4.7 Article

munuSSM: A python package for the μ-from-ν Supersymmetric Standard Model

Journal

COMPUTER PHYSICS COMMUNICATIONS
Volume 264, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.cpc.2021.107935

Keywords

Supersymmetry; Higgs physics; Collider phenomenology

Funding

  1. Deutsche Forschungsgemeinschaft, Germany underGermany's Excellence Strategy EXC2121 Quantum Universe [390833306]

Ask authors/readers for more resources

The Python package munuSSM presented in this paper allows for phenomenological studies in the context of the mu-from-nu Supersymmetric Standard Model. It incorporates radiative corrections to the neutral scalar potential at full one-loop level, and can include higher-order corrections via an automated link to the public code FeynHiggs for an accurate prediction of the SM-like Higgs-boson mass. The package also calculates effective couplings and branching ratios of neutral and charged Higgs bosons, and provides a flexible framework that can be extended by users for further calculations and constraints.
We present the public python package munuSSM that can be used for phenomenological studies in the context of the mu-from-nu Supersymmetric Standard Model (mu nu SSM). The code incorporates the radiative corrections to the neutral scalar potential at full one-loop level. Sizable higher-order corrections, required for an accurate prediction of the SM-like Higgs-boson mass, can be consistently included via an automated link to the public code FeynHiggs. In addition, a calculation of effective couplings and branching ratios of the neutral and charged Higgs bosons is implemented. This provides the required ingredients to check a benchmark point against collider constraints from searches for additional Higgs bosons via an interface to the public code HiggsBounds. At the same time, the signal rates of the SM-like Higgs boson can be tested applying the experimental results implemented in the public code HiggsSignals. The python package is constructed in a flexible and modular way, such that it provides a simple framework that can be extended by the user with further calculations of observables and constraints on the model parameters. (C) 2021 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available