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In particle simulations, the weights of particles determine how many physicalparticles they represent. Adaptively adjusting these weights can greatlyimprove the efficiency of the simulation, without creating severe nonphysicalartifacts. We present a new method for the pairwise merging of particles.Pairwise merging reduces the number of particles by combining two particlesinto one. To find particles that are `close' to each other, we use a k-d treedata structure. With a k-d tree, close neighbors can be searched forefficiently, and independently of the mesh used in the simulation. The mergingcan be done in different ways, conserving for example momentum or energy. Weintroduce probabilistic schemes, which set properties for the merged particleusing random numbers. The effect of various merge schemes on the energydistribution, the momentum distribution and the grid moments is compared.

Controlling the weights of simulation particles: adaptive particle management using k-d trees