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Physics Physics

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Wednesday 31 December

Accelerator Physics

Achieving High Efficiency And Enhanced Beam Quality In Laser Wakefield Acceleration

Jia Wang, Ming Zeng, Dazhang Li et al. (+4 more)

Laser wakefield acceleration, characterized by the extremely high electric field gradient exceeding 100GV/m, is regarded as a compact and cost affordable technology for the next generation of particle colliders and light sources. However, it has always been a major challenge to effectively increase the energy transfer efficiency from the laser to the accelerated beam, while ensuring the beam quality remains suitable for practical applications. This study demonstrates that the laser with shorter pulse duration allows for a two-step dechirping process of the accelerated electron beam with charge of nanocoulomb level. The electron beams with an energy spread of 1% can be generated with the energy transfer efficiency of 10% to 30% in a large parameter space. For example, one electron beam with the energy of 420MeV, the charge of 5.5nC and the RMS energy spread of 2% can be produced using an 8.3J laser pulse with 7.2fs duration.
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All authors Jia Wang, Ming Zeng, Dazhang Li, Wentao Wang, Song Li, Ke Feng, Jie Gao

Optics

Scalable ultrafast random bit generation using wideband chaos-based entropy sources

Chin-Hao Tseng, Atsushi Uchida and Sheng-Kwang Hwang

The exponential growth of data transmission and processing speeds in modern digital infrastructure requires entropy sources capable of producing large volumes of true randomness for information security. Chaotic emissions from semiconductor lasers are attractive in this context because of their fast dynamics and nonrepetitive behavior. Their spectral bandwidth, however, is typically limited to several tens of gigahertz, which constrains the achievable entropy rate and makes ultrafast random bit generation difficult without substantial post-processing. Here, we demonstrate a chaos-based entropy source that employs optical heterodyning between the chaotic emission from a semiconductor laser and an optical frequency comb, yielding a bandwidth exceeding 100 GHz and an experimentally verified single-channel entropy rate of 1.86 Tb/s. By directly extracting multiple bits from the digitized output of the entropy source, we achieve a single-channel random bit generation rate of 1.536 Tb/s, while four-channel parallelization reaches 6.144 Tb/s with no observable interchannel correlation. This linear scalability suggests that aggregate throughput could reach hundreds of terabits per second with additional parallel channels. The broadband, low-overhead photonic architecture presented here provides a viable route to real-time, ultrafast random bit generation with broad implications for secure communications, high-performance AI computing, and large-scale data analytics.
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Chemical Physics

Panchromatic Absorbing Materials: Molecular Design and Challenges in Photovoltaic Applications

Hsien-Hsin Chou

Panchromatic absorbing materials are widely regarded as a key strategy for enhancing solar energy utilization and photocurrent generation. However, in artificial molecular systems, broadening the absorption spectrum is often accompanied by fundamental challenges, including bandgap narrowing, poor energy-level alignment, and limited charge-transfer kinetics, indicating that pursuing broadband absorption alone is insufficient to guarantee high photovoltaic performance. This article examines the relationship between design strategies and performance of panchromatic absorbing materials from the perspectives of molecular engineering and photovoltaic devices, with particular emphasis on the delicate balance among molecular electronic structure, charge-transfer characteristics, interfacial energy-level alignment, as well as electron injection, regeneration efficiency, and energy losses. Ultimately, the molecular design of panchromatic photovoltaic materials should move beyond molecular-level optimization toward synergistic tuning among molecules, semiconductors, and electrolytes or active-layer materials, thereby providing concrete conceptual guidance for achieving efficiency optimization rather than simple spectral maximization.
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Chemical Physics

Thermodynamics Reconstructed from Information Theory:An Axiomatic Framework via Information-Volume Constraints and Path-Space KL Divergence

Tatsuaki Tsuruyama

We develop an axiomatic reconstruction of thermodynamics based entirely on two primitive components: a description of what aspects of a system are observed and a reference measure that encodes the underlying descriptive convention. These ingredients define an "information volume" for each observational cell. By incorporating the logarithm of this volume as an additional constraint in a minimum-relative-entropy inference scheme, temperature, chemical potential, and pressure arise as conjugate variables of a single information-theoretic functional. This leads to a Legendre-type structure and a first-law-like relation in which pressure corresponds to information volume rather than geometric volume. For nonequilibrium dynamics, entropy production is characterized through the relative-entropy asymmetry between forward and time-reversed stochastic evolutions. A decomposition using observational entropy then separates total dissipation into system and environment contributions. Heat is defined as the part of dissipation not accounted for by the system-entropy change, yielding a representation that does not rely on local detailed balance or a specific bath model. We further show that the difference between joint and partially observed dissipation equals the average of conditional relative entropies, providing a unified interpretation of hidden dissipation and information-flow terms as projection-induced gaps.
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Chemical Physics · Cross-listed from cs.LG

