data, limiting applicability to low winds ($\leq 8~\mathrm{m}\,\mathrm{s}^{-1}$) and being prone to high uncertainties. To overcome these limitations, we calibrate $w$ measurement in a wind tunnel and validate it in field measurements. Calibration is performed in a wind tunnel with an active grid used to deflect horizontal flow into the vertical. This wind is measured with a multi hole probe, while wind forces acting on the drone are determined from the avionics data, allowing an empirical relationship between these quantities. For validation, we conduct comparative fleet measurements with up to 10 drones simultaneously around an array of meteorological masts equipped with sonic anemometers. The results show high accuracy for turbulence statistics: the variance determination for $w$ has a root mean square error (RMSE) of 0.12~$\mathrm{m^2\,s^{-2}}$ and a normalized RMSE (nRMSE) of 17.0~%, for the horizontal wind components the RMSEs are $\sim$0.3~$\mathrm{m^2\,s^{-2}}$ and nRMSEs $\sim$25~%. The RMSEs for the covariances of the components are $<\,$0.3~$\mathrm{m^2\,s^{-2}}$. The variance spectra of $w$ measured with drones and reference sensors agree in all frequency ranges, the RMSE for covariances between different measurement points is $\sim$0.1~$\mathrm{m^2\,s^{-2}}$. Accurate $w$ retrieval at all wind speeds sustainable by the drone enables studies of strongly three-dimensional flows, supports eddy-covariance flux estimation, enables resolving diurnal turbulence evolution in the ABL, and improves spatial turbulence characterization.
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Space Physics · Cross-listed from astro-ph.SR

Connection-angle dependence of proton anisotropy in ground-level enhancement events

Alessandro Bruno and Silvia Dalla

Ground Level Enhancements (GLEs) probe the earliest, highest-energy solar energetic particles and thus provide a unique window onto particle release and transport from the low corona to 1 AU. We present a uniform, event-resolved analysis of the early anisotropy for ten well-observed GLEs, combining consistently reconstructed neutron-monitor pitch-angle distributions (PADs) with Parker-spiral footpoint mapping. We find a clear, monotonic decline of initial anisotropy with increasing magnetic connection angle: well-connected events exhibit strong, persistent forward-directed beams, while poorly connected events show systematically weaker and more rapidly decaying anisotropies. This relationship holds across a wide range of flare classes and CME speeds, demonstrating that magnetic connectivity and interplanetary transport, rather than eruption magnitude, dominate the directional properties of the earliest relativistic arrivals at Earth. A principal component analysis was applied to time-resolved spectral and angular parameters to separate source-driven changes from transport effects. By explicitly identifying and removing secondary sunward (back-scattered) components-attributable to scattering and reflection from solar-wind structures and transient interplanetary features-from the PAD fits, we isolate the intrinsic relaxation of the primary forward beam and show that apparent departures from simple exponential decay are frequently attributable to reflected or delayed populations rather than prolonged source injection. The empirical anisotropy–connection-angle relation reported here provides an event-resolved, quantitative benchmark that constrains focused-transport and shock-acceleration models and offers immediate operational value: rapid footpoint estimates can meaningfully limit expected initial beaming and directional radiation risk.
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Chemical Physics

Coupled cluster theory for positron binding in anions and polyatomic molecules

Rosario R. Riso, Jan Haakon M. Trabski, Federico Rossi et al. (+2 more)

We present the positron coupled cluster singles and doubles (POS-CCSD) method to calculate positron binding energies in molecules. This framework treats electrons and positrons on an equal footing and includes up to simultaneous double-electron-single-positron excitations. We benchmark the approach by computing binding energies for atomic anions and several polar and non-polar polyatomic systems, comparing the results with independent theoretical studies and, where available, experimental data. The fully converged results for H$^{-}$ are in excellent agreement with quantum Monte Carlo and multi-reference configuration interaction results. Quantitative agreement with experiments is not reached in the present study due to the slow convergence of the binding energy with respect to the size of the orbital bases for the electrons and the positron. However, the POS-CCSD results underscore the critical role of electron correlation in the description of electron-positron systems required for a balanced description of these complex systems. In addition, we examine nuclear relaxation effects following positron attachment in LiH.
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All authors Rosario R. Riso, Jan Haakon M. Trabski, Federico Rossi, Dermot Green, Henrik Koch