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Physics Physics

physics Watch Physics

Wednesday 1 July

Computational Physics · Cross-listed from math.NA

A Nonstandard Finite Difference Scheme for a Nonlinear Parabolic Equation with p-Laplacian-Type Diffusion

Achraf Zinihi, Matthias Ehrhardt and Moulay Rchid Sidi Ammi

We propose and analyze a nonstandard finite difference (NSFD) scheme for nonlinear parabolic equations involving a p-Laplacian-type diffusion operator in one- and two-dimensional spatial domains. Following Mickens' design principles, the proposed discretization employs a nonlinear denominator function phi(.) together with a nonlocal approximation of the nonlinear diffusion term Delta_p, yielding a structure-preserving discrete model. The scheme is designed to retain key qualitative properties of the continuous problem, including positivity, boundedness, and stability, which may be lost by standard finite difference methods (FDMs). We establish the well-posedness of the continuous model, derive the NSFD scheme, and investigate its consistency, convergence, and local truncation error. Numerical experiments confirm the theoretical results and demonstrate that, unlike the standard explicit FDM, the proposed NSFD scheme avoids spurious oscillations and nonphysical negative solutions even for relatively large time-step sizes.
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Biological Physics

When is vaccine prioritization worth optimizing?

Mi Feng, Zhaohua Lin, Changsong Zhou et al. (+1 more)

Optimizing vaccine prioritization is often treated as the default policy response when vaccine supply is limited. Yet optimized prioritization carries administrative, ethical and communication costs, motivating an upstream question: whether differences among vaccine allocations can alter epidemic outcomes enough to make optimization epidemiologically necessary. We show that optimization is not always worth pursuing: in some regimes, vaccination markedly reduces epidemic burden, but many feasible allocation rules perform almost equally well, making the necessity of optimization low. We quantify this necessity as the range of epidemic outcomes generated by different allocations under fixed supply and show that it is governed by competition between vaccinating high-contact groups to slow transmission and vaccinating groups that benefit most directly: necessity is low when these protection routes are balanced and high when one dominates. Increasing transmission intensity changes this balance and drives a transition in the optimal allocation from transmission-focused prioritization toward direct protection. Different prevention objectives exhibit distinct transition thresholds, creating regimes in which optimizing one objective substantially compromises another, thereby revealing when the choice of prevention target matters most. This framework reframes vaccine prioritization as a prior decision problem, identifying when optimization is warranted, when simpler rules suffice, and when prevention goals conflict.
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All authors Mi Feng, Zhaohua Lin, Changsong Zhou, Liang Tian

Optics

Finite-Size Effect Induced Spatial-Spectral Mode Splitting in Membrane Metasurfaces

Chih-Zong Deng, Mu-Hsin Chen, Chun-Hao Chiang et al. (+4 more)

This work reports the spatial-spectral engineering and finite-size quantization of optical modes within a triangular-lattice silicon nitride membrane metasurface. Truncating the lattice into a finite square cavity breaks translational symmetry and lifts modal degeneracy, splitting optical modes into discrete cavity-envelope sub-modes. High-resolution photoluminescence (PL) scanning reveals distinct spatial field distributions. The corner-localized sub-mode features the highest Q-factor due to multipolar far-field destructive interference, whereas the core-localized sub-mode exhibits strong radiative coupling. PL mapping reveals a symmetric, four-fold clover-like wavelength arrangement. These results demonstrate that boundary-induced deterministic symmetry can override underlying lattice characteristics, offering a robust strategy for precise spatial-spectral tailoring of light-matter interactions at the nanoscale.
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All authors Chih-Zong Deng, Mu-Hsin Chen, Chun-Hao Chiang, Jui-Han Fu, Vincent Tung, Masanobu Iwanaga, Ya-Lun Ho

Optics · Cross-listed from quant-ph

Bridging quantum mechanics and nonlinear optics in Raman scattering

Naoki Fukutake and Hideaki Kano

We present a theoretical framework for spontaneous Raman scattering that fundamentally bridges quantum-mechanical and nonlinear-optical approaches. By conceptualizing spontaneous Raman scattering as a stimulated Raman gain or loss event seeded by the quantum vacuum field, we rigorously derive the spontaneous Raman cross-section directly from the third-order nonlinear susceptibility. Crucially, this framework predicts the existence of a hitherto unrecognized phenomenon: "spontaneous Raman loss" (sRL), which acts as the vacuum-seeded counterpart to stimulated Raman loss, complementing traditional spontaneous Raman scattering (spontaneous Raman gain, sRG). Furthermore, we establish a rigorous connection to the traditional Kramers-Heisenberg-Dirac (KHD) theory, revealing that the spontaneous process is governed by interference before a detector between the signal field emitted from molecules and the vacuum field itself that stimulates the molecules. This insight uncovers a direct correspondence between the sRG susceptibility and the rotating/counter-rotating interference terms in the KHD formula. Ultimately, we extend the foundational KHD theory by incorporating previously unrecognized essential terms, achieving perfect analytical agreement between the quantum mechanical and nonlinear optical descriptions of Raman scattering.
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Computational Physics · Cross-listed from cs.CE

