Biological Physics
Collective Emission in LH2 Assembly Beyond the Point-Dipole Approximation
Javed Akhtar and Himangshu Prabal Goswami
Collective emission in light-harvesting assemblies is governed by the local transition dipole and finite geometry of emitting units, a fact that point-dipole approximation obscures. To go beyond this picture, we develop a non-Hermitian
Hamiltonian using the
quantum electrodynamic dyadic Green's tensor for a purple bacteria. We construct it for the isolated 24-bacteriochlorophyll conical frustum and its P42$_1$2 crystallographic assembly. The P42$_1$2 unit-
cell symmetry is found to invert the bright-dark ordering of the single ring, placing subradiant states at the low-energy end and revealing the entire
crystal to be the energy-harvesting entity. Tilt-driven switching is activated only in
crystal geometries where the finite dipole-carrier (LH2) lies perpendicular to the growth plane. Vacancy and orientational disorder work only in cooperation to renormalize the switching threshold from higher polar angles to lower values.
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Applied Physics · Cross-listed from eess.SY
Emergent Non-Hermitian Topology in Multi-Robot Network
Jielong Zhang, Guiju Duan, Tinggui Chen et al. (+3 more)
Non-Hermitian (NH)
topology has been extensively explored in wave and matter systems, typically relying on the routing of complex, non-reciprocal couplings in physical space. This work demonstrates the experimental realization of programmable NH topological phases within decentralized multi-
robot networks. By digitally programming non-reciprocal interaction rules and establishing real-time state exchange among active
robots, we observe emergent topological zero modes (TZMs) and NH skin effects in synthetic
lattices spanning one to three dimensions. Dynamically tailoring non-reciprocal parameters enables the precise morphing of TZMs between localized and delocalized states, establishing a versatile framework for topological mode engineering across dimensionalities. This platform establishes multi-
robot networks as highly reconfigurable systems for exploring non-
equilibrium topological physics, while paving the way for topologically protected, robust collective behaviors in active matter.
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All authors Jielong Zhang, Guiju Duan, Tinggui Chen, Shengjie Zheng, Bozheng Xue, Baizhan Xia
Fluid Dynamics
Effect of Acoustics on Droplet Grouping Behaviour in a Single Stream of Droplets
M. Kumar, V. Vaikuntanathan, M. Ibach et al. (+5 more)
Droplet and particle grouping can be influenced by applying an acoustic field and have practical applications such as particle scavenging and aerosol filters of engine exhaust and air purifiers. The present work experimentally investigates the influence of a standing acoustic wave on a single stream of droplets. The experimental setup consists of an acoustic transducer and a reflector plate through which the droplet stream passes in the presence or absence of an external pressure field generated by a standing acoustic wave. A droplet stream is generated with the help of a nozzle connected to a pressurized working fluid supply and piezoelectric transducer to control the spacing between droplets. The effect of the acoustic pressure field on the droplet stream generated by the nozzle operated at different piezoelectric excitation frequencies and fluid pressures is investigated. Droplet stream characteristics at every nozzle excitation frequency are observed with a high-speed camera when the acoustic field is switched OFF and ON. The competing effect of nozzle excitation frequency and acoustic field is observed. At lower nozzle frequencies, the nozzle generates an unstable stream of droplets having different sizes and spacings between them. When the acoustic field is applied at these lower frequencies, the stream of droplets becomes organized, and in some cases, it becomes equispaced and of the same size. However, an opposite behavior is observed at higher frequencies. In these cases, as the acoustic field is applied, an equispaced mono-disperse droplet stream becomes unstable due to the coalescence of droplets within the stream.
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All authors M. Kumar, V. Vaikuntanathan, M. Ibach, A. Arad, R. Bar-On, B. Weigand, D. Katoshevski, J. B. Greenberg
Data Analysis and Statistics · Cross-listed from math-ph
The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport
Peter Halmos and Boris Hanin
We exhibit an exact correspondence between sampling with score-based
diffusion models and adiabatic transport of ground states for a family of Schrödinger operators we call Score
Hamiltonians, built from the learned score's
quantum potential. We obtain novel density reconstruction bounds and principled annealing schedules via adiabatic
theorems for Fokker-Planck equations with time-varying potentials. We find the fundamental limit of sampling is set by the ratio of squared score-matching error to Score
Hamiltonian spectral gap - the inverse Poincaré constant of the data density.
