Tests of gluino-driven radiative breaking of the electroweak symmetry at the LHC

TL;DR

This paper investigates a supersymmetric model where a large gluino mass causes radiative electroweak symmetry breaking, leading to a split spectrum with light sleptons, and uses neural networks to analyze potential discoveries at future colliders.

Contribution

It introduces a gluino-driven radiative breaking mechanism and employs neural network analysis to identify signals of light sleptons at the LHC.

Findings

Neural network analysis supports gluino-driven radiative breaking.

Model predicts light sleptons detectable at HL-LHC and HE-LHC.

Split mass spectrum consistent with muon g-2 and Higgs mass measurements.

Abstract

The recent muon $g-2$ result from Fermilab combined with the Brookhaven result, strongly points to new physics beyond the Standard Model which can be well described by the electroweak sector of supersymmetry if the masses of the sleptons and some of the electroweak gauginos are in the few hundred GeV range. However, the Higgs boson mass measurement at 125 GeV indicates a mass scale for squarks which lies in the few TeV region indicating a split mass spectrum between squarks and sleptons. This apparent puzzle is resolved in a natural way in gluino-driven radiative breaking of the electroweak symmetry where radiative breaking is driven by a large gluino mass and the gluino color interactions lead to a large splitting between the squarks and the sleptons. We show that an analysis without prejudice using an artificial neural network also leads to the gluino-driven radiative breaking. We use a set of benchmarks and a deep neural network analysis to test the model for the discovery of light sleptons and sneutrinos at HL-LHC and HE-LHC.