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

Xin-Yang Liu, Xiantao Fan and Jian-Xun Wang

Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters. An effective approach to address these challenges is leveraging physics priors in training neural networks, known as physics-informed deep learning (PiDL). In this work, we introduce the Multi-Resolution Finite-Volume-inspired network, MuRFiV, designed to capitalize on the conservative property of finite volume on the global scale and the expressive power of deep learning on the local scale. We demonstrate the effectiveness of MuRFiV on several spatio-temporal systems governed by partial differential equations (PDEs), including Burgers' equation, shallow water equations, and incompressible Navier-Stokes equations. By embedding PDE information into the deep learning architecture, MuRFiV achieves strong long-term prediction accuracy and remains stable over very long autoregressive rollouts, significantly outperforming data-driven neural network baselines. This result highlights the promise of combining multiresolution learning with finite-volume-inspired inductive bias for accurate and robust long-term prediction of complex dynamics.
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Space Physics · Cross-listed from astro-ph.SR

Optimization Algorithm for Determining the Source Surface Radius Based on Parker Solar Probe in situ Measurements from Encounters 1 to 19

Shiouhe Wang, Fang Shen, Yi Yang et al. (+2 more)

The Potential Field Source Surface (PFSS) extrapolation is a method for estimating the large scale coronal magnetic field from photospheric magnetograms. The source surface serves as the outer boundary of its solution domain, and is typically a spherical surface. An appropriate source surface radius ($R_{ss}$) enables more accurate identification of the coronal magnetic field topology and estimation of the open flux, thereby potentially enhancing the accuracy of space weather modeling. We prove the well-posedness of the PFSS forward problem and establish the existence and uniqueness of the optimal source surface by combining compactness of the admissible set with continuity of the objective functional. The objective functional is the mean squared error (MSE) between PFSS extrapolation and Parker Solar Probe (PSP) radial magnetic field measurements after Parker spiral backmapping and radial scaling for Encounters 1-19. The optimization algorithm is validated with an analytical solution, and Advanced Composition Explorer (ACE) in situ measurements are used as an independent cross-validation dataset. Additional evaluation metrics and Pareto analysis are used to identify the dominant metrics between open flux and polarity prediction accuracy. Our results show that the optimal $R_{ss}$ derived from the algorithm generally increase from solar minimum into the ascending phase of solar cycle 25. The optimized solution improves open flux agreement while preserving or improving polarity prediction accuracy relative to $2.5R_{s}$. The Pareto frontiers show a transition for dominant metrics from open flux during solar minimum to polarity prediction accuracy during the ascending phase.
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All authors Shiouhe Wang, Fang Shen, Yi Yang, Xueshang Feng, Jiansen He

Fluid Dynamics · Cross-listed from cond-mat.stat-mech

Slow heat-driven flow in a gas of hard disks

Amit Kumar, Abhishek Dhar and Baruch Meerson

We study a slow heat-driven flow in a gas of elastically colliding hard disks confined to a long channel. The initial state consists of two regions with large temperature and density contrasts but nearly equal pressures, leading to a low-Mach-number, nearly isobaric evolution. In the dilute limit, the corresponding isobaric hydrodynamic theory reduces to a previously known ideal-gas description. We extend this theory to finite densities by incorporating a non-ideal equation of state of a hard-disk fluid, and solve the resulting one-dimensional equations numerically. Finite-density effects produce appreciable deviations from the ideal-gas prediction. We then test the theory directly against event-driven molecular dynamics simulations of hard disks and find very good agreement in both the dilute and finite-density regimes. The results provide, to our knowledge, the first particle-level test of isobaric gas dynamics of a strongly inhomogeneous cooling flow.
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Space Physics

VLBI Tracking of the JUICE Mission: Two Years of Cruise Phase Operations and Performance Analysis

Oliver James White, Guifre Molera Calves, Jasper Edwards et al. (+2 more)

The JUpiter ICy moons Explorer (JUICE) mission, launched by the European Space Agency (ESA) in April 2023, represents one of the most ambitious deep space exploration endeavours targeting Jupiter's icy moons. This paper presents results from the Very Long Baseline Interferometry (VLBI) radio telescope tracking conducted by the University of Tasmania during the first two years of JUICE's cruise phase operations. We have conducted over 100 tracking sessions capturing the spacecraft across different orbital regimes as JUICE progresses through its complex cruise trajectory towards Jupiter. Our analysis focuses on three key areas: Doppler residual characterisation, mission performance indicator extraction, and solar wind scintillation pattern analysis (including space weather forecasting). UTAS measurements demonstrate the enhanced capabilities that VLBI networks provide for deep space mission support, particularly for precision orbit determination and spacecraft health diagnosis. The results showcase the UTAS VLBI array as a valuable complement to traditional tracking infrastructure, offering Southern Hemisphere coverage and enhanced geometric diversity for deep space missions.
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All authors Oliver James White, Guifre Molera Calves, Jasper Edwards, Giuseppe Cimo, Dominic Dirkx

