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Title: Incorporating Experimental Information in the Total Monte Carlo Methodology Using File Weights

Some criticism has been directed towards the Total Monte Carlo method because experimental information has not been taken into account in a statistically well-founded manner. In this work, a Bayesian calibration method is implemented by assigning weights to the random nuclear data files and the method is illustratively applied to a few applications. In some considered cases, the estimated nuclear data uncertainties are significantly reduced and the central values are significantly shifted. The study suggests that the method can be applied both to estimate uncertainties in a more justified way and in the search for better central values. Some improvements are however necessary; for example, the treatment of outliers and cross-experimental correlations should be more rigorous and random files that are intended to be prior files should be generated.
Authors:
 [1] ;  [1] ;  [2] ;  [3] ;  [2] ; ;  [1]
  1. Dept. of Physics and Astronomy, Uppsala University, Uppsala (Sweden)
  2. Nuclear Research and Consultancy Group NRG, Petten (Netherlands)
  3. (Sweden)
Publication Date:
OSTI Identifier:
22436785
Resource Type:
Journal Article
Resource Relation:
Journal Name: Nuclear Data Sheets; Journal Volume: 123; Conference: International workshop on nuclear data covariances, Santa Fe, NM (United States), 28 Apr - 1 May 2014; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; DATA COVARIANCES; MONTE CARLO METHOD; NUCLEAR DATA COLLECTIONS; RANDOMNESS EXPERIMENT RESULTS