Using the Monte-Carlo method to analyze experimental data and produce uncertainties and covariances

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