Data Analysis and Statistics · Cross-listed from nucl-th

Spin Femtoscopy: A Framework for Revealing Genuine Spin Correlations

Kehao Zhang, Xuan Wang and Xiaofeng Luo

Spin correlations are among the most fundamental quantum observables in many-body systems, yet they remain difficult to access experimentally in relativistic heavy-ion collisions. Existing spin measurements, including hyperon polarization and vector-meson spin alignment, have revealed important single-particle spin phenomena, but genuine two-particle spin correlations in the produced hadronic system remain largely unexplored. Here we propose spin femtoscopy, a framework for accessing genuine two-particle spin correlations through spin-resolved femtoscopic measurements. The key principle is that different two-particle spin configurations can give rise to different femtoscopic correlation functions because of quantum statistics, spin-dependent final-state interactions. Using $\Lambda\Lambda$ pairs as a proof of principle, we exploit the self-analyzing weak decay of $\Lambda$ hyperons to construct spin-sensitive femtoscopic correlation functions with different singlet and triplet admixtures. We show that these observables provide experimental access to the spin-state populations of the pair and allow genuine spin correlations to be separated from spin-dependent femtoscopic mixing caused by quantum statistics and final-state interactions. This work extends femtoscopy from a probe of source geometry and final-state interactions to a framework for revealing the quantum spin structure of strongly interacting matter.
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Biological Physics

A bilayer cellular Potts model of epithelial docking

Troy Singletary, Andrea James and Tyler A. Engstrom

Fusion of two epithelial cell sheets brought together in a bilayer configuration is a common step in animal morphogenesis, yet, in contrast to other epithelial fusion processes such as wound healing in a monolayer of cells, it has not been a strong focus of modeling efforts. Here we consider a preliminary stage of bilayer fusion, recently termed "docking." In multiple instances of docking that span apical and basal varieties, cells appear to have a tendency to remodel so as to co-localize their bilateral junctions (match their edges) across the bilayer. Motivated by this observation, we introduce a bilayer cellular Potts model that couples two standard 2D area- and perimeter-elasticity models via short-range, out-of-plane interactions between cell edges. The new coupling involves a single adjustable parameter that minimally models the combined effect of dynamic cytoskeletal protrusions, cadherins, and other potential edge-associated adhesion molecules. Our model predicts that bilayer edge matching is maximized when the two monolayers are in their fluid-like regimes (average cell shape index greater than 4.6 in our implementation), and when the bilayer coupling strength strikes a balance between in-plane and out-of-plane energy scales. At higher coupling strengths, the system tends to get stuck in metastable states with sub-optimal edge matching. Exploration of the mechanisms of edge matching reveals that pairs and quadruplets of coordinated T1 transitions play a particularly important role. We also find numerous examples of emergent features we term "domain walls" - branching or unbranching curves that cross no matched edges, but that separate regions of nearly complete matching. These domain walls can be both system spanning and long lived. Finally, we extend our model to crudely account for bending of the two sheets, and study the distributions of docking front speeds that result.
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Atomic Physics

Correct Asymptotic Wavefunctions for Calculating Photoelectron Angular Distributions of O2- and NO-

Wenru Jie, Rui Zhang, Jiayi Chen et al. (+2 more)

The ab initio calculation of photoelectron angular distributions (PADs) for negative ions remains a significant theoretical challenge. In this work, we report a joint experimental and theoretical investigation of PADs for a series of molecular anions with varying polarities, including the nonpolar O2-, the weakly polar NO-, and the strongly polar AsO- and SbO-. To accurately describe the long-range electronic wavefunctions – where photodetachment contributes most strongly – we modified the standard Gaussian-type orbitals (GTOs) by augmenting them with a correct exponential Slater-tail basis set (~e^(-{\xi}r)). This simple yet effective approach significantly improves the agreement between the experimental and theoretical PADs for O2- and NO-. However, notable discrepancies persist for NO- for transitions to the v = 0 and v = 1 vibrational levels of neutral NO even after this correction. Given that our methodology successfully reproduced PADs for strongly polar anions (e.g., AsO- and SbO-), these residual discrepancies are unlikely to stem from "exit-channel scattering" induced by long-range dipole fields. Instead, we tentatively attribute the failure for NO- to the breakdown of the Born-Oppenheimer approximation or the frozen orbital approximation, arising from the extremely weak binding of the excess electron.
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All authors Wenru Jie, Rui Zhang, Jiayi Chen, Qihan Liu, Chuangang Ning