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Fluid Dynamics
Neural-Network-based Viscosity Closure for Non-Newtonian Multiphase Flows
Suresh Murugaiyan, Claire L. Nelson, Dhruv Gamdha et al. (+11 more)
Materials used in
polymer-based additive manufacturing processes, such as Digital Light Processing (DLP) and direct ink writing (DIW), typically exhibit non-Newtonian rheology. Carreau-Yasuda and power-law models describe basic shear-thinning and shear-thickening behavior well, but applying them to a new material requires choosing a functional form, deriving it, and re-implementing it inside the flow solver. We present a deployment workflow in which a neural network trained on experimental rheometry data serves as the viscosity closure inside a Cahn-Hilliard-Navier-Stokes (CHNS) finite element solver. The learned closure is generalized Newtonian: viscosity depends only on the local shear rate at the current instant. Lipschitz regularization during training produces smooth viscosity predictions, and the trained network is exported in the Open Neural Network Exchange (ONNX) format and queried by the solver at runtime via the ONNX runtime, without solver modification or network reimplementation. The framework is built on a parallel octree-based adaptive mesh refinement infrastructure that concentrates resolution at the fluid interface. We validate the CHNS solver against
benchmark shear-thinning bubble-rise cases from the literature, reproducing reported bubble shapes across varying power-law indices and Weber numbers. We characterized two silicone ink formulations, recorded their rise dynamics in perfluorodecalin on high-speed
video, and used the resulting data to test the full workflow. Simulated rise velocities fall within the experimentally measured spread, and the simulated steady-state droplet shape agrees with the observed one. This work contributes to a growing body of literature on integrating neural constitutive closures into multiphysics simulations, and demonstrates a practical path for deploying experimentally trained rheological surrogates inside finite element solvers.
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All authors Suresh Murugaiyan, Claire L. Nelson, Dhruv Gamdha, Austin Cunniff, Cheng-Hau Yang, Abraham Wiletsky, Kaitlyn W. Dilley, Patrick Babb, Andrew Rhode, Christopher M. Bates, Angela A. Pitenis, Michael L. Chabinyc, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Optics
Tailoring Defects in Photonic Time Crystals for Coherent Energy Control
Dayeong Lee, Jongheon Yeo, Gitae Lee et al. (+3 more)
Recent advances in time-varying photonics have revealed new degrees of freedom for manipulating optical states, arising from the distinctive nature of the temporal axis: causality and open-system dynamics. A representative example is photonic time
crystals (PTCs) characterized by discrete time-translational
symmetry, which exhibit space-analogous yet distinct phenomena, such as momentum gaps and amplifying-decaying Floquet-mode pairs. Although PTCs enable optical-energy amplification beyond conventional gain media, their application as programmable energy-functional devices remains challenging. Here, we propose a design framework for tailoring optical energy via defective PTCs. By optimizing defect permittivity and duration using analytic gradients of time transfer
matrices, we realize prescribed coherent energy amplification and suppression. We show that a single defect enables continuous energy tailoring, while revealing an intrinsic asymmetry between amplification and suppression due to the inherently amplifying nature of the momentum gap. Extending the framework to coupled defects expands the design space and markedly improves suppression, establishing temporal-defect engineering as a route to programmable coherent energy control.
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All authors Dayeong Lee, Jongheon Yeo, Gitae Lee, Jungmin Kim, Namkyoo Park, Sunkyu Yu
Space Physics
Advancing Heliophysics and Space Weather Modeling through Open Science
C. Corti, M.M. Kuznetsova, M.A. Reiss et al. (+85 more)
We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potential to enhance the quality and pace of scientific discovery, its application to scientific modeling requires more careful consideration regarding open data and open software guidelines, as scientific models differ significantly from data analysis software. We gathered feedback from modeling teams worldwide through a living survey and discussion sessions at the 2024 Open Science Workshop in College Park, USA, and at the 2025 COSPAR ISWAT Working Meeting in Cape Canaveral, USA. We complement these findings with lessons learned from almost 25 years of experience at the Community Coordinated Modeling Center in enabling open use of models. We identify key roadblocks in current open science practices and guidelines and offer recommendations for future progress across four overlapping themes: open use of models and simulation results, open validation, open development, and open collaboration. An essential outcome of the discussion is the need for model developers and model users to speak with a united voice and promote the role of models in future open science efforts. We introduce a new cross-domain community initiative called the Heliophysics Open Modeling Environment (HOME), which will be integrated as an overarching activity within COSPAR ISWAT. HOME will serve as a platform for modelers and model users to work together, facilitate community modeling, improve the scientific return on modeling investment, and advance understanding, modeling, and
forecasting in heliophysics and space weather.
