Exploring new models in all detail with SARAH

TL;DR

SARAH is a Mathematica package that streamlines the implementation and analysis of complex particle physics models, enabling detailed analytical and numerical studies, including Higgs mass predictions and phenomenological constraints.

Contribution

The paper introduces SARAH's comprehensive features for model building, analytical calculations, and interfacing with various tools for phenomenological analysis, applicable to a wide range of models beyond the MSSM.

Findings

SARAH can handle models with additional gauge groups and superfields.

SARAH provides two-loop RGEs, tadpole equations, and mass matrices.

The combination of SARAH and SPheno allows precise Higgs mass predictions.

Abstract

I give an overview about the features the Mathematica package SARAH provides to study new models. In general, SARAH can handle a wide range of models beyond the MSSM coming with additional chiral superfields, extra gauge groups, or distinctive features like Dirac gaugino masses. All of these models can be implemented in a compact form in SARAH and are easy to use: SARAH extracts all analytical properties of the given model like two-loop renormalization group equations, tadpole equations, mass matrices and vertices. Also one- and two-loop corrections to tadpoles and self-energies can be obtained. For numerical calculations SARAH can be interfaced to other tools to get the mass spectrum, to check flavour or dark matter constraints, and to test the vacuum stability, or to perform collider studies. In particular, the interface to SPheno allows a precise prediction of the Higgs mass in a given model comparable to MSSM precision by incorporating the important two-loop corrections. I show in great detail at the example of the B-L-SSM how SARAH together with SPheno, HiggsBounds/HiggsSignals, FlavorKit, Vevacious, CalcHep, MicrOmegas, WHIZARD, and MadGraph can be used to study all phenomenological aspects of a model. Even if I concentrate in this manuscript on the analysis of supersymmetric models most features are also available in the non-supersymmetric case.