Instrumentation and Detectors

The Solar Neutrino and Astro-Particle PhYsics (SNAPPY) CubeSat Development

Brian M. Sutin, Edward Bierens, Brian Doty et al. (+14 more)

The SNAPPY CubeSat, which was launched May 3, 2026, will demonstrate and space qualify the nuSol neutrino-detection technology. The nuSol technology detects solar neutrinos using a gallium isotope which decays by emitting two particles spaced apart in time; this allows differentiating neutrino events from cosmic rays. In the NIAC Phase II project review in 2021, concept and science were determined to be feasible; however, two precursor studies were recommended before pursuing a full mission study. These studies were to characterize the true deep-space background for the detector's gallium double-pulse signal and to collect a statistically significant number of double-pulse events demonstrating that fast electronics can reliably select and analyze this signal. To test double-pulse signals in space, a NIAC Phase III funded building a 3U CubeSat carrying a 0.1-kg gallium-aluminum-gadolinium-garnet detector housed within an active veto array and shielding. Because the detector requires deep-space-like conditions, the CubeSat is designed for a polar low-Earth orbit at 450 km or higher altitude, collecting data over the Earth's poles above the Van Allen belts. The detector is highly sensitive, with roughly 7-percent energy resolution, with active veto shielding and passive shielding using a patented tungsten-powder and epoxy mixture that disintegrates upon atmospheric reentry. SNAPPY enables additional science during the extended mission phase of year two operations. These include measurements of solar wind particle density and energy spectra with particle identification of electrons, protons, and alpha particles; detection of very low-energy gamma rays from galactic gamma-ray bursts without directionality.
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All authors Brian M. Sutin, Edward Bierens, Brian Doty, Atri Dutta, Jonathan Folkerts, Brooks Hartsock, Kyle Messick, Holger Meyer, Daniel Reichart, Nick Solomey, Mark Crystal, Miguel Rodriguez-Otero, Evgeny Kuznetsov, Robert McTaggart, James Cutler, Joel Steinkraus, Jose G. Rivera

Optics

Finite slab first passage statistics of Henyey Greenstein scattering

Robert Cordery and Claude Zeller

A photon entering a plane parallel scattering slab performs a random walk and eventually escapes through one of the two faces or is absorbed. The standard model employs a Henyey Greenstein phase function (HG) and an exponential step length distribution (Exp). Slab reflectance, transmittance, absorptance, and emergent angular distributions can be calculated in terms of random walk statistics. A central result is that the slab calculations factor into the order resolved first passage statistics of a half space combined with the a factor for the slab thickness. Absorptance is derived from order resolved walk statistics using the absorption rate. Two approaches are used. In the Monte Carlo (MC) approach, an extremely long random walk with many steps is efficiently generated without regard to any boundaries. The intersection of this walk with a large collection of target objects creates an ensemble of excursions of the objects. The MC approach relies explicitly on the memoryless property of Exp so that the portion of the first and last steps inside the object follow the same length distribution as the walk steps. The details of each excursion are recorded and any statistics can be extracted from the database of excursions. In particular, first passage statistics are extracted from this ensemble. In this work the objects are slabs with different positions and thicknesses. In the radiative transfer (RT) approach the slab is divided into thin layers with scattering treated to first order in each layer. The RT equations are then directly integrated over the slab to give the desired first passage statistics, reflectance, transmittance, and absorptance. The two methods agree to the Monte Carlo precision over the tested range of random walk parameters.
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Plasma Physics

Improved Particle Confinement with Resonant Magnetic Perturbations in DIII-D Tokamak H-Mode Plasmas

N. C. Logan, Q. Hu, C. Paz-Soldan et al. (+9 more)

Experiments on the DIII-D tokamak have identified a novel regime in which applied resonant magnetic perturbations (RMPs) increase the particle confinement and overall performance. This Letter details a robust range of counter-current rotation over which RMPs cause this density pump-in effect for high confinement (H mode) plasmas. The pump in is shown to be caused by a reduction of the turbulent transport and to be correlated with a change in the sign of the induced neoclassical transport. This novel reversal of the RMP induced transport has the potential to significantly improve reactor relevant, three-dimensional magnetic confinement scenarios.
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All authors N. C. Logan, Q. Hu, C. Paz-Soldan, R. Nazikian, T. Rhodes, T. Wilks, S. Munaretto, A. Bortolon, F. Laggner, F. Scotti, R. Hong, H. Wang

Optics

X-ray Coherent Attosecond Pulse Pair Spectroscopy

Zain Abhari, Thomas M. Linker, Thomas Kroll et al. (+14 more)

