Pith. sign in

REVIEW

Realistic simplified gaugino-higgsino models in the MSSM

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1710.09941 v2 pith:CZ5GCVYE submitted 2017-10-26 hep-ph hep-ex

Realistic simplified gaugino-higgsino models in the MSSM

classification hep-ph hep-ex
keywords mssmmixingmodelsrealisticmassmatrixsimplifiedspectra
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We present simplified MSSM models for light neutralinos and charginos with realistic mass spectra and realistic gaugino-higgsino mixing, that can be used in experimental searches at the LHC. The formerly used naive approach of defining mass spectra and mixing matrix elements manually and independently of each other does not yield genuine MSSM benchmarks. We suggest the use of less simplified, but realistic MSSM models, whose mass spectra and mixing matrix elements are the result of a proper matrix diagonalisation. We propose a novel strategy targeting the design of such benchmark scenarios, accounting for user-defined constraints in terms of masses and particle mixing. We apply it to the higgsino case and implement a scan in the four relevant underlying parameters {{\mu}, tan\beta, M_1, M_2} for a given set of light neutralino and chargino masses. We define a measure for the quality of the obtained benchmarks, that also includes criteria to assess the higgsino content of the resulting charginos and neutralinos. We finally discuss the distribution of the resulting models in the MSSM parameter space as well as their implications for supersymmetric dark matter phenomenology.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.