Appendix A: Statistical methods
The following sections are included: Introduction
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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A Problem in the Statistical Description of Beta-Delayed Neutron Emission
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Physics - Nuclear physics and radiation physics↗
Bayesian inference for plasmonic nanometrology
Here, we introduce a Bayesian method for the characterization of plasmonic nanoparticles, which is applicable to both near- and far-field problems. Designed to combine data generated from any photon-plasmon interaction experiment with physically motivated theoretical models, our approach leverages state-of-the-art Markov chain Monte Carlo sampling techniques and returns parameter estimates on nanometric scales. Simulated spectral data sets, describing resonant scattering of photons from ellipsoidal and toroidal nanoparticles, are explored as concrete examples of our approach, with the resulting Bayesian estimates showing excellent agreement with the ground truth, even under conditions of high statistical noise. By incorporating Bayes factors into the method as well, we reveal how model selection can determine which one of competing geometric shapes better explains the observed data. Our comprehensive nanometrology procedure can be tailored to a variety of light-particle interaction models, and its reliance on Bayesian inference furnishes automatic uncertainty quantification. In addition to applicability to a host of plasmonic configurations such as nanoparticle dimers, trimers, and array studies, it is proposed that the presented analysis can be extended to the quantum regime, where nonclassical photon statistics may provide additional insight for inference of scatterer properties.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Galaxy interactions and strength of nuclear activity
Analysis of data in the literature for differential velocities and projected separations of nearby Seyfert galaxies with possible companions shows a clear difference in projected separations between type 1's and type 2's. This kinematic difference between the two activity classes reinforces other independent evidence that their different nuclear characteristics are related to a non-nuclear physical distinction between the two classes. The differential velocities and projected separations of the galaxy pairs in this sample yield mean galaxy masses, sizes, and mass to light ratios which are consistent with those found by the statistical methods of Karachentsev. Although the galaxy sample discussed here is too small and too poorly defined to provide robust support for these conclusions, the results strongly suggest that nuclear activity in Seyfert galaxies is associated with gravitational perturbations from companion galaxies, and that there are physical distinctions between the host companions of Seyfert 1 and Seyfert 2 nuclei which may depend both on the environment and the structure of the host galaxy itself.
Simkin, S. M.↗
Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance
In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.
Reinforcement learning↗
Parametric interactions in presence of different size colloids in semiconductor quantum plasmas
Present work is an attempt to investigate the effect of different size colloids on parametric interaction in semiconductor quantum plasma. Inclusion of quantum effect is being done in this analysis through quantum correction term in classical hydrodynamic model of homogeneous semiconductor plasma. The effect is associated with purely quantum origin using quantum Bohm potential and quantum statistics. Colloidal size and quantum correction term modify the parametric dispersion characteristics of ion implanted semiconductor plasma medium. It is found that quantum effect on colloids is inversely proportional to their size. Moreover critical size of implanted colloids for the effective quantum correction is determined which is found to be equal to the lattice spacing of the crystal.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Regularly arranged indium islands on glass/molybdenum substrates upon femtosecond laser and physical vapor deposition processing
A bottom-up approach is presented for the production of arrays of indium islands on a molybdenum layer on glass, which can serve as micro-sized precursors for indium compounds such as copper-indium-gallium-diselenide used in photovoltaics. Femtosecond laser ablation of glass and a subsequent deposition of a molybdenum film or direct laser processing of the molybdenum film both allow the preferential nucleation and growth of indium islands at the predefined locations in a following indium-based physical vapor deposition (PVD) process. A proper choice of laser and deposition parameters ensures the controlled growth of indium islands exclusively at the laser ablated spots. Based on a statistical analysis, these results are compared to the non-structured molybdenum surface, leading to randomly grown indium islands after PVD.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material
Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.
36 MATERIALS SCIENCE↗
Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation
Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. Here, this work can also have application to other physical models which produce coherent structures.