X-ray free electron laser (XFEL) experiments using self-amplified spontaneous emission (SASE) pulses typically achieve temporal resolutions of order several femtoseconds, as the pulse duration puts a practical limit to pump-probe or probe-probe schemes. Even with the emerging capabilities to generate pulses with attosecond durations with new single-spike SASE schemes, direct access to attosecond electron dynamics remains an experimental challenge. Here we show how X-ray coherent attosecond pulse-pair spectroscopy (X-CAPPS) provides a powerful new approach to access the ultrashort time-delay window. Coherent attosecond pulse pairs with time delays varying from ~500 as to ~5 fs are generated with Cu K$\alpha_1$ stimulated X-ray emission from a gain medium pumped by intense SASE XFEL pulses. These pulse pairs are analyzed with two subsequent Bragg crystal spectrometers, and the resulting interference spectrum is captured on two sequential 2D image detectors encoding their time separation, relative amplitudes, and phases with high precision. X-CAPPS requires no split-and-delay X-ray optics, nor XFEL pulse modifications, making it broadly implementable across existing facilities. This technique enables the investigation of attosecond processes with $\mathring{A}$ngström resolution, providing a new tool for probing ultrafast dynamics across a wide range of atomic, molecular, and solid systems.
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All authors Zain Abhari, Thomas M. Linker, Thomas Kroll, Yurina Michine, Gota Yamaguchi, Yuichi Inubushi, Taito Osaka, Jumpei Yamada, Ichiro Inoue, Makina Yabashi, Aliaksei Halavanau, Andrei Benediktovitch, Nina Rohringer, Matthias F. Kling, Claudio Pelligrini, Hitoki Yoneda, Uwe Bergmann

Applied Physics · Cross-listed from quant-ph

Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization

Caleb Walton, Patricia García-Caspueñas, Filippo Zacchei et al. (+3 more)

The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control.
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All authors Caleb Walton, Patricia García-Caspueñas, Filippo Zacchei, Ana Larrañaga, Steven L. Brunton, Sara Mouradian

Tuesday 30 June

General Physics

Hyperfine transitions in atoms and rotational transitions in molecules of the early Universe as a possible source of CMB distortions

M. N. Golubev, P. A. Kislitsyn and A. V. Ivanchik

In the near future, measurements are expected to detect the influence of the hyperfine transition in the ground state of the hydrogen atom on the cosmic microwave background (CMB) spectrum. This raises the question of what other substances could produce similar distortions and to what extent. This work presents a systematic analysis of the potential for the most abundant atoms, molecules, and their ions to distort the CMB spectrum. In addition, the wavelength ranges for all the transitions considered in which such distortions might be observed have been calculated. As a result, we conclude that the distortions caused by the most abundant elements are orders of magnitude smaller than those produced by hydrogen.
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Applied Physics

Non-reciprocal heat transfer advances flexible thermoelectric devices

Jinwen Yang, Wenmei Luo, Hongbin Xu et al. (+5 more)

Complex heat dissipation assemblies, inferior performance, and limited flexibility are the primary constraints impeding the wide application and commercialization of conventional flexible thermoelectric devices in wearable electronics and other high-end cooling scenarios. In this work, we report a non-conventional design for flexible thermoelectric devices which can reduce the temperature to -7.03 at room temperature without external heat sink, achieving a cooling temperature drop of 29.25. The design is based on non-reciprocal heat transfer, integrated with thermally conductive composites and screen-printing technologies. This approach takes advantage of directional heat flow, thereby eliminating the need for complex heat sink networks, which extend the applications of flexible thermoelectric devices from personal thermal management to more broader fields such as home healthcare and emergency first aid.
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All authors Jinwen Yang, Wenmei Luo, Hongbin Xu, Fuqing Duan, Yafei Ding, Jie Chen, Guimei Zhu, Baowen Li

Biological Physics

Reaction-Coordinate-Dependent Non-Markovian Friction Governs Protein-Folding Dynamics

Lucas Tepper, Benjamin J. A. Héry, Cihan Ayaz et al. (+4 more)

It is common to project the full atomic-resolution representation of a protein onto a one-dimensionalreaction coordinate (RC) to capture the protein-folding kinetics. As a direct consequence ofthis dimensionality reduction, non-Markovian friction emerges in the framework of the general-ized Langevin equation (GLE). All previous applications of GLEs to protein folding employed anRC-independent friction memory function and therefore did not account for the different frictionin the folded and unfolded states. Using a recently derived GLE with RC-dependent mass andfriction memory function, we introduce a novel method to extract memory functions from timeseries data via a conditional Volterra equation. When applied to molecular dynamics (MD) data ofsix fast-folding proteins, we find strongly RC-dependent memory friction in line with the intuitiveexpectation that friction is higher in the folded than in the unfolded state due to internal proteinfriction. Our numerically efficient method to simulate the GLE confirms the accuracy of the GLEparameter extraction by comparison with the MD data. We show that RC-dependent memoryfriction not only adds physical insight into the folding process but also significantly improves thedescription of protein folding kinetics using low-dimensional RCs.
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All authors Lucas Tepper, Benjamin J. A. Héry, Cihan Ayaz, Florian N. Brünig, Benjamin Dalton, Anton Klimek, Roland R. Netz