Diagnosing Heteroskedasticity and Resolving Multicollinearity Paradoxes in Physicochemical Property Prediction

Malikussaid, Septian Caesar Floresko, Ade Romadhony et al. (+3 more)

Lipophilicity (logP) prediction remains central to drug discovery, yet linear regression models for this task frequently violate statistical assumptions in ways that invalidate their reported performance metrics. We analyzed 426,850 bioactive molecules from a rigorously curated intersection of PubChem, ChEMBL, and eMolecules databases, revealing severe heteroskedasticity in linear models predicting computed logP values (XLOGP3): residual variance increases 4.2-fold in lipophilic regions (logP greater than 5) compared to balanced regions (logP 2 to 4). Classical remediation strategies (Weighted Least Squares and Box-Cox transformation) failed to resolve this violation (Breusch-Pagan p-value less than 0.0001 for all variants). Tree-based ensemble methods (Random Forest R-squared of 0.764, XGBoost R-squared of 0.765) proved inherently robust to heteroskedasticity while delivering superior predictive performance. SHAP analysis resolved a critical multicollinearity paradox: despite a weak bivariate correlation of 0.146, molecular weight emerged as the single most important predictor (mean absolute SHAP value of 0.573), with its effect suppressed in simple correlations by confounding with topological polar surface area (TPSA). These findings demonstrate that standard linear models face fundamental challenges for computed lipophilicity prediction and provide a principled framework for interpreting ensemble models in QSAR applications.
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All authors Malikussaid, Septian Caesar Floresko, Ade Romadhony, Isman Kurniawan, Warih Maharani, Hilal Hudan Nuha

Optics

Pathway to Optical-Cycle Dynamic Photonics: Extreme Electron Temperatures in Transparent Conducting Oxides

Jae Ik Choi, Vahagn Mkhitaryan, Colton Fruhling et al. (+4 more)

We theoretically demonstrate that transparent conducting oxides (TCOs) exhibit oscillatory and sign-reversing dynamic modulation in transmittance on the order of a few optical cycles under extreme electron temperatures, providing a possible explanation for TCO dynamics observed in earlier experiments. We present an inverse-designed multilayer cavity incorporating an ultrathin TCO layer, which supports an oscillatory optical response more pronounced than those previously observed experimentally in TCOs. This approach yields transmittance oscillations with a characteristic period of ~20fs, which corresponds to approximately four optical cycles of the 1.431{\mu}m probe beam. To achieve a similar oscillatory modulation in {\Delta}n, we incorporate a TCO electron-acceptor layer on top of the inverse-designed cavity, enabling thermionic carrier injection at the TCO junction. The resulting acceptor layer exhibits a striking {\Delta}n response as fast as 20fs, corresponding to only three optical cycles of the 1.8-2.0{\mu}m probe, and can be further tailored into the sub-optical-cycle regime. The findings could both clarify the previously unexplained transient dynamics in TCOs and, for the first time, demonstrate the critical role of electron temperatures in driving oscillatory dynamic responses.
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All authors Jae Ik Choi, Vahagn Mkhitaryan, Colton Fruhling, Jacob B. Khurgin, Alexander V. Kildishev, Vladimir M. Shalaev, Alexandra Boltasseva

Applied Physics · Cross-listed from quant-ph

Volcano Architecture for Scalable Quantum Processor Units

Dong-Qi Ma, Qing-Xuan Jie, Ya-Dong Hu et al. (+12 more)

Quantum information processing platforms based on array of matter qubits, such as neutral atoms, trapped ions, and quantum dots, face significant challenges in scalable addressing and readout as system sizes increase. Here, we propose the "Volcano" architecture that establishes a new quantum processing unit implementation method based on optical channel mapping on a arbitrarily arranged static qubit array. To support the feasibility of Volcano architecture, we show a proof-of-principle demonstration by employing a photonic chip that leverages custom-designed three-dimensional waveguide structures to transform one-dimensional beam arrays into arbitrary two-dimensional output patterns matching qubit array geometries. We demonstrate parallel and independent control of 49-channel with negligible crosstalk and high uniformity. This architecture addresses the challenges in scaling up quantum processors, including both the classical link for parallel qubit control and the quantum link for efficient photon collection, and holds the potential for interfacing with neutral atom arrays and trapped ion crystals, as well as networking of heterogeneous quantum systems.
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All authors Dong-Qi Ma, Qing-Xuan Jie, Ya-Dong Hu, Wen-Yi Zhu, Yi-Chen Zhang, Hong-Jie Fan, Xiao-Kang Zhong, Guang-Jie Chen, Yan-Lei Zhang, Tian-Yang Zhang, Xi-Feng Ren, Liang Chen, Zhu-Bo Wang, Guang-Can Guo, Chang-Ling Zou