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All authors C. Corti, M.M. Kuznetsova, M.A. Reiss, J. Yue, J. Karpen, C.N. Arge, F. Bacchini, C. Bard, S. Bruinsma, R.M. Caplan, L.K.S. Daldorff, P.J. Deka, C.R. DeVore, S. Elvidge, N. Ganushkina, J.D. Huba, B.V. Jackson, V. Jordanova, J.A. Linker, H. Liu, J.G. Luhmann, S. Markidis, P. Mayank, V. Merkin, N. Moens, D. Odstrcil, Y.A. Omelchenko, M. Palmroth, S. Poedts, A.J. Ridley, Y. Shou, V. Tenishev, D.R. Themens, G. Toth, W. Wang, R.-P. Wilhelm, M.A. Young, B. Cecconi, M.-Y. Chou, D. De Zeeuw, G.L. Delzanno, C. Didigu, M. El Alaoui, S. Fung, J. Green, Z. Huang, L.K. Jian, L.J. Landwer, M. Lesko, P. MacNeice, A. Masson, M.L. Mays, P.M. Mehta, M.S. Miesch, E. Palmerio, M. Petrenko, E. Provornikova, L. Rastätter, L. Rusaitis, N. Sachdeva, E. Samara, D. Sur, A. Taktakishvili, J. Topper, T. Tsui, C. Verbeke, J. Wang, C. Wiegand, M. Wiltberger, Y. Zheng, M.M. Bisi, M.K. Georgoulis, T. Kodikara, T. Pulkkinen, A. Chartier, D. da Silva, A. Faturahman, K. Garcia-Sage, D. Kondrashov, V.E. Ledvina, W. Liu, C. Pandey, E. Resnick, C. Shi, R.S. Weigel, K. Whitman, I. Zakharenkova, K. Zhang
Physics Education
Constraints From Simulation Improve Experiential Outcomes in Laboratory Environment
Jeff Bale, James Day, J. Ives et al. (+1 more)
In a first-year physics inquiry lab, pairs of students were randomly assigned to study pendulum motion using either a physical apparatus or a computer simulation. The experiment required detecting a ~1% difference in period between pendulums released at 10$^\circ$ and 20$^\circ$. This is the subtle failure of the small angle approximation and a goal that demands iteratively refined, high-precision measurements. Students using the simulation achieved significantly more reproducible timing measurements across all rounds of data collection and, by the third round, had also adopted more effective data collection strategies overall. As a result, 78% of simulation groups met the precision threshold required to identify the model failure, compared with 52% of physical apparatus groups. We attribute these outcomes primarily to a specific simulation constraint: students were required to use the simulation's built-in timer, which forced them to decouple pendulum release from the start of timing. This prevented students from pursuing a reaction-time-limited synchronization strategy that often traps users of physical apparatus in a low-precision measurement dead end. A post-lab survey further shows that students using simulations were more confident in their results than those who instead used a physical pendulum, as well as preferred greater use of simulations in future labs. These findings suggest that carefully designed simulation constraints can guide students toward productive experimental strategies while preserving their investigative autonomy.
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All authors Jeff Bale, James Day, J. Ives, D. A. Bonn
Space Physics
Storm-Time Cusp Precipitation: Insights from TRACERS Multi-Crossing Observations
Shirsh Lata Soni, David M. Miles, Jasper Halekas et al. (+12 more)
The dayside cusp provides a direct pathway for
solar wind
plasma entry into the magnetosphere ionosphere system through
magnetic reconnection. Using low altitude ion and electron measurements from TRACERS, together with upstream
solar wind and geomagnetic conditions, we investigate the
evolution of the cusp during a geomagnetic storm on 30 September 2025, spanning its rising, main, and recovery phases, and compare these with a quiet-time reference. Storm-time observations show broader and more poleward precipitation regions and enhanced electron energy flux, indicating intensified dayside coupling. To interpret these variations, we combine
solar wind and IMF measurements with the maximum
magnetic shear reconnection model to determine X-line locations and use a Tsyganenko field model to compute event-specific field-line transit distances between the X-line and TRACERS. The results demonstrate that cusp morphology and latitude track IMF-driven reconnection geometry, and that realistic path lengths are essential for quantitative reconnection-rate estimates, highlighting the capability of TRACERS to resolve storm-time cusp
evolution. Enhanced cusp precipitation during the recovery phase is consistent with IMF conditions, indicating sustained
solar wind driving rather than intrinsic storm-phase effects.
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All authors Shirsh Lata Soni, David M. Miles, Jasper Halekas, Stephen A. Fuselier, George Hospodarsky, Marit Øieroset, K. J. Trattner, S. M. Petrinec, Jeremy Faden, Ivar Christopher, Chris Pikar, Sarah A. Henderson, Daniel Da Silva, Scott Bounds, Robert Strangeway
Atmospheric and Oceanic Physics
Learning effective Sargassum transport dynamics from limited drifter observations
F.J. Beron-VEra, M.J. Olascoaga, J. Morell et al. (+1 more)
Floating-material transport is influenced by unresolved processes that are often absent from available circulation products. We develop a data-driven transport-learning framework for learning effective transport corrections from limited Lagrangian observations using physically motivated ocean–atmosphere diagnostics and finite-
memory representations motivated in part by inertial-particle
memory effects. The diagnostic representation is analyzed through predictive and sparse symbolic-discovery approaches under leave-one-trajectory-out validation. Applications to Sargassum-following drifters in the Puerto Rico region and the Gulf Stream show that the diagnostics contain transport-relevant information beyond the baseline circulation products. Multilayer perceptron (MLP) ensembles provide flexible predictive trajectory corrections, while Sparse Identification of Nonlinear Dynamics (SINDy) tests whether instantaneous or delayed sparse symbolic transport structure can be extracted from the diagnostics. The results differ across flow regimes: (i) in Puerto Rico, delayed sparse symbolic corrections provide modest but systematic improvement; (ii) in the Gulf Stream application, dynamically useful sparse symbolic corrections remain primarily instantaneous even though delayed predictive information persists. These results support finite-
memory transport effects in coarse-grained floating-material transport while also illustrating the difficulty of obtaining stable delayed sparse symbolic closures.
