A variance-reduction strategy for the sensitivity of

The Monte Carlo computation of the GPT-based sensitivity of the effective delayed neutron fraction βeff to nuclear data proves to be quite difficult to converge due to the small amount of delayed neutrons that are sampled in k-eigenvalue calculations. This paper describes a variance-reduction method aimed at efficiently computing the sensitivity coefficients of βeff, reducing the associated Monte Carlo uncertainty and increasing the Figure Of Merit. This variance-reduction technique allows also computing the sensitivities of βj eff for a specific precursor family j. Verification and performance evaluation are achieved using simple configurations admitting analytical solutions and several continuousenergy benchmarks.