Chemical Physics

Position: The Inevitable Transition to Machine Learning in Quantum Chemistry

Karen Sargsyan and Chao-Ping Hsu

Finding exact solutions to the quantum many-body problem is computationally intractable (QMA-hard). Traditional approximations for electrons in an atom or molecule – density functional theory and wavefunction methods – have been indispensable, but their development shows signs of saturation: DFT functionals have proliferated without converging toward the exact functional, and strong correlation remains largely unsolved after decades of effort. This position paper argues that machine learning represents the most promising path forward – not as a proof of logical necessity, but as a decision-theoretic argument: ML succeeds whether the underlying problems are truly hard or merely lack simple analytical solutions. We reframe recent traditional method development as “hand-crafted machine learning” that has exhausted the hypothesis space accessible to human intuition. Significant challenges remain, but these have clear research paths forward, unlike the fundamental barriers facing traditional approaches. ML-based approaches merit strategic priority in quantum chemistry's next phase.
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Atmospheric and Oceanic Physics

GraphCast Skill and Systematic Biases in Indian Summer Monsoon Forecasts: Evaluation Against ERA5 and IMERG

Somnath Luitel, Parthasarathi Mukhopadhyay, Manmeet Singh et al. (+2 more)

The Indian Summer Monsoon (ISM) is one of the most dynamically complex components of the global climate system, yet its accurate prediction remains challenging for both physics-based and emerging AI weather models. We present a climatological evaluation of Google's GraphCast against ERA5 reanalysis and the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation dataset during the boreal summer (June-September, JJAS) for 2021-2024. Deterministic GraphCast forecasts initialized at 00 UTC are composited to evaluate +24 h, +48 h, and +72 h lead times over the full ISM domain. The analysis examines the climatological mean state (rainfall, surface temperature, and low-level winds), rainfall intensity distribution, thermodynamic structure (tropospheric temperature gradient, DTT; apparent heat source and moisture sink, Q1 and Q2), monsoon dynamics (vertical wind shear), and rainfall variability across intraseasonal, synoptic, and spectral timescales. GraphCast reproduces the broad spatial pattern and seasonal evolution of monsoon rainfall with good fidelity at short lead times but exhibits a domain-averaged wet bias over the core monsoon region. It also substantially suppresses rainfall variability across nearly all timescales (regional power-spectrum variance ratio of 0.14 relative to IMERG) and produces a compressed rainfall intensity distribution, overestimating moderately heavy rainfall (95th percentile) while systematically underrepresenting the most extreme events. These biases are accompanied by a deficient lower-tropospheric Q1 profile and weaker northward-propagating intraseasonal variability. Together, the results reveal a consistent deterministic-smoothing signature in GraphCast's precipitation forecasts and provide benchmark diagnostics for evaluating next-generation AI weather models for tropical medium-range forecasting (+24 h to +72 h).
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All authors Somnath Luitel, Parthasarathi Mukhopadhyay, Manmeet Singh, Sandeep Juneja, Saptarishi Dhanuka

Chemical Physics

Quantum Thermodynamics of Electron Transport along Chains of Redox Centers

Fabrizio Cleri, Ralf Blossey and Stefano Giordano

Intramolecular electron transport in biological systems is typically described as a diffusive hopping process, according to the semi-classical rate theories of Marcus and Hopfield combined with classical Pauli-type master equations. However, the possibility that non-trivial quantum mechanical effects could play a functional role in the transport dynamics in certain biomolecular processes has attracted increasing attention. Here, we extend the quantum mechanical model of open system dynamics by the Lindblad equation to a key biological component, the long chains of redox centers based on iron-sulfur clusters or heme groups that are widespread in many biological organisms, where they realize the cellular respiration. This approach allows to explore a wide range of physical parameters, showing key features of electron transport in these multi-domain protein structures. We pay particular attention to heat and entropy transfer between the electrons and the protein bath, which constitutes a benchmark of physical realism for the models. Electron currents, average transfer times and relative efficiency of the transport process are also explicitly characterized.
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Physics and Society

Edge-based mean-field approximation of dynamics on networks via approximate lumping of Markov chains

Gábor Timár, Jonathan A. Ward and Péter L. Simon

Mean-field approximations for dynamical processes on networks are widely used, but existing derivations often rely either on moment closures or on idealised assumptions about network structure, leaving the nature of the underlying averaging unclear. Here we present a mathematically principled framework for deriving edge-based mean-field approximations for a broad class of Markov processes on networks using approximate lumping. We consider models in which each vertex is in one of a finite number of vertex states and transitions depend on the number of neighbours in each state. Our approach partitions the full Markov chain state space according to the number of vertices and edges in each possible state, and averages transition rates between partitions. This yields density-dependent population processes that, in the limit of large system size, reduce to a low-dimensional system of ordinary differential equations. We demonstrate the method on single graphs and graph ensembles, such as Erdős-Rényi random networks, and show that well-known edge-based mean-field approximations arise as special cases of our approach. Our approximate lumping framework clarifies the nature of the averaging underlying mean-field approximations, providing a basis for future work on assessing their accuracy.
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Instrumentation and Detectors