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All authors F.J. Beron-VEra, M.J. Olascoaga, J. Morell, E. Cruz
Physics Education
A new (old?) coupled pendulum experiment
Neil G. R. Broderick, Benjamin Pollard, Karthik Sivasubramanian et al. (+2 more)
We present a description and analysis of a coupled pendulum experiment that has been used at the University of Auckland for many years. Although similar experiments are very common in undergraduate physics labs, both the physical system and our teaching approach are, we believe, novel. Compared to other coupled pendulum experiments, it has the advantage that the coupling can easily be varied, allowing students to observe the changing nature of the normal modes and beat frequencies. Additionally, the system displays a surprising degree of complexity, making it of interest to more advanced students in laboratory courses that are augmented with computer-based numerical modeling and
algebra software.
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All authors Neil G. R. Broderick, Benjamin Pollard, Karthik Sivasubramanian, Marc Lescano, Chemgjie Chen
Physics and Society
Participation Costs Narrow Democratic Cooperation
Mohammad Salahshour, Fjolle Shabani, Urs Fischbacher et al. (+1 more)
Collective action often requires institutions that make cooperation individually worthwhile. We ask whether democratic allocation of public-good return can transform a repeated public good into a self-sustaining cooperative institution, and how participation costs reshape that process. A simple evolutionary model shows that voted redistribution can support a prosocial allocation order, but can also sustain an antisocial allocation order or democratic free riding, in which individuals benefit from an institution maintained by others while avoiding the cost of participation. The model predicts competing effects of voting cost. Cost can suppress use of the institution to reward low contributors under strong selection, but can also thin the active electorate and erode contributor-rewarding support. We test these predictions in a preregistered online experiment with \NIncludedGroupsVone{} five-person groups. Endogenous democratic redistribution increased contributions relative to an equal-share public-goods control, with zero-cost voting producing the strongest temporal improvement. Voting costs did not mainly turn active voters toward low-contributor-rewarding allocation. Instead, they shifted behavior toward abstention and democratic free riding, made abstention locally rewarding, and widened the gap between post-task perceptions of democratic participation and the behavioral record. Democratic allocation can therefore stabilize cooperation, but participation costs can reduce the number of people actively sustaining the institution and can make that erosion less visible to participants themselves.
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All authors Mohammad Salahshour, Fjolle Shabani, Urs Fischbacher, Iain D. Couzin
Physics and Society
Beyond Direct Retweets: Multi-Step Pathways in Italian COVID-19 Twitter
Edoardo Maggioni, Ren Manfredi, Fabio Saracco et al. (+1 more)
We study how retweet interactions in large-scale Twitter debates are organized beyond direct links alone. Focusing on Twitter debate in Italy during the first phase of the COVID-19 pandemic, we combine a validated community-reconstruction pipeline with a higher-order random-walk framework to examine how short multi-step pathways redistribute
attention across discursive communities. Rather than reconstructing observed cascades of individual tweets, we use motif-based random-walk paths as a structural device to compare direct community-to-community connectivity with the distribution of multi-step endpoints. We find that
attention is initially concentrated within communities, but that this concentration weakens as path length increases. At the same time, the resulting cross-community redistribution is not uniform: some communities become increasingly relevant as endpoints of longer pathways, while others lose relative prominence. These differences are not fully captured by community size or by first-order retweet connectivity alone, and they also display important directional asymmetries when the network is analyzed under the reversed orientation. In summary, the results show that moving beyond direct retweets changes the community-level representation of online debate and reveals higher-order structural patterns that remain invisible in first-order analyses.
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All authors Edoardo Maggioni, Ren Manfredi, Fabio Saracco, Rossana Mastrandrea
Geophysics · Cross-listed from cs.LG
SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data
Joseph Stitt, Pratik Rathore, Madeleine Udell et al. (+1 more)
Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration to earthquake hazard assessment. Machine learning approaches to FWI need field-scale, geologically diverse, and physically realistic training data, but existing resources such as Marmousi, SEAM, and OpenFWI fall short on spatial extent, temporal extent, geological diversity, and physical realism. We address these limitations with SubsurfaceGen, a GPU-accelerated generator for 3D velocity models and seismic data. Along with SubsurfaceGen, we release a paired
dataset of 4,276 2D velocity slices, 5 s wavefields, and 8 s shot gathers drawn from 42 realistic, field-scale 3D velocity models, each spanning 10 km x 10 km laterally and 6.19 km deep at 10 m resolution. The
dataset spans six geological settings – four built with SubsurfaceGen and two drawn from prior sources – relevant for carbon sequestration and hydrocarbon exploration. We use this
dataset to evaluate neural operators on wavefield prediction and encoder-decoders on end-to-end velocity inversion, holding out one geological setting for out-of-distribution testing. These experiments surface failure modes at field-scale and demonstrate how SubsurfaceGen and the associated
dataset can impact ML-based FWI.