Atomic Physics

ExoAtom: A Database of Atomic Spectra in ExoMol Format

Qing-He Ni, Rujia Wang, Tianyang Xie et al. (+4 more)

We present the ExoAtom database, www.exomol.com/exoatom, an extension of the ExoMol database to provide atomic line lists in the ExoMol format. ExoAtom is designed for detailed astrophysical, planetary, and laboratory applications. ExoAtom currently includes atomic data for 80 neutral atoms and 74 singly charged ions. These data are extracted from both the NIST and Kurucz databases, with 79/71 atoms/ions sourced from NIST and 38/37 atoms/ions sourced from Kurucz. ExoAtom uses the file types .all, .def, .states, .trans and .pf as fundamental components for structuring atomic data in a consistent hierarchy. The .states file contains quantum numbers, uncertainties, lifetimes, etc. The .trans file specifies Einstein A coefficients and their associated wavenumbers. The .pf file provides partition functions over a wide grid of temperatures. Post-processing of the ExoAtom data is provided by the program PyExoCross. Future development of ExoAtom will include additional ionization stages.
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All authors Qing-He Ni, Rujia Wang, Tianyang Xie, Jingxin Zhang, Christian Hill, Sergei N. Yurchenko, Jonathan Tennyson

Optics · Cross-listed from quant-ph

Band Structure and Dynamics of Single Photons in Atomic Lattices

Wenxuan Xie and John C. Schotland

We present a framework to investigate the collective properties of atomic lattices in one, two, and three dimensions. We analyze the single-photon band structure and associated atomic decay rates, revealing a fundamental dependence on dimensionality. One- and two-dimensional arrays are shown to be inherently radiative, exhibiting band gaps and decay rates that oscillate between superradiant and subradiant regimes, as a function of lattice spacing. In contrast, three-dimensional lattices are found to be fundamentally non-radiative due to the inhibition of spontaneous emission, with decay only at discrete Bragg resonances. Furthermore, we demonstrate that this structural difference dictates the system dynamics, which crosses over from dissipative decay in lower dimensions to coherent transport in three dimensions. Our results provide insight into cooperative effects in atomic arrays at the single-photon level.
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Biological Physics · Cross-listed from cond-mat.soft

Dynamics of Interfacial Diffusion Control in Amphiphilic Lipid-Coated Micro-Particles for Stochastic Release Systems

Joonggwon Kim and Dogyun Byeon

The release of hydrophilic solutes from micron scale particulate formulations can be understood as an interfacial transport problem in which diffusion across a heterogeneous amphiphilic coating competes with dissolution and convective removal in the surrounding medium. Here we reinterpret a glycerin fatty acid ester (GFAE) coated thiamine (vitamin B1) micro particle formulation as a condensed matter system: a soft matter core shell geometry whose effective permeability is set by the nanoscale organization of amphiphilic lipids at the interface. Using in vivo time course serum measurements in mice as a proxy for a stochastic sink, we compare the coated formulation (UTEV) with a composition matched uncoated comparator (UMFG). Early time systemic appearance is similar, whereas late time levels are enhanced for the coated particles, implying a reduced effective interfacial diffusivity and a broadened release-time distribution. We discuss the results in terms of diffusion barrier physics, heterogeneous interfacial energetics, and coarse grained transport models that map microstructural coating parameters to macroscopic persistence (AUC).
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Plasma Physics

Characterizing Flux-Surface Shapes in Tokamaks and Quasi-Symmetric Stellarators

M.J. Gerard, M.J. Pueschel, S. Stewart et al. (+2 more)

Modern stellarator designs routinely attain high levels of magnetic-field quasi-symmetry through flux-surface shaping. Here, we examine different methods for characterizing stellarator flux-surface shapes in a manner analogous to flux-surface shaping in tokamaks. The methods considered use a Fourier analysis to define the shaping modes (e.g. elongation, triangularity, squareness, etc.) of equilibrium cross-sections. Relative to an axisymmetric equilibrium, the additional degree of freedom in a non-axisymmetric equilibrium manifests as a rotation of each shaping mode about the magnetic axis. This analysis is performed on non-axisymmetric configurations with a high degree of quasi-symmetry and equilibria with varying quasi-symmetry quality from the QUASR database. One method in particular is shown to reduce shape complexity in quasi-symmetric equilibria by defining a set of cross-sections that efficiently fill out an equilibrium volume. This is accomplished by defining a cross-section as the set of points that occupy the shortest distance between the magnetic axis and an equilibrium flux-surface across all quasi-symmetry contours. Using this method, we find empirically an equilibrium geometry can be described with significantly fewer non-negligible shaping modes relative to other shape characterization methods. Moreover, the method reveals that quasi-symmetry quality is strongly correlated with equilibrium shapes that exhibit a highly constrained linear distribution of shaping modes, where an increase in shape complexity is proportional to an increase in shape rotation about the magnetic axis. It is therefore argued this method provides a way to efficiently characterize the shape of quasi-symmetric equilibria in a manner analogous to how equilibrium shapes are described in tokamaks.
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All authors M.J. Gerard, M.J. Pueschel, S. Stewart, H.O.M. Hillebrecht, B. Geiger

Chemical Physics

Imaging nanoscale photocarrier traps in solar water-splitting catalysts

Levi D. Palmer, Wonseok Lee, Pushp Raj Prasad et al. (+12 more)

Defects trap photocarriers and hinder solar water splitting. The nanoscale photocarrier transport, trapping, and recombination mechanisms are usually inferred from ensemble-averaged measurements and remain elusive. Because an individual high-performing nanoparticle photocatalyst may outperform the ensemble average, design rules that would otherwise enhance catalytic efficiency remain unclear. Here, we introduce photomodulated electron energy-loss spectroscopy (EELS) in an optically coupled scanning transmission electron microscope (STEM) to map photocarrier localization. Using rhodium-doped strontium titanate (SrTiO3:Rh) solar water-splitting nanoparticles, we directly image the carrier densities concentrated at oxygen-vacancy surface trap states. This is achieved by separating photothermal heating from photocarrier populations through experimental and computational analyses of low-loss spectra. Photomodulated STEM-EELS enables angstrom-scale imaging of defect-induced photocarrier traps and their impact on photocatalytic efficiency.
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All authors Levi D. Palmer, Wonseok Lee, Pushp Raj Prasad, Bradley W. Layne, Han-Hsuan Wu, Zejie Chen, Jianguo Wen, Yuzi Liu, Xiaoqing Pan, A. Alec Talin, Akihiko Kudo, Shane Ardo, Joseph P. Patterson, Thomas E. Gage, Scott K. Cushing

Computational Physics

BF-APNN: A Low-Memory Method for Accelerating the Solution of Radiative Transfer Equations

Xizhe Xie, Wengu Chen, Weiming Li et al. (+2 more)

The Radiative Transfer Equations (RTEs) exhibit high dimensionality and multiscale characteristics, rendering conventional numerical methods computationally intensive. Existing deep learning methods perform well in low-dimensional or linear RTEs, but still face many challenges with high-dimensional or nonlinear RTEs. To overcome these challenges, we propose the Basis Function Asymptotically Preserving Neural Network (BF-APNN), a framework that inherits the advantages of Radiative Transfer Asymptotically Preserving Neural Network (RT-APNN) and accelerates the solution process. By employing basis function expansion on the microscopic component, derived from micro-macro decomposition, BF-APNN effectively mitigates the computational burden associated with evaluating high-dimensional integrals during training. Numerical experiments, which involve challenging RTE scenarios featuring, nonlinearity, discontinuities, and multiscale behavior, demonstrate that BF-APNN substantially reduces training time compared to RT-APNN while preserving high solution accuracy. Moreover, BF-APNN exhibits superior performance in addressing complex, high-dimensional RTE problems, underscoring its potential as a robust tool for radiative transfer computations.
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All authors Xizhe Xie, Wengu Chen, Weiming Li, Peng Song, Han Wang

Medical Physics

Towards Interpretable AI in Personalized Medicine: A Radiological-Biological Radiomics Dictionary Connecting Semantic Lung-RADS and imaging Radiomics Features; Dictionary LC 1.0

Ali Fathi Jouzdani, Shahram Taeb, Mehdi Maghsudi et al. (+3 more)

Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival strongly dependent on early detection. Standard-dose computed tomography (CT) screening using the Lung Imaging Reporting and Data System (Lung-RADS) standardizes pulmonary nodule assessment but is limited by inter-reader variability and reliance on qualitative descriptors, while radiomics offers quantitative biomarkers that often lack clinical interpretability. To bridge this gap, we propose a radiological-biological dictionary that aligns radiomic features (RFs) with Lung-RADS semantic categories. A clinically informed dictionary translating ten Lung-RADS descriptors into radiomic proxies was developed through literature curation and validated by eight expert reviewers. As a proof of concept, imaging and clinical data from 977 patients across 12 collections in The Cancer Imaging Archive (TCIA) were analyzed; following preprocessing and manual segmentation, 110 RFs per nodule were extracted using PyRadiomics in compliance with the Image Biomarker Standardization Initiative (IBSI). A semi-supervised learning framework incorporating 499 labeled and 478 unlabeled cases was applied to improve generalizability, evaluating seven feature selection methods and ten interpretable classifiers. The optimal pipeline (ANOVA feature selection with a support vector machine) achieved a mean validation accuracy of 0.79. SHapley Additive exPlanations (SHAP) analysis identified key RFs corresponding to Lung-RADS semantics such as attenuation, margin irregularity, and spiculation, supporting the validity of the proposed mapping. Overall, this dictionary provides an interpretable framework linking radiomics and Lung-RADS semantics, advancing explainable artificial intelligence for CT-based lung cancer screening.
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All authors Ali Fathi Jouzdani, Shahram Taeb, Mehdi Maghsudi, Arman Gorji, Arman Rahmim, Mohammad R. Salmanpour

Atmospheric and Oceanic Physics

Improved rainfall forecasts in daily use over East Africa

Fenwick C. Cooper, Shruti Nath, Andrew T. T. McRae et al. (+21 more)

Ensemble forecasting has proven to be a vital tool for predicting extreme, life-threatening or only partially predictable weather events. As well as providing probabilistic products, individual ensemble members provide context and realisations of possible extreme weather. However, many National Meteorological Services in East Africa lack the computing resources required to run their local area models in ensemble mode over the full period of the two-week medium range. In this paper we test the performance of a short and medium-range forecast system recently put into daily use, comprising the global ECMWF ensemble forecast, post-processed using cGAN, a neural network model. We find that cGAN achieves probabilistic forecast skill comparable to that obtained using isotonic distributional regression (IDR), a recent statistical post-processing method, applied to either the ECMWF ensemble or the machine-learning models FuXi and GraphCast. Unlike IDR, cGAN generates individual ensemble-member realisations (potentially many thousands), including exceptional weather events. This ability was deemed important when assessed in an operational context by the National Meteorological and Hydrological Services of Kenya and Ethiopia. cGAN is cheap to train/run and requires no additional post-processing. It is run on laptops making it practical as a high-resolution medium-range ensemble forecast system for Meteorological Services with limited computational resources.
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All authors Fenwick C. Cooper, Shruti Nath, Andrew T. T. McRae, Bobby Antonio, Matthew Wright, Antje Weisheimer, Tim Palmer, Masilin Gudoshava, Nishadh Kalladath, Ahmed Amidhun, Jason Kinyua, Hannah Kimani, David Koros, Zacharia Mwai, Christine Maswi, Benard Chanzu, Samrawit Abebe, Bekalu Tamene, Bekele Kebebe, Asaminew Teshome, Florian Pappenberger, Matthew Chantry, Isaac Obai, Jesse Mason

Tuesday 30 December

Space Physics

First Multi-Constellation Observations of Navigation Satellite Signals in the Lunar Domain by Post-Processing L1/L5 IQ Snapshots

Lorenzo Sciacca, Alex Minetto, Andrea Nardin et al. (+5 more)

The use of Global Navigation Satellite Systems (GNSS) to increase spacecraft autonomy for orbit determination has gained renewed momentum following the Lunar GNSS Receiver Experiment (LuGRE), which demonstrated feasible onboard GPS and Galileo signal reception and tracking at lunar distances. This work processes in-phase and quadrature (IQ) snapshots collected by the LuGRE receiver in cis-lunar space and on the lunar surface to assess multi-frequency, multi-constellation signal availability. Signals from additional systems beyond GPS and Galileo, including RNSS and SBAS constellations, are observable and successfully acquired exclusively in the recorded IQ snapshots. These observations provide the first experimental evidence that signals from multiple constellations, including systems not supported by LuGRE realtime operations, are detectable at unprecedented distances from Earth. Useful observables can be extracted from the IQ snapshots, despite minimal sampling rates, 4-bit quantization, and short durations (200 ms-2 s), through a hybrid coherent/non-coherent acquisition stage compensating for code Doppler. These observations are exploited to tune simulation tools and to perform extended simulation campaigns, showing that the inclusion of additional constellations significantly improves availability; for a 26 dB-Hz acquisition threshold, the fraction of epochs with at least four visible satellites increases from 11% to 46% of the total epoch count. These findings indicate that BeiDou, RNSS, and SBAS signals can substantially enhance GNSS-based autonomy for lunar and cislunar missions.
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All authors Lorenzo Sciacca, Alex Minetto, Andrea Nardin, Fabio Dovis, Luca Canzian, Mario Musmeci, Claudia Facchinetti, Giancarlo Varacalli

