Related Experiment Video
Updated: Aug 14, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
Published on: April 20, 2016
Covariance and sensitivity data generation at ORNL
L C Leal1, H Derrien, N M Larson
1Oak Ridge National Laboratory, P. O. Box 2008, Oak Ridge, TN 37831, USA. leallc@ornl.gov
Abstract:
Covariance data are required to assess uncertainties in design parameters in several nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the US Evaluated Nuclear Data Library, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. In this paper we address the generation of covariance data in the resonance region done with the computer code SAMMY. SAMMY is used in the evaluation of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on the generalised least-squares formalism (Bayesian theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, it provides the resonance parameter covariances. For resonance parameter evaluations where there are no resonance parameter covariance data available, the alternative is to use an approach called the 'retroactive' resonance parameter covariance generation. In this paper, we describe the application of the retroactive covariance generation approach for the gadolinium isotopes.
More Related Videos
Related Concept Videos
Correlation of Experimental Data
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Variability: Analysis
The range is a simple measure of variability, indicating the difference between the highest and...
Calibration Curves: Correlation Coefficient
Response Surface Methodology
The process of RSM involves several key steps:
Correlation and Regression
Calculating and Interpreting the Linear Correlation Coefficient