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All authors Joseph Stitt, Pratik Rathore, Madeleine Udell, Ching-Yao Lai
Physics and Society
Private Noise and Public Error in Collective Information Acquisition
Mohammad Salahshour, Sumanth Bhargava, Kajal Kumari et al. (+4 more)
Collective information acquisition requires groups to combine personal evidence with social information while remaining coupled to the external state. Communication noise can affect this process, but the role of noise remains unclear. In an online experiment, 600 participants worked in four-person human groups estimating a room temperature across 25 rounds while receiving either faithful social information, comprehension noise in which each receiver saw independently perturbed social information, or production noise in which perturbations were stored before display and could be seen by multiple receivers. The thermometer cue was objectively veridical, but its reliability was subjectively uncertain and the unitless 50–250 room-temperature range created a task-induced conflict between displayed evidence and everyday temperature expectations. Production-noise groups spent more rounds tightly clustered around a wrong value than comprehension-noise groups (\(p=0.016\), group-level permutation). Production noise more often created a wrong common signal (\(p=0.025\), Fisher's exact test) and made that signal persist across more rounds (\(p=0.004\), permutation). Dynamic update models showed that production noise was not more harmful because people followed peers more strongly, but because the same peer influence acted on more correlated production-noise perturbations. Exploratory human analyses linked the mechanism to psychological patterns while a GPT-
agent experiment clarified a boundary condition: GPT
agents registered uncertainty through reduced confidence without reproducing human-scale production-noise vulnerability. Overall, noise did not simply degrade collective information acquisition. Comprehension noise could sometimes improve correction relative to the faithful control, whereas production noise could turn perturbations into common evidence and stabilize consensus on error.
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All authors Mohammad Salahshour, Sumanth Bhargava, Kajal Kumari, Niccolo Pescetelli, Yasser Roudi, Bahador Bahrami, Iain D. Couzin
Atmospheric and Oceanic Physics
Conservation-Based Feedback-Circuit Decomposition for Linear Forced Systems
Ming Cai
We present a conservation-based feedback-
circuit decomposition specifically for general linear forced systems. In a role parallel to that of
eigenvalues and eigenvectors for initial-value problems, the complete set of independent intrinsic
circuit gains and their associated forcing-transformation vectors provide a complete analytical representation of both transient and
equilibrium forced solutions. The sign of intrinsic
circuit gains determines whether successive feedback cycles exhibit monotonic or oscillatory convergence to transformed forcing, while the forcing-transformation vectors determine the structure of transformed forcing. The exact transient and
equilibrium solutions are represented analytically through the convergence of the finite-cycle forcing-transformation kernel to the
equilibrium forcing-transformation kernel, which is guaranteed regardless of whether the magnitudes of
circuit gains exceed one or unstable modes exist in the system. The feedback-
circuit decomposition provides a new generic foundational mathematical tool for understanding, predicting, and controlling forced responses in a broad range of coupled linear systems across science and engineering.
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Optics
Metasurfaces for neutral-atom trapping
Chengyu Fang, Minjeong Kim, Mark Saffman et al. (+2 more)
Trapped neutral atoms are one of the leading platforms for
quantum information technologies, in particular for
quantum computing, but scaling them to array sizes needed for utility-scale
quantum computing is a major engineering challenge. Here we review optical metasurfaces as an enabling technology that provides fine control over the phase, amplitude, and polarization of light, with pixel counts far exceeding what is available with spatial light modulators (SLMs) and other active devices. The large pixel counts have recently led to demonstrations of arrays of optical tweezers with hundreds of thousands of sites and arrays of optical bottle-beams with complex three-dimensional trapping profiles. The flexibility and scalability of optical metasurfaces provides a route towards miniaturized, integrated, and highly scalable atomic experiments and instruments.
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All authors Chengyu Fang, Minjeong Kim, Mark Saffman, Jennifer T. Choy, Mikhail Kats
Atomic Physics · Cross-listed from quant-ph
Rydberg state engineering of trapped ions
Robin Thomm, Vinay Shankar, Natalia Kuk et al. (+3 more)
Microwave dressing of Rydberg ions creates tunable eigenstates with controllable polarizability and interaction strength, but coherent
navigation between these states has remained elusive. Here, we report on the first demonstration of coherent population transfer between different Rydberg states of a trapped ion. We investigate both microwave-mediated Rabi oscillations between Rydberg S and P states and adiabatic transfer between microwave-dressed Rydberg states. Between Rydberg S and P states we achieve a population transfer efficiency of 91.5(5)% in a single microwave ${\pi}$-pulse. Microwave dressing hybridizes the S and P Rydberg states into new eigenstates with tunable polarizability, enabling both noise-resilient zero-polarizability states and maximally interacting states. We demonstrate adiabatic transfer between these zero-polarizability and maximally dressed states, enabling experiments that combine noise-resilient excitation with strong dipole-dipole interactions and Förster resonance control within a single measurement sequence.
