https://doi.org/10.1051/epjconf/202328401045
Using the Monte-Carlo method to analyze experimental data and produce uncertainties and covariances
1 Université de Strasbourg, CNRS, IPHC/DRS UMR 7178, 23 Rue du Loess, F-67037 Strasbourg, France
2 CEA, DES, IRESNE, DER, SPRC, LEPh, F-13108 Saint-Paul-lez-Durance, France
3 CEA, DAM, DIF, F-91297 Arpajon, France
4 Nuclear Data Section, International Atomic Energy Agency, Wagramer Strasse, A-1400 Vienna, Austria
5 Horia Hulubei National Institute for Physics and Nuclear Engineering, 077125 Bucharest-Măgurele, Romania
6 European Commission, Joint Research Centre, Retieseweg 111, B-2440 Geel, Belgium
* e-mail: ghenning@iphc.cnrs.fr
** Currently, Univ. of Helsinki
Published online: 26 May 2023
The production of useful and high-quality nuclear data requires measurements with high precision and extensive information on uncertainties and possible correlations. Analytical treatment of uncertainty propagation can become very tedious when dealing with a high number of parameters. Even worse, the production of a covariance matrix, usually needed in the evaluation process, will require lenghty and error-prone formulas. To work around these issues, we propose using random sampling techniques in the data analysis to obtain final values, uncertainties and covariances and for analyzing the sensitivity of the results to key parameters. We demonstrate this by one full analysis, one partial analysis and an analysis of the sensitivity to branching ratios in the case of (n,n’γ) cross section measurements.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
