Results for “statistical physics” — Page 81

DOE OSTI2021

Comparison of Results from Recent NNSA and CEA Measurements of the 239 Pu(n, f) Prompt Fission Neutron Spectrum

The National Nuclear Security Administration (NNSA)/DP French Alternative Energies and Atomic Energy Commission (CEA)/DAM agreement on cooperation on fundamental science is a U.S.-French collaborative effort to combine intellectual and experimental resources and further the relevant nuclear science. Recently, both the NNSA and CEA experimental teams performed high-statistics measurements of the 239 Pu(n, f) prompt fission neutron spectrum (PFNS) at the Los Alamos Neutron Science Center, both of which were recently published in the journal Physical Review C. These separate measurements used the same experimental area and a common neutron detector array, but differ in many aspects, including background assessments, data acquisition systems and philosophies, fission detectors, and PFNS extraction techniques. Hence, some aspects of the experimental methods and associated uncertainties are highly correlated while others are independent. The results from both measurements broke new ground for PFNS measurements given their higher accuracy and more detailed study of corrections necessary for the measured quantity compared to existing literature measurements, and both will significantly impact PFNS nuclear data evaluations for the foreseeable future. Here, the focus of this work is to document a comparison of the results from these distinct measurements in terms of the acquired data, the PFNS results, and the measured average PFNS energies. While systematic differences between the PFNS results are present on the 1–3% level, the acquired data relative to each respective measurement at low incident neutron energies are in remarkable agreement, as are the conclusions regarding the magnitude and position of features in the PFNS relating to second-chance fission, third-chance fission, and pre-equilibrium neutron emission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Understanding the role of morphology and environment in the dynamical evolution of isolated galaxy triplets

The environments where galaxies reside affects their evolutionary histories. Galaxy triplets (systems composed of three physically bound galaxies) are one of simplest groups of galaxies, and are therefore excellent laboratories to study evolutionary mechanisms where effects of the environment are minimal. We present a statistical study of the dynamical properties of isolated galaxy triplets as a function of their local and large-scale environments. To explore the connection of the dynamical evolution on the systems with the evolution of the galaxies composing the triplets, we consider observational properties such as morphology and star formation rate (SFR). We used the SDSS-based catalogue of Isolated Triplets (SIT), which contains 315 triplets. We classified each triplet according to galaxy morphologies and defined a parameter Q trip to quantify the total local tidal strengths in the systems. To quantify the dynamical stage of the system we used the parameters of harmonic radius R H , velocity dispersion σvr, crossing time H 0 t c , and virial mass M vir . Triplets composed of three early-type galaxies present the smallest R H , indicating that they are in general more compact than triplets with one or more late-type galaxies. Among triplets with low values of R H and H 0 t c , SIT triplets with Q trip < –2 are relaxed systems that are more dynamically evolved, while triplets with Q trip > –2 show compact configurations due to interactions within the system, such as ongoing mergers. We found that there is no dominant galaxy in triplets in terms of properties of stellar populations such as global colour and SFR. Moreover, the global SFR in isolated triplets composed of two or more early-type galaxies increases with the stellar mass ratio of the galaxies with respect to the central galaxy, therefore the system is globally ‘rejuvenated’.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Separable shape tensors for aerodynamic design

Airfoil shape design is a classical problem in engineering and manufacturing. In this work, we combine principled physics-based considerations for the shape design problem with modern computational techniques using a data-driven approach. Modern and traditional analyses of two-dimensional (2D) and three-dimensional (3D) aerodynamic shapes reveal a flow-based sensitivity to specific deformations that can be represented generally by affine transformations (rotation, scaling, shearing, and translation). We present a novel representation of shapes that decouples affine-style deformations over a submanifold and a product submanifold principally of the Grassmannian. As an analytic generative model, the separable representation, informed by a database of physically relevant airfoils, offers: (i) a rich set of novel 2D airfoil deformations not previously captured in the data, (ii) an improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal 2D shapes.