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All authors Robin Thomm, Vinay Shankar, Natalia Kuk, Marion Mallweger, Ivo Straka, Markus Hennrich
Data Analysis and Statistics · Cross-listed from hep-ph
Generative Models and Statistical Validation
Sascha Diefenbacher, Sofia Palacios Schweitzer and Gregor Kasieczka
Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density
estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.
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Popular Physics
When market boundaries weaken: Network reconfiguration and regime-dependent cross-asset spillovers
Ruixue Jing and Luis Enrique Correa Rocha
Cryptocurrencies are increasingly adopted as investment assets, making their interactions with traditional financial
markets central to cross-asset diversification and systemic risk. This paper studies the integration of cryptocurrencies, fiat currencies, and S&P500 equities using a balanced panel of 381 assets from October 2017 to February 2024. We combine rolling correlation networks, community structure,
market-specific and system-wide
Turbulence Indices, and VAR-based connectedness analysis to examine how
market stress, network structure, and shock transmission vary across financial regimes. The results show that cross-asset integration is episodic. In calm periods, the three asset classes remain relatively segmented, whereas under stress, local clustering increases, modular separation weakens, and communities become more compositionally mixed across asset classes. Connectedness analysis further shows that regime shifts alter the structure of transmission rather than simply increasing spillover magnitudes. In high-
turbulence states, fiat-
market turbulence becomes the dominant propagation channel, while network clustering and modularity play a greater role in transmitting forecast uncertainty. These findings support the interpretation of network structure as an emergent, state-dependent transmission layer rather than a persistent exogenous driver of
turbulence. The results highlight the need for regime-aware risk monitoring, since full-sample connectedness estimates can understate the cross-asset coupling that emerges precisely when diversification benefits are most fragile.
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Physics and Society · Cross-listed from math.DS
Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy
Moyi Tian, Daniel A. Messenger, Vanja Dukic et al. (+2 more)
Social systems consist of networks of individuals who influence one another through social interactions. Studying how processes evolve on these networks can help us better understand patterns of social behavior. We study a system that couples online and offline social activity and investigate how to learn effective models directly from data using Weak Form Sparse Identification of Nonlinear Dynamics (WSINDy), a method for discovering governing equations. We assess learning performance using data generated by a mean-field approximation model of a
stochastic interaction process on networks and test how accurately the system can be recovered under different noise levels. Our results show that using more trajectories improves accuracy when noise is high, but only a small number of additional trajectories is needed to gain most of the benefit, with little improvement beyond that. We also learn effective ODE models from averaged
stochastic data on networks. When traditional mean-field approximations fail, identifying continuum ODEs directly from
stochastic processes yields efficient models that better match the data and provide deeper insight into the underlying dynamics.
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All authors Moyi Tian, Daniel A. Messenger, Vanja Dukic, Nancy Rodríguez, David M. Bortz
General Physics
The Saddle Point of Everything
K. Sravan Kumar
The harmonic oscillator is the universal
Hamiltonian of stable
equilibrium. Its counterpart, the inverted harmonic oscillator (IHO), is the
Hamiltonian of unstable
equilibrium: the saddle point of physical systems. It appears across disciplines, from condensed matter,
quantum optics, and
quantum chemistry to the Standard Model
Higgs instability and
quantum field theory near gravitational horizons. Its mathematical depth is further reflected in its relation to the non-trivial zeros of the Riemann
zeta function through the Berry-Keating
Hamiltonian. Remarkably, a dual
Hamiltonian to the IHO has recently been shown to govern the additional
spin-2 sector of the unique
unitary perturbatively renormalizable theory of
quantum gravity in four dimensions, with that sector remaining purely virtual and regularizing gravitational interactions at the Planck scale. This paper argues that the universal physics of the saddle point course-corrects the history of
quantum gravity approaches that abandoned renormalizability, uniqueness, and predictivity. Its consequences include a non-singular Universe, Starobinsky inflation, and possible implications for large-scale CMB features and
primordial gravitational waves.
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Optics · Cross-listed from quant-ph
Analytical model for structured light propagation through a turbulent atmosphere
Konstantin Kravtsov
We develop a straightforward analytical framework for the propagation of spatial light modes through a turbulent atmosphere. Built upon the split-step approach with the mode-based optical field representation, it directly assesses how
turbulence-induced phase fluctuations deplete the optical power in the original mode and re-distribute it into neighboring spatial modes. Importantly, this power transfer scales linearly with the propagation distance in a uniform channel, yielding a simple solution for arbitrary distances in the form of a
matrix exponential. The transfer rate is determined by the spatial spectral overlap between the
turbulence spectrum and the acceptance spectrum for a pair of interacting spatial modes. The model predicts the average power in each spatial mode and is exact when a single mode strongly dominates all others. Our predictions show reasonably good agreement with simulations up to medium-to-strong
turbulence levels. The model also confirms the scalings with mode order previously known as empirical observations.
