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Sobes, Vladimir

Publications and source records attributed to Sobes, Vladimir.

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recognizing Unrecognized Sources of Uncertainty (USU) in Nuclear Data

Historically, time-of-flight (TOF) nuclear cross section measurements on different nuclides are assumed to be uncorrelated if they were recorded in different facilities with identical methods, identical facilities with different methods, and even identical facilities with identical methods. Ideally, measurements of different nuclides would truly be uncorrelated thus providing independent assessments of some cross section. In reality, correlations exist between measurements but are simply assumed to be unimportant. To eliminate these qualitative assumptions, in this paper we make a counter-intuitive suggestion to perform an intentionally correlated measurement of energy-differential fission and capture reactions for nuclides in a single criticality safety benchmark during a single experimental campaign. While this would introduce undesirable correlations, it would fully quantify correlations between datasets, rather than assume that the correlations do not exist.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Templates of expected measurement uncertainties

The covariance committee of CSEWG (Cross Section Evaluation Working Group) established templates of expected measurement uncertainties for neutron-induced total, (n,γ), neutron-induced charged-particle, and (n,xn) reaction cross sections as well as prompt fission neutron spectra, average prompt and total fission neutron multiplicities, and fission yields. Templates provide a list of what uncertainty sources are expected for each measurement type and observable, and suggest typical ranges of these uncertainties and correlations based on a survey of experimental data, associated literature, and feedback from experimenters. Information needed to faithfully include the experimental data in the nuclear-data evaluation process is also provided. These templates could assist (a) experimenters and EXFOR compilers in delivering more complete uncertainties and measurement information relevant for evaluations of new experimental data, and (b) evaluators in achieving a more comprehensive uncertainty quantification for evaluation purposes. This effort might ultimately lead to more realistic evaluated covariances for nuclear-data applications. In this topical issue, we cover the templates coming out of this CSEWG effort–typically, one observable per paper. This paper here prefaces this topical issue by introducing the concept and mathematical framework of templates, discussing potential use cases, and giving an example of how they can be applied (estimating missing experimental uncertainties of 235 U(n,f) average prompt fission neutron multiplicities), and their impact on nuclear-data evaluations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Templates of expected measurement uncertainties for total neutron cross-section observables

This paper provides a template of expected uncertainties and correlations for measurements of total neutron cross-section observables by transmission. Measurements with time-of-flight and mono-energetic neutron sources are covered. The information required for evaluations in the resonance region and high energy region is detailed, along with the template of uncertainties and correlations that can be used in the absence of other information.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Templates of expected measurement uncertainties for neutron-induced capture and charged-particle production cross section observables

This paper provides a template of expected uncertainties and correlations for measurements of neutron-induced capture and charged-particle production cross sections. Measurements performed in-beam include total absorption spectroscopy, total energy detection, γ -ray spectroscopy, and direct charged-particle detection. Offline measurements include activation analysis and accelerator mass spectrometry. The information needed for proper use of the datasets in resonance region and high energy region evaluations is described, and recommended uncertainties are provided when specific values are not available for a dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Methodology for physics-informed generation of synthetic neutron time-of-flight measurement data

Accurate neutron cross section data are a vital input to the simulation of nuclear systems for a wide range of applications from energy production to national security. The evaluation of experimental data is a key step in producing accurate cross sections. There is a widely recognized lack of reproducibility in the evaluation process due to its artisanal nature and therefore there is a call for improvement within the nuclear data community. This can be realized by automating/standardizing viable parts of the process, namely, parameter estimation by fitting theoretical models to experimental data. This automation effort could greatly benefit from a synthetic data resource. This work leverages problem-specific physics, Monte Carlo sampling, and a general methodology for data synthesis to generate unlimited, labelled experimental cross-section data that is statistically indistinguishable to the observed data. Heuristic and, where applicable, rigorous statistical comparisons to observed data support this claim. The demonstration is based on/limited to transmission measurements at Rensselaer Polytechnic Institute (RPI) and energy-differential cross sections in the resolved resonance region (RRR). An open-source software is published alongside this article that executes the complete methodology to produce high-utility synthetic datasets. The goal of this work is to provide an approach and corresponding tool that will allow the evaluation community to begin exploring more data-driven, ML-based solutions to long-standing challenges in the field.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Bayesian Monte Carlo Evaluation of Imperfect (n, 233 U) Data and Model

Conventional nuclear data evaluation methods using generalized linear least squares make the following assumptions: prior and posterior probability distribution functions (PDFs) of all model parameters and data are normal (Gaussian); the linear approximation is sufficiently accurate to minimize the cost function (even for nonlinear models); the model (e.g., of neutron cross section) and experimental data (including covariance data) are without defect and prior PDFs of parameters and measured data are known perfectly. Neglect of covariance between model parameters and measured data in conventional evaluations contributes to imperfections. These assumptions are inherent to the generalized linear least squares minimization method commonly used for resolved resonance region neutron cross section evaluations but are often not justified due to the presence of non-normal PDFs, nonlinear models (e.g., R-matrix formalism), and inherent imperfections in data and models (e.g., imperfect covariance data). Here, these assumptions are removed in a mathematical framework of Bayes’ theorem, which is implemented using the Metropolis-Hastings Monte Carlo method. Most importantly, new parameters are introduced to parameterize discrepancies between the theoretical model and measured data to quantify judgement about discrepancies or imperfections in a reproducible manner. An evaluation of 233U in the eV region using the ENDF-B/VIII.0 library and transmission data (Guber et al.) is presented, and posterior parameters are compared to those obtained by conventional evaluation methods. This example illustrates the effects of removing the most harmful assumption: that of model-data perfection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Stochastic gradient descent for optimization for nuclear systems

The use of gradient descent methods for optimizing k-eigenvalue nuclear systems has been shown to be useful in the past, but the use of k-eigenvalue gradients have proved computationally challenging due to their stochastic nature. ADAM is a gradient descent method that accounts for gradients with a stochastic nature. This analysis uses challenge problems constructed to verify if ADAM is a suitable tool to optimize k-eigenvalue nuclear systems. ADAM is able to successfully optimize nuclear systems using the gradients of k-eigenvalue problems despite their stochastic nature and uncertainty. Furthermore, it is clearly demonstrated that low-compute time, high-variance estimates of the gradient lead to better performance in the optimization challenge problems tested here.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