54 ENVIRONMENTAL SCIENCES↗
β-cyclodextrin-enhanced Spectrofluorometric Methods for the Determination of Substituted Benzamides (Itopride Hydrochloride, Alizapride, and Mosapride Citrate) in Pharmaceutical Preparations
Two spectrofluorometric methods are developed and optimized for the determination of substituted benz amides, namely itopride hydrochloride (ITO), alizapride (ALI), and mosapride citrate (MOS), in drug substances and pharmaceutical formulations. The first method is based on the measurement of the relative fluorescence intensity of the cited drugs in the absence of methyl-β-cyclodextrin at 380 ± 2 nm using an excitation wavelength of 310 nm in an aqueous solution. The linearity ranges were found to be 0.05–10, 0.1–10, and 0.1–10 μg/mL, with mean recoveries of 100.04% ± 0.21, 100.06% ± 0.65, and 100.08% ± 0.26 for ITO, ALI, and MOS, respectively. The second method is based on the significant enhancement of the fluorescence intensities of the drugs produced through complex formation with methyl-β-cyclodextrin in an aqueous solution at pH 6.6 in the case of ITO and pH 7.2 in the case of ALI and MOS. The fluorescence intensities of the formed complexes were measured at 420 ± 2 nm using an excitation wavelength of 310 nm. The linearity ranges were found to be 5–35, 3–15, and 2–30 ng/mL, with mean recoveries of 100.22 ± 30, 100.12 ± 0.24, and 100.30 ± 0.44, and limits of detection of 1.98, 1.32, and 0.66 ng/mL for ITO, ALI, and MOS, respectively. 1:1 stoichiometries for β -CD-drugs complexes were established. Association constants and Gibbs free energy were calculated by applying the Benesi–Hil de brand equation. The results were found to agree statistically with those obtained from the reference methods.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
On the Local Equivalence Between the Canonical and the Microcanonical Ensembles for Quantum Spin Systems
We study a quantum spin system on the d-dimensional hypercubic lattice Λ with N=L{sup d} sites with periodic boundary conditions. We take an arbitrary translation invariant short-ranged Hamiltonian. For this system, we consider both the canonical ensemble with inverse temperature β{sub 0} and the microcanonical ensemble with the corresponding energy U{sub N}(β{sub 0}). For an arbitrary self-adjoint operator  whose support is contained in a hypercubic block B inside Λ, we prove that the expectation values of  with respect to these two ensembles are close to each other for large N provided that β{sub 0} is sufficiently small and the number of sites in B is o(N{sup 1/2}). This establishes the equivalence of ensembles on the level of local states in a large but finite system. The result is essentially that of Brandao and Cramer (here restricted to the case of the canonical and the microcanonical ensembles), but we prove improved estimates in an elementary manner. We also review and prove standard results on the thermodynamic limits of thermodynamic functions and the equivalence of ensembles in terms of thermodynamic functions. The present paper assumes only elementary knowledge on quantum statistical mechanics and quantum spin systems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Impact of climate change on European winter and summer flood losses
Climate change is expected to alter European floods and associated economic losses in various ways. Here we investigate the impact of precipitation change on European average winter and summer financial losses due to flooding under a 1.5 °C warming scenario (reflecting a projected climate in the year 2115 according to RCP2.6)and for a counterfactual current-climate scenario where the climate has evolved without anthropogenic influence (reflecting a climate corresponding to pre-industrial conditions). Climate scenarios were generated with the Community Atmospheric Model (CAM)version 5. For each scenario, we derive a set of weights that when applied to the current climate's precipitation results in a climatology that approximates that of the scenario. We apply the weights to annual losses from a well-calibrated (to the current climate)flood loss model that spans 50,000 years and re-compute the average annual loss to assess the impact of precipitation changes induced by anthropogenic climate change. The method relies on a large stochastic set of physically based flood model simulations and allows quick assessment of potential loss changes due to change in precipitation based on two statistics, namely total precipitation, and total precipitation of very wet days (defined here as the total precipitation of days above the 95th percentile of daily precipitation). We compute the statistics with the raw CAM precipitation and bias-corrected precipitation. Our results show that for both raw and bias-corrected statistics i)average flood loss in Europe generally tend to increase in winter and decrease in summer for the future scenario, and consistent with that change we also show that ii)average flood losses have increased (decreased)for winter (summer)from pre-industrial conditions to the current day. Finally, the magnitude of the change varies among scenarios and statistics chosen.
