# Automated Choice for the Best Renormalization Scheme in BSM Models

## TL;DR

This paper introduces an automated method for selecting the optimal renormalization scheme in BSM models to ensure stable and accurate higher-order calculations across parameter spaces.

## Contribution

A novel automated approach for choosing the best renormalization scheme in BSM models, demonstrated in the MSSM chargino/neutralino sector, with broad applicability.

## Key findings

- Method successfully identifies suitable renormalization schemes.
- Improves stability and accuracy of loop-level predictions.
- Applicable to various BSM models.

## Abstract

The explorations of models beyond the Standard Model (BSM) naturally involve scans over the unknown BSM parameters. On the other hand, high precision predictions require calculations at the loop-level and thus a renormalization of (some of) the BSM parameters. Often many choices are possible for the renormalization scheme (RS). This concerns the choice of the set of to-be-renormalized parameters out of a larger set of BSM parameters, but can also concern the type of renormalization condition which is chosen for a specific parameter. 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, which may not even be noticed numerically if an isolated parameter point is investigated, or when the higher-order BSM calculations are performed in an automated, not supervised set-up. Consequently, the (automated) exploration of BSM models requires a choice of a good RS {\em before} the calculation is performed. We propose a new method how such a choice can be performed. 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.