42 ENGINEERING↗

DOE OSTI2024

Applications of flow models to the generation of correlated lattice QCD ensembles

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman–Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Time-Based CAN IDS Paper Results Code

Modern vehicles are complex cyber-physical systems made of hundreds of electronic control units (ECUs) that communicate over controller area networks (CANs). This inherited complexity has expanded the CAN attack surface which is vulnerable to message injection attacks. These injections change the overall timing characteristics of messages on the bus, and thus, to detect these malicious messages, time-based intrusion detection systems (IDSs) have been proposed. However, time-based IDSs are usually trained and tested on low-fidelity datasets with unrealistic, labeled attacks. This makes difficult the task of evaluating, comparing, and validating IDSs. Here we detail and benchmark four time-based IDSs against the newly published ROAD dataset, the first open CAN IDS dataset with real (non-simulated) stealthy attacks with physically verified effects. We found that methods that perform hypothesis testing by explicitly estimating message timing distributions have lower performance than methods that seek anomalies in a distribution related statistic. In particular, these “distribution-agnostic” based methods outperform “distribution-based” methods by at least 55% in area under the precision-recall curve (AUC-PR). Our results expand the body of knowledge of CAN time-based IDSs by providing details of these methods and reporting their results when tested on datasets with real advanced attacks. Finally, we develop an after-market plug-in detector using lightweight hardware, which can be used to deploy the best performing IDS method on nearly any vehicle.

Moriano, Pablo [Oak Ridge National Lab. (ORNL), Oa↗

DOE OSTI2025

Exploiting the PRISM Feature and Purity Monitors of the SBND Neutrino Detector

The Short-Baseline Neutrino (SBN) program at Fermilab will pursue neutrino properties searches, exploiting three Liquid Argon Time Projection Chamber (LArTPC) experiments to investigate neutrino oscillations with neutrinos from the Booster Neutrino Beam (BNB). SBND will be the near detector in the program, measuring the un-oscillated flux. It is anticipated to begin operating in 2023. Thanks to its peculiar proximity to the neutrino source of about 110 meters, SBND will collect extremely high statistics data of the flux in the GeV energy range, providing unprecedented precision measurements. SBND particular setting also allows to take measurements at different distances from BNB’s center maintaining statistics high enough at each range. LArTPC and the cold electronics technologies, as well as the argon-neutrino interaction studies, will play a key role in future neutrino physics. In this prospect, this work was focused on two SBND features: SBND PRISM tool, with the aim of investigating neutrino flux at different distances from the beam axis, and Purity Monitors, which provide a clever measurement of argon purity in the detector, whose value is fundamental in neutrino-argon interaction.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-halo Connection with Optimal Statistics

Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or limited to clustering statistics that can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of Sloan Digital Sky Survey DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark matter only simulations with a ΛCDM cosmology. To constrain the theoreStical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find a significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4σ) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first of its kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

A Probabilistic Approach to Load Modeling for Central HVAC Systems in Large Commercial Buildings for Retrofit Decisions Under Uncertainty

Retrofitting central HVAC systems in large commercial buildings with advanced technologies like heat recovery chillers (HRCs) offers a significant opportunity to enhance energy efficiency. However, analyzing these retrofits is challenging with traditional whole-building simulation tools, which require intensive calibration and struggle to model innovative system configurations and controls. To overcome these limitations, this study proposes a load profilebased retrofit analysis framework that provides better decisions under uncertainty. The main focus of this paper is the development of a probabilistic load profile model that can be used in the framework by using exploratory data analysis (EDA) of measured building data to properly quantify its inherent variability. A non-parametric Gaussian Process (GP) model was employed to capture the time- and weather-dependent characteristics of the heating load while explicitly modeling its uncertainty. The model's effectiveness is demonstrated through strong predictive performance on unseen data and physically interpretable insights into load behavior. This data-driven, probabilistic load profile serves as a robust and flexible input for subsequent system simulations, enabling a more confident and statistically sound analysis of retrofit potential.

Ham, S W↗

NASA NTRS1990

The behavior of elastic anisotropic laminated composite flat structures subjected to deterministic and random loadings

Within this research project, the following topics were studied: (1) foundation of the refined theory of flat cross-ply laminated composite flat and curved panels as well as their static and dynamic response analysis; (2) foundation of a geometrically-nonlinear shear-deformable theory of composite laminated flat panels including the effect of initial geometric imperfections and its application in the postbuckling analysis; (3) the study of the dynamic response of shear deformable elastic laminated composite panels to deterministic time-dependent external excitations as the sonic boom and explosive blast type-loadings; (4) the study of the dynamic response of shear deformable elastic laminated composite panels to random excitation as e.g. the one produced by a jet noise or by any time-dependent external excitation whose characteristics are expressed in a statistical sense; and (5) the dynamic stability of fiber-reinforced composite flat panels whose materials (due to e.g. an ambient high temperature field) exhibit a time-dependent physical behavior.

Librescu, Liviu↗

NASA NTRS1995

On the spatial distribution of magnetic fields on the solar surface

Recent measurements of solar surface magnetic fields suggest that the spatial distribution of these fields is fractal. In order to understand the physical basis for such geometric complexity, we study here the advection of magnetic flux tubes relatively simple random motions on the surface of a fluid and investigate the spatial statistics of the resulting surface field. While this study does not directly address the question of why solar surface fields have the observed spatial structure, it is designed to build our intuition about how surface flows lead to complex spatial structuring of magnetic fields. As part of our study, we discuss the various methods by which one can describe the spatial distribution of the surface magnetic flux and relate them mathematically; this turns out to be a crucial point of our work since, as we show, a number of previous analyses have misinterpreted the analysis procedures for determining fractal dimensions. Our principal result is the explicit demonstration that simple random flows lead to magnetic flux spatial distributions with a multifractal dimension spectrum. Furthermore, we demonstrate that this magnetic spatial structure is generic, i.e., is characteristic of a very large class of random flows.

Tao, L.↗

NASA NTRS2004

Uncertainty-Based Approach for Dynamic Aerodynamic Data Acquisition and Analysis

Development of improved modeling methods to provide increased fidelity of flight predictions for aircraft motions during flight in flow regimes with large nonlinearities requires improvements in test techniques for measuring and characterizing wind tunnel data. This paper presents a method for providing a measure of data integrity for static and forced oscillation test techniques. Data integrity is particularly important when attempting to accurately model and predict flight of today s high performance aircraft which are operating in expanded flight envelopes, often maneuvering at high angular rates at high angles-of-attack, even above maximum lift. Current aerodynamic models are inadequate in predicting flight characteristics in the expanded envelope, such as rapid aircraft departures and other unusual motions. Present wind tunnel test methods do not factor changes of flow physics into data acquisition schemes, so in many cases data are obtained over more iterations than required, or insufficient data may be obtained to determine a valid estimate with statistical significance. Additionally, forced oscillation test techniques, one of the primary tools used to develop dynamic models, do not currently provide estimates of the uncertainty of the results during an oscillation cycle. A method to optimize the required number of forced oscillation cycles based on decay of uncertainty gradients and balance tolerances is also presented.

Heim, Eugene H. D.↗

NASA NTRS2008

High Energy Polarization - Historical Remarks

We discuss the history and briefly outline the potential scientific impact of X-ray polarimetry and in particular studies of the Crab nebula and its pulsar. Despite major progress in X-ray imaging, spectroscopy, and timing, there have been only modest attempts at X-ray polarimetry. The last dedicated experiment, conducted by us over three decades ago, had such limited observing time and sensitivity that even a ten percent degree of polarization would not have been detected from some of the brightest X-ray sources in the sky, and statistically-significant X-ray polarization was detected in only the subject of this meeting, the Crab Nebula. Radio and optical astronomers use polarimetry extensively to probe the radiation physics and the geometry of sources. Sensitive X-ray polarimetry promises to reveal unique and crucial information about physical processes and structure of of all classes of X-ray sources. X-ray polarimetry remains the last undeveloped tool for the X-ray study of astronomical objects and needs to be properly exploited. We hope that this conference may mark the beginning of a new era for for this important scientific window.