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Atomic Physics
Enhanced Loading of a Molecular Magneto-Optical Trap
Ebram Youssef, Kaiya Wilson, Jiahe Cao et al. (+6 more)
Molecular magneto-optical traps (MOTs) typically capture orders of magnitude fewer particles than their atomic counterparts due in part to their significantly lower capture velocities. Here, we employ a
Stochastic Schrödinger Equation Monte Carlo approach to model a CaF DC MOT to understand the factors limiting capture velocity. We provide physical intuition into the mechanisms that affect capture velocity and identify important parameters and general strategies to improve it. In addition, we point out a loss mechanism intrinsic to molecular MOTs and determine parameter regimes that should be avoided experimentally. We
benchmark our simulations against a CaF DC MOT and experimentally implement the improvements predicted by our model. In doing so, we demonstrate a molecular MOT with 1.5 million trapped molecules. This represents an eight-fold improvement and is an important step toward achieving
quantum degeneracy with
laser cooled molecules.
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All authors Ebram Youssef, Kaiya Wilson, Jiahe Cao, Reilly Brislawn, Avani Lakkireddy, Kun Liu, Aaron Teo, Phoebe Turner, Loïc Anderegg
Physics and Society
A temporal proximity network dataset from a wedding cocktail hour with relationship category labels
Joshua Z. Stadlan, Richard B. Kahn and Michelle Birkett
Objectives: We captured a fine-grained
dataset of unstructured social interaction with socially meaningful group labels to fill a gap in the study of face-to-face interaction. Prior interaction data from conferences, classrooms, hospitals, and workplaces exhibit network signatures such as heterogeneous contact rates, clustering, and bursty dynamics. However, schedules, room assignments, and authority roles in these settings may obscure unstructured social group dynamics. Studies on group mixing often rely on demographic proxies like gender, or assigned categories like school classes, rather than relationship-based groups. We aim to understand if temporal network signatures of institutionally structured settings generalize to unstructured social interaction. Data description: We present the first public temporal proximity network
dataset of a privately hosted social event with contextual relationship-based group membership. At the outdoor cocktail hour of a wedding, 95 participants wore proximity sensor badges that detected other badge-wearers within approximately 1.5 m in 5 s intervals. This
dataset, coarsened to 10 s temporal bins, contains 7,213 contact events over 2,760 observed dyads. Participants self-reported their relationship category with respect to the wedding couple, enabling group mixing analysis. Beyond implications for the generalizability of interaction patterns, this
dataset supports social event modeling for applications from contact tracing to social-space design.
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Instrumentation and Detectors
Characterization of Spurious Charge in SENSEI Skipper-CCDs
Yikai Wu, Ansh Desai, Sho Uemura et al. (+16 more)
Skipper Charge-Coupled Devices (Skipper-CCDs) are a leading technology in the search for sub-GeV dark matter and coherent elastic
neutrino-
nucleus scattering. A key background for rare-event searches with these detectors arises from "spurious charge" – single-electron events generated when charges are transferred through the active region to the serial register, and across the serial register to the readout stage. We present a characterization of spurious charge in both the active region and the serial register of SENSEI Skipper-CCDs, and show that, in a well-shielded low-background environment, the dominant contribution originates in the serial register during Skipper readout, when horizontal clocks are held at constant voltage between pixel transfers. Motivated by this finding, we develop a "tri-level" clocking scheme in which the held-low phase is raised to an intermediate voltage during readout to suppress trap-mediated charge generation. Using the SENSEI detector near the MINOS cavern, we measure a serial-register single-electron density of $(2.9 \pm 0.1) \times 10^{-5}$ electrons/pixel/image under standard SENSEI readout conditions, reduced to $(4.0 \pm 0.4) \times 10^{-6}$ electrons/pixel/image with tri-level clocking – a factor of $\sim$7 improvement. This technique offers a promising path to lower backgrounds in current and future Skipper-CCD experiments.