Computer vision-based neural networks for radioisotope identification in urban environments

Masen Bachleda, Alea Minar, Ayush Panigrahy et al. (+1 more)

Algorithm development for radioisotope identification in mobile urban search scenarios face significant challenges from non-uniform backgrounds, momentary source encounters, and severe class imbalance between rare threat signatures and background measurements. We present a machine learning-based approach to this problem that converts list-mode gamma-ray data into two-dimensional waterfall spectrograms and applies computer vision architectures to the resulting images. Rather than treating waterfalls as conventional images, we employ a representation where consecutive time spectra can form input channels, similar to RGB channels in color images. This representation encodes both spectral and temporal information, enabling neural networks to more effectively learn patterns that distinguish source signatures from background fluctuations. We evaluate three architectures, a multilayer perceptron (MLP), convolutional neural network (CNN), and vision transformer (ViT), on the Radiological Anomaly Detection and Identification (RADAI) benchmark dataset. At a false positive rate of less than one false alarm per hour, our CNN outperforms the previous-best non-negative matrix factorization (NMF) method across all global metrics, achieving true detection, classification, and identification rates of 0.4334, 0.3965, and 0.2950 respectively, compared to 0.4151, 0.3611, and 0.2625 for NMF. At lower false positive rate constraints, the neural network approaches show comparable but ultimately lower performance than NMF, indicating opportunities for further research.
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All authors Masen Bachleda, Alea Minar, Ayush Panigrahy, Peter Lalor

Biological Physics · Cross-listed from q-bio.PE

Demographic senescence as multi-level selection in miniature

Ananda Shikhara Bhat and Hanna Kokko

Multi-level selection and senescence do not at first sight have much in common. Here, we demonstrate that the emergent mortality patterns generated by demographic senescence can be understood as the product of multi-level selection. We formulate a two-level Moran type process and use its scaling limits to illustrate that a simple mathematical framework that models multi-level selection in group-structured populations also models damage accumulation patterns and resultant mortality curves in ageing organisms. To verbally make the connection, observe that defectors spread within a group consisting of cooperators and defectors; when groups compete against each other, defector-rich groups suffer, and between-group selection causes such groups to be systematically under-represented. Exactly analogously, senescing individuals accumulate damage to physiological sub-systems, and `damage begets damage'; individuals who are more damaged are more likely to die, hence damage-rich individuals are systematically under-represented in later age classes. Thus, emergent senescence patterns in complex, integrated organisms are formally equivalent to the patterns generated by a within-generation multi-level selection process in which intra-organismal sub-systems play the role of particles, organisms play the role of collectives, and selective disappearance plays the role of group selection.
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Applied Physics · Cross-listed from eess.SP

ADC-Aware End-to-End Optimization of a Dynamic Metasurface Antenna with Strong Mutual Coupling for Monostatic Scene Classification

Philipp del Hougne

Dynamic metasurface antennas (DMAs) enable programmable wave-domain signal processing that can be jointly optimized with downstream digital processing in an end-to-end manner. Existing studies, however, typically assume ideal analog-to-digital conversion (ADC) and often rely on simplified electromagnetic models. Here, we study ADC-aware end-to-end optimization of a monostatic sensing pipeline based on a DMA with strong mutual coupling (MC). We model the wave domain using an MC-aware multiport-network model whose parameters were experimentally estimated for a fabricated chaotic-cavity-backed DMA with 96 one-bit-programmable meta-elements. We perform ADC-aware end-to-end optimization of the DMA configurations and digital classifier, either with awareness of a fixed uniform ADC or, optionally, with jointly learned ADC decision thresholds, and compare against baselines that assume an ideal ADC and/or ignore MC. Our results show that ADC awareness is essential in low-resolution ADC regimes: with one-bit ADCs and eight DMA configurations, deploying an ideal-ADC-trained system with a uniform one-bit ADC reduces the test accuracy from 95.5% to 56.0%, whereas ADC-aware training with the same fixed uniform one-bit ADC achieves 87.2%. We also show that without MC awareness the accuracy drops to the random-guess level. Learning non-uniform ADC thresholds provides at most modest additional gains over fixed uniform ADCs in the considered DMA-based sensing pipeline.
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Physics and Society

Unequal Access to Power in Corruption Networks: Evidence from Colombia

Giovanna Rodríguez-García, María Elizabeth Mesa-Pineda and José R. Nicolás-Carlock

