Eur. Phys. J. Plus (2018) 133: 537
https://doi.org/10.1140/epjp/i2018-12361-x

Regular Article

Monte Carlo nuclear data adjustment via integral information

¹ Paul Scherrer Institut, Villigen, Switzerland
² CEA, DAM, DIF, 91297, Arpajon Cedex, France
³ Nuclear Data Section, International Atomic Energy Agency, Vienna, Austria

^* e-mail: dimitri-alexandre.rochman@psi.ch

Received: 15 March 2018
Accepted: 4 November 2018
Published online: 20 December 2018

Abstract

In this paper, we present three Monte Carlo methods to include integral benchmark information into the nuclear data evaluation procedure: BMC, BFMC and Mocaba. They allow to provide posterior nuclear data and their covariance information in a Bayesian sense. Different examples will be presented, based on 14 integral quantities with fast neutron spectra (k_eff and spectral indices). Updated nuclear data for ²³⁵U, ²³⁸U and ²³⁹Pu are considered and the posterior nuclear data are tested with MCNP simulations. One of the noticeable outcomes is the reduction of uncertainties for integral quantities, obtained from the reduction of the nuclear data uncertainties and from the rise of correlations between cross sections of different isotopes. Finally, the posterior nuclear data are tested on an independent set of benchmarks, showing the limit of the adjustment methods and the necessity for selecting well representative systems.