54 ENVIRONMENTAL SCIENCES↗
Machine-Learning-Based Predictive Modeling of Glass Transition Temperatures: A Case of Polyhydroxyalkanoate Homopolymers and Copolymers
Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and economically viable and possessing a broad range of tunable properties—are currently being actively pursued as promising alternatives for petroleum-based plastics. The vast chemical complexity accessible within this class of polymers gives rise to challenges in the rational discovery of novel polymer chemistries for specific applications. Here, the burgeoning field of polymer informatics addresses this challenge via providing tools and strategies for accelerated property prediction and materials design via surrogate machine-learning models built on reliable past data. In this contribution, we use glass transition temperature T g as an example target property to demonstrate promise of the data-enabled route to accelerated learning of accurate structure–property mappings in PHA-based polymers. Our analysis uses a data set of experimentally measured T g values, polymer molecular weights, and a polydispersity index for PHA-based homo- and copolymers that was carefully assembled from the literature. A fingerprinting scheme that captures key properties based on topology, shape, and charge/polarity of specific chemical units or motifs forming the polymer backbone was devised to numerically represent the polymers. A validated statistical learning model is then developed to allow for a mapping of the polymer fingerprints onto the property space in a physically meaningful and reliable manner. Once developed, the model can not only rapidly predict the property of new PHA polymers but also provide uncertainties underlying the predictions. The model is further combined with an evolutionary-algorithm-based search strategy to efficiently identify multicomponent polymer compositions with a prespecified T g . While the present contribution is focused specifically on T g , the surrogate model development approach put forward here is general and can, in principle, be extended to a range of other properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The optimization of high resolution topographic data for 1D hydrodynamic models
The main focus of our research presented in this paper is to optimize and use high resolution topographical data (HRTD) for hydrological modelling. Optimization of HRTD is done by generating adaptive mesh by measuring distance of coarse mesh and the surface of the dataset and adapting the mesh from the perspective of keeping the geometry as close to initial resolution as possible. Technique described in this paper enables computation of very accurate 1-D hydrodynamic models. In the paper, we use HEC-RAS software as a solver. For comparison, we have chosen the amount of generated cells/grid elements (in whole discretization domain and selected cross sections) with respect to preservation of the accuracy of the computational domain. Generation of the mesh for hydrodynamic modelling is strongly reliant on domain size and domain resolution. Topographical dataset used in this paper was created using LiDAR method and it captures 5.9km long section of a catchment of the river Olše. We studied crucial changes in topography for generated mesh. Assessment was done by commonly used statistical and visualization methods.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Normalizing flows for likelihood-free inference with fusion simulations
Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Study of e + e − → π + π − π 0 at s from 2.00 to 3.08 GeV at BESIII
With the data samples taken at center-of-mass energies from 2.00 to 3.08 GeV with the BESIII detector at the BEPCII collider, a partial wave analysis on the e + e − → π + π − π 0 process is performed. The Born cross sections for e + e − → π + π − π 0 and its intermediate processes e + e − → ρ π and ρ ( 1450 ) π are measured as functions of s . The results for e + e − → π + π − π 0 are consistent with previous results measured with the initial state radiation method within one standard deviation, and improve the uncertainty by a factor of ten. By fitting the line shapes of the Born cross sections for the e + e − → ρ π and e + e − → ρ ( 1450 ) π , a structure with mass M = 2119 ± 11 ± 15 MeV / c 2 and width Γ = 69 ± 30 ± 5 MeV is observed with a significance of 5.9 σ , where the first uncertainties are statistical and the second ones are systematic. This structure can be interpreted as an excited ω state. Published by the American Physical Society 2024
Astronomy & Astrophysics↗
Lattice calculation of hadronic light-by-light contribution to the muon anomalous magnetic moment
The quark-connected part of the hadronic light-by-light scattering contribution to the muon’s anomalous magnetic moment is computed using lattice QCD with chiral fermions. Here we report several significant algorithmic improvements and demonstrate their effectiveness through specific calculations which show a reduction in statistical errors by more than an order of magnitude. The most realistic of these calculations is performed with a near-physical 171 MeV pion mass on a (4.6 fm) 3 spatial volume using the 32 3 ×64 Iwasaki+DSDR gauge ensemble of the RBC/UKQCD Collaboration.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Lanczos algorithm for lattice QCD matrix elements
Recent work [M. L. Wagman, Lanczos, the transfer matrix, and the signal-to-noise problem, .] found that an analysis formalism based on the Lanczos algorithm allows energy levels to be extracted from Euclidean correlation functions with faster ground-state convergence than effective masses, convergent estimators for multiple states from a single correlator, and two-sided error bounds. After filtering out spurious eigenvalues and using outlier-robust estimators within a nested bootstrap framework, Lanczos estimators behave more like multistate fit results than effective masses—but without involving statistical fitting. We extend this formalism to the determination of matrix elements from three-point correlation functions and provide a physical picture of “spurious-state filtering” involving restriction to a Hermitian subspace. We demonstrate similar advantages for matrix elements as for spectroscopy through example applications to noiseless mock-data and (bare) forward matrix elements of the strange scalar current between both ground and excited states with the quantum numbers of the nucleon.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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