ARIADNE – a program estimating covariances in detail for neutron experiments
Abstract
The python program ARIADNE is a tool developed for evaluators to estimate detailed uncertainties and covariances for experimental data in a consistent and efficient manner. Currently, it is designed to aid in the uncertainty quantification of prompt fission neutron spectra, and was employed to estimate experimental covariances for CIELO and ENDF/B-VIII.0 evaluations. It provides a streamlined way to estimate detailed covariances by (1) implementing uncertainty quantification algorithms specific to the observables, (2) defining input quantities for typically encountered uncertainty sources and correlation shapes, and (3) automatically generating plots of data, uncertainties and correlations, GND formatted XML and plain text output files. Covariances of the same and between different datasets can be estimated, and tools are provided to assemble a database of experimental data and covariances for an evaluation based on ARIADNE outputs. The underlying IPython notebook files can be easily stored, including all assumptions on uncertainties, leading to more reproducible inputs for nuclear data evaluations. Here, the key inputs and outputs are shown along with a representative example for the current version of ARIADNE to illustrate its usability and to open a discussion on how it could address further needs of the nuclear data evaluation community.
- Publication Date:
- Research Org.:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1489949
- Report Number(s):
- LA-UR-17-30469
Journal ID: ISSN 2491-9292
- Grant/Contract Number:
- 89233218CNA000001; AC52-06NA25396
- Resource Type:
- Accepted Manuscript
- Journal Name:
- EPJ Nuclear Sciences and Technologies
- Additional Journal Information:
- Journal Volume: 4; Journal ID: ISSN 2491-9292
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Neudecker, Denise. ARIADNE – a program estimating covariances in detail for neutron experiments. United States: N. p., 2018.
Web. doi:10.1051/epjn/2018012.
Neudecker, Denise. ARIADNE – a program estimating covariances in detail for neutron experiments. United States. https://doi.org/10.1051/epjn/2018012
Neudecker, Denise. Wed .
"ARIADNE – a program estimating covariances in detail for neutron experiments". United States. https://doi.org/10.1051/epjn/2018012. https://www.osti.gov/servlets/purl/1489949.
@article{osti_1489949,
title = {ARIADNE – a program estimating covariances in detail for neutron experiments},
author = {Neudecker, Denise},
abstractNote = {The python program ARIADNE is a tool developed for evaluators to estimate detailed uncertainties and covariances for experimental data in a consistent and efficient manner. Currently, it is designed to aid in the uncertainty quantification of prompt fission neutron spectra, and was employed to estimate experimental covariances for CIELO and ENDF/B-VIII.0 evaluations. It provides a streamlined way to estimate detailed covariances by (1) implementing uncertainty quantification algorithms specific to the observables, (2) defining input quantities for typically encountered uncertainty sources and correlation shapes, and (3) automatically generating plots of data, uncertainties and correlations, GND formatted XML and plain text output files. Covariances of the same and between different datasets can be estimated, and tools are provided to assemble a database of experimental data and covariances for an evaluation based on ARIADNE outputs. The underlying IPython notebook files can be easily stored, including all assumptions on uncertainties, leading to more reproducible inputs for nuclear data evaluations. Here, the key inputs and outputs are shown along with a representative example for the current version of ARIADNE to illustrate its usability and to open a discussion on how it could address further needs of the nuclear data evaluation community.},
doi = {10.1051/epjn/2018012},
journal = {EPJ Nuclear Sciences and Technologies},
number = ,
volume = 4,
place = {United States},
year = {Wed Nov 14 00:00:00 EST 2018},
month = {Wed Nov 14 00:00:00 EST 2018}
}
Search WorldCat to find libraries that may hold this journalFigures / Tables:
Fig. 1: The total time resolution uncertainty relative to the PFNS is compared for shape, shape ratio and shape ratio calibration data for a time resolution of 1 ns, a TOF length of 1m for all measurements and Maxwellian temperatures of 1.33 and 1.42MeV for the investigated and the monitormore »
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Figures / Tables found in this record: