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Monte Carlo radiation transport simulations have clearly contributed toimprove the design of nuclear systems. When performing in-beam or shieldingsimulations a complexity arises due to the fact that particles must be trackedto regions far from the original source or behind the shielding, often lackingsufficient statistics. Different possibilities to overcome this problem such asusing particle lists or generating synthetic sources have already beenreported. In this work we present a new approach by using the adaptivemultivariate kernel density estimator (KDE) method. This concept wasimplemented in KDSource, a general tool for modelling, optimizing and samplingKDE sources, which provides a convenient user interface. The basic propertiesof the method were studied in an analytical problem with a known densitydistribution. Furthermore, the tool was used in two Monte Carlo simulationsthat modelled neutron beams, which showed good agreement with experimentalresults.

KDSource, a tool for the generation of Monte Carlo particle sources using kernel density estimation