  The smoothed particle hydrodynamic technique is strongly based on the proper
choice of interpolation functions. This statement is particularly relevant for
the study of subsonic fluxes and turbulence, where inherent small errors in the
averaging procedures introduce excessive damping on the smallest scales. To
mitigate these errors we can increase both the number of interpolating points
and the order of the interpolating kernel function. However, this approach
leads to a higher computational burden across all fluid regions. Ideally, the
development of a single kernel function capable of effectively accommodating
varying numbers of interpolating points in different fluid regions, providing
good resolution and minimal errors would be highly desirable. In this work, we
revisit and extend the main properties of a family of interpolators called
$Sinc~kernels$ and compare them with the widely used family of Wendland
kernels. We show that a linear combination of low- and high-order Sinc kernels
generates good-quality interpolators, which are resistant to pairing
instability while maintaining good 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 Sincs produces a well-balanced and robust kernel
that improves previous results in the Gresho-Chan vortex experiment even when
the number of neighbors is not large, while yielding a good convergence rate.
Although such a mixing technique is ideally suited for Sinc kernels owing to
their excellent flexibility, it can be easily applied to other interpolating
families such as the B-splines and Wendland kernels.