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

Fluoride doping into SiO2-MgO-CaO bioactive glass nanoparticles: bioactivity, biodegradation and biocompatibility assessments

N. Esmati, T. Khodaei, E. Salahinejad et al. (+1 more)

In this research, for the first time, the structure, bioactivity, biodegradation and biocompatibility of SiO2-MgO-CaO glasses doped with different levels of fluoride were studied. The glassy powder samples were synthesized by a coprecipitation method followed by calcination at 500 C, where amorphicity and fluoride incorporation were verified by X-ray diffraction and Raman spectroscopy, respectively. The in vitro biomineralization and biodegradation of the samples were also investigated by electron microscopy, Raman spectroscopy and inductively coupled plasma optical emission spectrometry. These assessments revealed that there is an optimum level of fluoride doping to meet the highest bioactivity. Remarkably, the same level of incorporation presented the foremost biocompatibility with respect to osteoblast-like MG-63 human cells, as realized by the MTT assay and cell attachment studies.
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All authors N. Esmati, T. Khodaei, E. Salahinejad, E. Sharifi

Physics and Society

Prediction of airport on-time performance

Xavier Lemay and Fabian Bastin

We investigate the factors contributing to departure and arrival delays at a major international airport and develop predictive models to estimate both the likelihood and duration of delays. Using logistic regression, random forest, and gradient boosting methods, we identify key predictors of flight punctuality, including historical delay rates of flight numbers and airlines, weather conditions, runway traffic, walk time from security to gate, and overall airport congestion. Our models achieve strong inference and predictive performance in both classification and regression tasks, demonstrating the potential for targeted operational interventions to improve on-time performance and providing actionable insights for airport management and airline operations.
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Computational Physics · Cross-listed from cs.LG

LLMize: A Framework for Large Language Model-Based Numerical Optimization

M. Rizki Oktavian

Large language models (LLMs) have recently shown strong reasoning capabilities beyond traditional language tasks, motivating their use for numerical optimization. This paper presents LLMize, an open-source Python framework that enables LLM-driven optimization through iterative prompting and in-context learning. LLMize formulates optimization as a black-box process in which candidate solutions are generated in natural language, evaluated by an external objective function, and refined over successive iterations using solution-score feedback. The framework supports multiple optimization strategies, including Optimization by Prompting (OPRO) and hybrid LLM-based methods inspired by evolutionary algorithms and simulated annealing. A key advantage of LLMize is the ability to inject constraints, rules, and domain knowledge directly through natural language descriptions, allowing practitioners to define complex optimization problems without requiring expertise in mathematical programming or metaheuristic design. LLMize is evaluated on convex optimization, linear programming, the Traveling Salesman Problem, neural network hyperparameter tuning, and nuclear fuel lattice optimization. Results show that while LLM-based optimization is not competitive with classical solvers for simple problems, it provides a practical and accessible approach for complex, domain-specific tasks where constraints and heuristics are difficult to formalize.
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Instrumentation and Detectors

Robust Lifetime Estimation from HPGe Radiation-Sensor Time Series Using Pairwise Ratios and MFV Statistics

Victor V. Golovko

High-purity germanium (HPGe) gamma-ray detectors are core instruments in nuclear physics and astrophysics experiments, where long-term stability and reliable extraction of decay parameters are essential. However, the standard exponential decay analyses of the detector time-series data are often affected by the strong correlations between the fitted parameters and the sensitivity to detector-related fluctuations and outliers. In this study, we present a robust analysis framework for HPGe detector decay data based on pairwise ratios and the Steiner's most frequent value (MFV) statistic. By forming point-to-point ratios of background-subtracted net counts, the dependence on the absolute detector response is eliminated, removing the amplitude-lifetime correlation inherent to conventional regression. The resulting pairwise lifetime estimates exhibit heavy-tailed behavior, which is efficiently summarized using the MFV, a robust estimator designed for such distributions. For the case study, a long and stable dataset from an HPGe detector was used. This data was gathered during a low-temperature nuclear physics experiment focused on observing the 216 keV gamma-ray line in 97-Ru. Using measurements spanning approximately 10 half-lives, we obtain a mean lifetime of tau = 4.0959 +/- 0.0007 (stat) +/- 0.0110 (syst) d, corresponding to a half-life of T1/2 = 2.8391 +/- 0.0005 (stat) +/- 0.0076 (syst) d. These results demonstrate that the pairwise-MFV approach provides a robust and reproducible tool for analyzing long-duration HPGe detector data in nuclear physics and nuclear astrophysics experiments, particularly for precision decay measurements, detector-stability studies, and low-background monitoring.
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Physics and Society

