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The smoothed particle hydrodynamics technique strongly relies on the properchoice of interpolating functions. In this work, we revisit and extend the mainproperties of a family of interpolators called $Sinc~kernels$ and compare themwith those of the widely used family of Wendland kernels. We show that a linearcombination of low and high-order Sinc kernels generates good qualityinterpolators, which are resistant to the pairing instability while keepinggood sampling properties in a wide range of neighbor interpolating points,$60\le n_b\le 400$. We show that a particular case of this linear mix of Sincsproduces a well-balanced and robust kernel, that improves previous results inthe Gresho-Chan vortex experiment even when the number of neighbors is notlarge, while yielding a good convergence rate. Although such a mixing techniqueis ideally suited for Sinc kernels owing to their excellent flexibility, it canbe easily applied to other interpolating families such as the B-splines andWendland kernels.

Mixing Sinc kernels to improve interpolations in smoothed particle hydrodynamics without pairing instability