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All authors Yikai Wu, Ansh Desai, Sho Uemura, Ana M. Botti, Brenda A. Cervantes-Vergara, Fernando Chierchie, Alex Drlica-Wagner, Rouven Essig, Juan Estrada, Erez Etzion, Guillermo Fernandez Moroni, Miqueas Gamero, Stephen E. Holland, Ian Lawson, Steffon Luoma, Nathan A. Saffold, Miguel Sofo-Haro, Javier Tiffenberg, Tomer Volansky
Fluid Dynamics · Cross-listed from cs.LG
Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor
Minseo Lee, Seongmin Oh, Chaehyeon Song et al. (+5 more)
Real-time thermal-hydraulic simulation is essential for digital twin (DT) technology that supports the safe and efficient operation of small modular reactors (SMRs). Computational fluid dynamics (CFD) provides high-fidelity flow analysis, but its computational cost prevents direct use in DT applications. AI-based surrogate modeling has been actively investigated to address this limitation, yet neural operator–based surrogates for CFD-level transient analysis of SMR-specific geometries have not been reported. This study presents an integrated framework that combines a reduced-order model (ROM) with neural operators, applied to the helical coil steam generator (HCSG) of the System-integrated Modular Advanced Reactor (SMART). Two ROM strategies tailored to each CFD data type were compared, an MLP-based autoencoder (AE) for unstructured mesh data and a convolutional autoencoder (CAE) for structured mesh data, and each was coupled with the deep operator network (DeepONet) to construct the latent DeepONet (L-DeepONet). The
Fourier neural operator (FNO) was additionally adopted for comparison. A multi-scale technique was incorporated into both frameworks to mitigate spectral bias and improve the prediction of Kármán
vortex streets developing inside the HCSG. The multi-scale L-DeepONet captured the instantaneous periodic
vortex dynamics in both velocity and pressure fields, while the FNO and its multi-scale variant predicted the time-averaged mean flow and provided reliable pressure drop estimates. These complementary characteristics provide a practical model-selection guideline that links each architecture to specific DT objectives based on CFD data type and the required level of flow resolution.
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All authors Minseo Lee, Seongmin Oh, Chaehyeon Song, Bumjin Cho, Shilaj Baral, Sangam Khanal, Minseop Song, Joongoo Jeon
Physics and Society
Exponent spectrum of Lorenz curves and its relation to system's heterogeneity
Soumyaditya Das and Soumyajyoti Biswas
We analyze the effect of microscopic heterogeneity on the Lorenz curve of macroscopic observables. Lorenz curve of a response function being a cumulative and bounded quantity, is often a more stable function than the corresponding probability density. We show here that by doing an exponent spectrum analysis of the complementary Lorenz curve, it is possible to obtain a reflection of the underlying heterogeneity that causes the response function to depart from a power law behavior. We demonstrate this framework first by synthetic data and then by analyzing the avalanche statistics of a two dimensional, Random Field Ising Model (RFIM) at zero temperature. This method can lead to possible use in estimating microscopic heterogeneity of a system from analysis of an estimated Lorenz curve, particularly in socio-economic and physical contexts where the full probability distribution function is unavailable.
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Optics · Cross-listed from quant-ph
Heralded ultrafast generation of macroscopic quantum states in matter with bright squeezed vacuum light
Shohei Imai
We show that bright squeezed
vacuum light, combined with a single-shot quadrature measurement of the post-interaction light, enables the ultrafast generation of macroscopic
quantum states in matter. Although in the weak-coupling regime multiphoton
quantum light leaves the unconditional matter state as a classical mixture due to light–matter
entanglement, quadrature-based heralding prepares the matter in a Gaussian-weighted
quantum superposition of
laser-driven matter states. For an ensemble of resonantly electric-dipole-coupled two-level systems, this heralding dynamics acts as a Gaussian filter with respect to the electric polarization, with brighter squeezed-
vacuum light accelerating the preparation of the zero-
eigenvalue Dicke state. Counter-rotating terms further drive a stroboscopic transition from this Dicke state to a cat-like state. Our results open a route to ultrafast engineering of macroscopic
quantum matter with strong-field
quantum light.
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Optics
On-Chip Chiroptical Sensor based on Directional Deflection of Light: A Stern-Gerlach Integrated Optical Analog
Josep Martínez-Romeu, Alejandro Martínez and J. Enrique Vázquez-Lozano
Chiroptical techniques for detecting and characterizing the chirality of matter and artificial nanostructures are traditionally based on their interaction with
chiral light, typically circularly-polarized fields propagating in free space. More recently, these approaches have been extended to integrated photonic platforms, offering significant practical advantages. However, the generation of
chiral guided light is challenging, it requires degeneracy of the fundamental modes of the integrated waveguide, which occurs at a single wavelength and limits broadband characterization. Here, we introduce and simulate numerically a new alternative integrated optical configuration inspired by the Stern-Gerlach experiment, in which a
chiral sample is illuminated by a linearly polarized light beam. Such a beam exits from a dielectric waveguide that propagates the fundamental TE mode, thereby eliminating the need for circularly polarized excitation. On this basis, enantio-discrimination is achieved through a spatially resolved scheme using a symmetric arrangement of optical
antennas on either side of the propagation axis. Our simulation results suggest that the selective scattering and absorption of the circular polarization component matching the chirality of the sample induces an imbalance of (
spin) angular momentum of the optical field, resulting in a transverse deflection of the beam toward the corresponding side. This on-chip platform then provides a direct route to chiroptical functionalities under linearly-polarized illumination, enabling compact implementations of
chiral sensing, spectroscopy, optical computing, and communication schemes.
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