Deep versus Broad Technology Search and the Timing of Innovation Impact

Likun Cao and James Evans

This study offers a new perspective on the depth-versus-breadth debate in innovation strategy, by modeling inventive search within dynamic collective knowledge systems, and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns in impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of complex recombination structures, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become "locked-in" with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.
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Instrumentation and Detectors

Identification and online monitoring of experimental measurement states via Cuscore statistic

Jichao Zhang, Baohua Sun, Isao Tanihata et al. (+1 more)

We present a statistical method for detecting and analysing state changes in experimental measurements using the Cuscore statistic and its special case, the Centred Cuscore statistic. These statistics are designed to identify deviations in detector responses using sequential hypothesis testing relative to a defined reference state. Applications to charge-changing reaction experiments at the FRagment Separator facility at the GSI Helmholtz Centre for Heavy Ion Research, Germany, and the Second Radioactive Ion Beam Line in Lanzhou at the Institute of Modern Physics, China, demonstrate the ability of these tools to quantify state changes, identify the change point, and classify data segments based on measured states. For long-term online monitoring, we use the exponentially weighted moving average to continuously update computations, enabling the detection of successive changes. This method supports both real-time and post-experiment diagnostics and provides a robust approach for enhancing data integrity and experimental control in nuclear physics and related fields.
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All authors Jichao Zhang, Baohua Sun, Isao Tanihata, Zilun Shen

Applied Physics · Cross-listed from cs.LG

Path Integral Solution for Dissipative Generative Dynamics

Xidi Wang

Can purely mechanical systems generate intelligent language? We prove that dissipative quantum dynamics with analytically tractable non-local context aggregation produce coherent text generation, while conservation laws cause fundamental failure. Employing Koopman operators with closed-form path integral propagators, we show irreversible computation fundamentally requires both controlled information dissipation and causal context aggregation. Spectral analysis reveals emergent eigenvalue structure, separating into decay modes (forgetting), growth modes (amplification), and neutral modes (preservation) – the essential ingredients for directed information flow. Hamiltonian constraints force the elimination of these dissipative modes and degrading performance despite unchanged model capacity. This establishes language generation as dissipative quantum field theory, proving mechanical systems acquire intelligence through the combination of dissipation and non-locality, not through conservation.
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Classical Physics

Electrostatics of a finite-thickness conducting cylindrical shell: coupled elliptic-kernel integral equations

J. Ricardo de Sousa

We develop an exact electrostatic formulation for a finite-length conducting cylindrical shell of finite thickness separating two dielectric media with arbitrary permittivity contrast. The boundary-value problem is reduced to a coupled system of singular integral equations with elliptic kernels governing the induced surface-charge densities on the inner and outer faces. High-accuracy numerical solutions are combined with a systematic asymptotic analysis that elucidates the interplay between geometry, thickness, and dielectric contrast. All classical limiting regimes are recovered, including the slender-body limit, the short-cylinder (ring-like) asymptote, and the thick-shell regime dominated by the outer surface. We demonstrate that the logarithmic short-cylinder behavior of zero-thickness models is a singular feature, which is regularized for any finite thickness, giving rise instead to a finite capacitance plateau. The asymptotic structure of the coupled equations explains both the electrostatic decoupling of the inner cavity in the thick-shell limit and the redistribution of charge between the two surfaces. The results provide exact benchmarks for finite cylindrical conductors, bridging classical analytical treatments and modern numerical approaches, and furnish a high-accuracy reference solution for the validation of axisymmetric electrostatic solvers.
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Optics

Exploring Spectral Singularities and Topological Lasers in PT-Symmetric Weyl Semimetals

Arda Sevinc, Rama Alassadi and Mustafa Sarisaman

This paper investigates the unique properties of PT-symmetric Topological Weyl Semimetals (TWS) within the framework of non-Hermitian physics, focusing on their potential for generating topological lasers. By exploring the role of spectral singularities and their relationship to exceptional points, we examine how these materials, characterized by Weyl nodes and topologically protected surface states, can support novel optical phenomena such as unidirectional propagation and enhanced lasing. Through a theoretical model based on the transfer matrix approach, we reveal how the interplay between the PT symmetry and the axion term introduces new dynamics, leading to 12 distinct topological laser configurations. The study also investigates the impact of the $\theta$-term on spectral singularities, showing how it quantizes the system's gain values and influences the topological properties of the lasers. By applying our model to the TaAs material, a known Weyl semimetal, we uncover previously unreported effects, demonstrating the potential of PT-symmetric TWS materials for advanced optoelectronic applications. We show that the axion-induced cyclotron-like Hall current in a PT-symmetric TWS medium, revealing its topological characteristics and distinct flow patterns in the gain and loss regions, which serve as indicators of the system's topological symmetry. Our findings open new avenues for the development of robust, tunable, and efficient topological lasers with applications in quantum information processing and beyond.
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Popular Physics