Weisskopf, Martin C.↗

NASA NTRS2020

On Perspectives of Generating Quasi-Fock State via Resonant Self-Phase-Modulation

Optical resonators with cubic nonlinearity were among the ?rst simplest physical systems proposed for generation of squeezed states of light as well as for veri?cation of quantum nondemolition measurement concept. These conventional squeezed states are characterized with Gaussian statistics and positive Wigner function and are frequently treated as semiclassical. In contrast, it is known that the quantum states resulting from the higher order self-phase modulation are characterized with negative Wigner function. Such states can be transformed to an imperfect Fock state via classical biasing. Using these known features, we have developed an experiment strategy for observation and characterization of the nonclassical state in a system based on a high-Q nonlinear optical microcavity. We discuss technical limitations for the state observation associated with the losses in the system and technical noise of the classical optical sources involved in the measurement.

Bilenko, I. A.↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

DOE OSTI2023

Exascale Multiphysics Nuclear Reactor Simulations for Advanced Designs

ENRICO is a coupled application developed under the U.S. Department of Energy's Exascale Computing Project (ECP) targeting the modeling of advanced nuclear reactors. It couples radiation transport with heat and fluid simulation, including the high-fidelity, highresolution Monte-Carlo code Shift and the Computational fluid dynamics code NekRS. NekRS is a highly-performant open-source code for simulation of incompressible and low-Mach fluid flow, heat transfer, and combustion with a particular focus on turbulent flows in complex domains. It is based on rapidly convergent high-order spectral element discretizations that feature minimal numerical dissipation and dispersion. State-of-the-art multilevel preconditioners, efficient high-order time-splitting methods, and runtime-adaptive communication strategies are built on a fast OCCA-based kernel library, libParanumal, to provide scalability and portability across the spectrum of current and future high-performance computing platforms. On Frontier, Nek5000/RS has recently achieved an unprecedented milestone in breaching over 1 billion spectral elements and 350 billion degrees of freedom. Shift has demonstrated the capability to transport upwards of 1 billion particles per second in full core nuclear reactor simulations featuring complete temperature-dependent, continuous-energy physics on Frontier. Shift achieved a weak-scaling efficiency of 97.8% on 8192 nodes of Frontier and calculated 6 reactions in 214,896 fuel pin regions below 1% statistical error yielding first-of-a-kind resolution for a Monte Carlo transport application.

Hamilton, Steven P.↗

DOE OSTI2026

Neutron detector response modeling in NOvA

Neutrons can present a significant challenge for neutrino experiments in which energy reconstruction is critical. With the ability to escape detection completely and with a weak correlation between their kinetic energy and any eventual energy deposition, it is difficult to fully account for neutrons produced in neutrino interactions. This in turn leads to significant model dependence when evaluating neutron-related systematic uncertainties. The NOvA experiment is a long-baseline neutrino oscillation experiment with a high-statistics sample of antineutrino data collected by its near detector. We report an excess relative to data of simulated neutron candidates with low energy depositions when using standard Geant4 physics lists. The simulation excess is traced to an overabundance of secondary photons produced from interactions of neutrons with kinetic energy greater than \SI{20}{\mega\eV}. Improved agreement with data is obtained by applying the data-driven neutron-on-carbon \menate model for neutrons between \SI{20}{\mega\eV} and ${\sim}$\SI{100}{\mega\eV}. With \menate, the residual oversimulation is more uniform across the calorimetric neutron energy spectrum, suggesting possible overproduction of primary neutrons by the GENIE neutrino interaction generator. These results motivate the adoption of \menate-supplemented Geant4 simulation as the nominal simulation in the production of future \nova simulation.

Abubakar, S.↗