Automated choice of the best renormalization scheme

High-precision predictions in BSM models require calculations at the loop-level and thus a renormalization of (some of) the BSM parameter. Here many choices for the renormalization scheme (RS) are possible. A given RS can be well suited to yield stable'' and well behaved’’ higher-order corrections in one part of the BSM parameter space, but can fail completely in other parts. The latter may not even be noticed numerically if an isolated parameter point is investigated. Here we review a new method for choosing a ``well behaved’’ RS. We demonstrate the feasibility of our new method in the chargino/neutralino sector of the Minimal Supersymmetric Standard Model (MSSM), but stress the general applicability of our method to all types of BSM models.