Causality in Physics: From Galileo to Einstein, and Beyond

Alessandro De Angelis

Causality is one of the most fundamental – and yet elusive – concepts in physics. From its intuitive role in everyday experience to its formal and often implicit role in scientific theories, causality has challenged philosophers and physicists alike. In what follows, we take a brief historical and conceptual journey through classical and modern physics, tracing how causality has been treated, questioned, or protected in successive physical frameworks – from Galilean mechanics to Newtonian dynamics, from Lagrangian and Hamiltonian formulations to special and general relativity, and finally to quantum mechanics and statistical physics. Our aim is to show how the notion of causality has repeatedly receded into the background of our most successful theories, even when it appears to be central to our everyday understanding of the world.
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Classical Physics

Transpositional rule for constrained systems

Federico Talamucci

This paper investigates the dynamics of nonholonomic mechanical systems, focusing on fundamental variational assumptions and the role of the transpositional rule. We analyze how the Cetaev condition and the first variation of constraints define compatible virtual displacements for systems subject to kinematic constraints, including those nonlinear in generalized velocities. The study explores the necessary conditions for commutation relations to hold, clarifying their impact on the consistency of the derived equations of motion. By detailing the interplay between these variational identities and the Lagrangian derivatives of constraint functions, we elucidate the differences between equations of motion formulated via the d'Alembert–Lagrange principle and those obtained from extended time-integral variational principles. This work aims to provide a clearer theoretical framework for applying these core principles to nonholonomic dynamics.
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Classical Physics

Electrostatics of a Finite Conducting Cylinder: Elliptic-Kernel Integral Equation and Capacitance Asymptotics

J. Ricardo de Sousa

We study the electrostatics of a thin, finite-length conducting cylindrical shell held at constant potential V0. Exploiting axial symmetry, we recast the problem as a one-dimensional singular integral equation for the axial surface-charge density, with a kernel written in terms of complete elliptic integrals. A Chebyshev-weighted collocation scheme that incorporates the square-root edge singularity yields rapidly convergent charge profiles and dimensionless capacitances for arbitrary aspect ratios L/a, recovering known long- and short-cylinder limits and providing accurate benchmark values in the intermediate regime. The method offers a compact, numerically robust reference formulation for the electrostatics of finite cylindrical conductors.
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Optics

Polarization-Differential Loss Enabled High Polarization Extinction in Hollow-Core Fibers

Yizhi Sun, Shoufei Gao, Xiangqi Wang et al. (+5 more)

Delivering a well defined state of polarization over hollow core fibres (HCFs) is pivotal for next generation ultra stable photonic systems. Yet in all existing HCFs, whether birefringent or not, their polarization extinction ratio (PER) rapidly deteriorates during propagation or under mechanical disturbance, leaving no practical high and stable PER solution. Here, we break this impasse by embedding a polarization differential loss (PDL) mechanism directly into the cladding architecture.
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All authors Yizhi Sun, Shoufei Gao, Xiangqi Wang, Xianhao Qi, Zhixi Liang, Rui He, Wei Ding, Yingying Wang

Medical Physics

A novel Boltzmann equation solver for calculation of dose and fluence spectra distributions for proton beam therapy

Oleg N Vassiliev and Radhe Mohan

The claim that Monte Carlo is the most accurate method is a case of misattributed credit. This claim is based on experience with advanced systems MCNPX, Geant4 and EGS. These systems achieve remarkable performance because they use most accurate physics, not because they use random numbers. The latter simplifies algorithms, but contaminates the solution with random noise. Currently prevalent fast Monte Carlo algorithms retain this worst part while achieving high computing speed at the expense of the best part- accurate physics. We employ an opposite strategy. We develop a Boltzmann solver for protons that retains unchanged the physics of most advanced Monte Carlo systems. We eliminate random noise, because our solution method is deterministic. Our method is also applicable to heavier ions, helium and carbon, for example. Results of the study provide a foundation for achieving a high computing speed with uncompromised accuracy in proton treatment planning systems.
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