Corruption is embedded in networks of access, coordination, and protection, yet little is known about how gender shapes actors' positions within them. This article examines whether corruption networks in Colombia's territorial press reproduce gendered patterns of exclusion. Drawing on an access-to-power perspective, we argue that women's lower presence may reflect unequal incorporation into the spaces where exchanges are organized. Empirically, we use Transparencia por Colombia's Radiografía de Hechos de Corrupción, integrating case- and actor-level information to build co-participation networks. We analyze gender differences in composition, position, recurrence, and institutional access to resources. Results show that these networks are strongly masculinized: men dominate actors and ties, and women appear less often among recurrent actors. However, women are not absent from connected or dense areas, suggesting uneven rather than absolute exclusion. The findings shift attention from whether women are ”less corrupt” to how unequal access to power structures participation in corruption networks.
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Atomic Physics

New Bounds on Exotic Long-Range Spin-Spin Interactions

Nathan B. Clayburn, Andrew Glassford, Thomas Uelmen et al. (+8 more)

Many proposed extensions to the Standard Model of particle physics introduce new bosons that can mediate forces which couple to particle spin. Here we describe a search for such forces coupling spin-polarized neutrons and protons in our magnetometer to spin-polarized electrons within Earth. We measure these interactions by varying the orientation of an optical $^{199}$Hg-$^{133}$Cs free-precession comagnetometer mounted upon a precision rotation platform. From these measurements, we establish upper bounds on the dimensionless coupling constants associated with the axial-axial potential $V_2$ and the axial-vector potential $V_{11}$ as a function of the force's range $\lambda$. For the electron-neutron and electron-proton potential $V_2$ at infinite range, we find $|g_A^eg_A^n| \leq 3.0 \times 10^{-48}$ and $|g_A^eg_A^p| \leq 3.0 \times 10^{-47}$. For $V_{11}$, we find our most stringent bounds to be $|g_A^eg_V^n| \leq 2.2 \times 10^{-25}$ and $|g_A^eg_V^p| \leq 2.2 \times 10^{-24}$ at $\lambda \approx 10^3$ km. Our results represent an improvement over previous results by up to a factor of 17 and set the most stringent bounds on long-range axial-axial and axial-vector couplings between electron spins and neutron and proton spins.
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All authors Nathan B. Clayburn, Andrew Glassford, Thomas Uelmen, Ashley R. Kyung, Yana Boneva, Saif Salim, Alison S. Weiss, Flanagan Waldherr, Claire Carlin, Stephen K. Peck, Larry R. Hunter

Fluid Dynamics

A High-Order Arbitrary Lagrangian-Eulerian Discontinuous Galerkin Method for the Boltzmann Equation in Nearly Incompressible Flows

Atakan Aygun, Onur Ata, Tim Warburton et al. (+1 more)

We propose the arbitrary Lagrangian-Eulerian (ALE) form of the Galerkin-Boltzmann formulation for the simulation of nearly incompressible flows with moving boundaries. The continuous Boltzmann equations are mapped to a reference state to compensate the mesh motion with an advection term. The resulting system is discretized in space using the discontinuous Galerkin method on unstructured meshes. A semi-analytic Runge-Kutta time discretization is used to overcome the stiffness introduced by the continuous Boltzmann equations. The well-known geometric conservation law is shown to be satisfied by the time and space discretizations and consistent update of geometric factors of the discretization. The implementation is on the GPU accelerated kernel library libParanumal and validated by a free stream preservation and moving Taylor-Green vortex test cases. Then, the capabilities are shown using a plunging symmetric airfoil in two-dimensions and moving carangiform fish in three-dimensions using perfectly matched layers.
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All authors Atakan Aygun, Onur Ata, Tim Warburton, Ali Karakus

Physics and Society

Synchronization and Swarming of Two-Mode Stochastic Oscillators

Szabolcs Vitus and Ferenc Járai-Szabó

Synchronization and swarming are canonical manifestations of self-organization, observable across scales from cellular processes to animal flocks. This study investigates the collective dynamics of a novel agent-based model where individuals exhibit both spatial mobility and internal, two-mode stochastic oscillatory states. By introducing a local, distance-dependent coupling between the agents' spatial configuration and their internal state transitions, we establish a mutual feedback loop that drives complex pattern formation. Through large-scale numerical simulations, we identify seven distinct morphological configurations, ranging from stationary Filled-disk states to highly disordered Intense-motion regimes. By performing a rigorous quantitative analysis of the rotational energy and radial dispersion, we transcend simple morphological classification and demonstrate that the system organizes into discrete, quantized topological attractors. We derive a macroscopic scaling law, $\Omega \propto r^{-1/2}$, which proves that the emerging rotating states are not rigid-body rotations, but rather composite differential vortex structures characterized by spontaneous chiral symmetry breaking. Our results suggest that these stable, quantized dynamical states are fundamental features of systems governed by bidirectional spatial-phase feedback, offering a robust framework for designing autonomous, decentralized robotic swarms.
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