Robustness of zero-variance Monte Carlo games

Abstract

Zero-variance Monte Carlo games are ideal sampling strategies that provide the theoretical foundations upon which very successful variancereduction schemes are built. In this work, we explore the robustness of zerovariance games and assess in particular the impact of two common simplifications used in production Monte Carlo codes. First, using a discretized adjoint function to bias the kernels that define the stochastic process. Second, replacing the exact sampling of the flight kernel by approximate sampling strategies that are more amenable to be implemented in production Monte Carlo codes. The resulting effects on the variance and on the Figure of Merit (FoM) will be probed in the framework of a benchmark configuration based on the singlespeed two-direction transport model.