Kernel density estimated Monte Carlo global flux tallies

The Kernel Density Estimator (KDE) is used to represent Monte Carlo (MC) tallies. Two new neutron flux estimators and their variances are developed, namely the KDE-collision and KDE-track-length. These new estimators are capable of estimating the flux at any point within a given domain without any bin structure. The strength of these two estimators is illustrated with numerical examples in 1D geometry. Convergence properties of the KDE estimators are discussed and the KDE estimators are compared with the Functional Expansion Tally (FET) and the conventional Histogram tally. The results show that the KDE tallies compare favorably with the FET and Histogram tallies with respect to accuracy and convergence rate. (authors)