Z' mass limits and the naturalness of supersymmetry

TL;DR

This paper examines how the mass limits of a new Z' boson in U(1) extended supersymmetric models impact their naturalness, suggesting that future LHC searches are crucial for testing these models' viability.

Contribution

It analyzes the fine tuning related to Z' boson mass constraints in U(1) supersymmetric models and compares it to the MSSM, emphasizing the importance of LHC searches.

Findings

Z' mass bounds significantly influence model naturalness.

Future LHC Z' searches can challenge the most natural models.

Tree level fine tuning is less dependent on supersymmetry breaking assumptions.

Abstract

The discovery of a 125 GeV Higgs boson and rising lower bounds on the masses of superpartners have lead to concerns that supersymmetric models are now fine tuned. Large stop masses, required for a 125 GeV Higgs, feed into the electroweak symmetry breaking conditions through renormalisation group equations forcing one to fine tune these parameters to obtain the correct electroweak vacuum expectation value. Nonetheless this fine tuning depends crucially on our assumptions about the supersymmetry breaking scale. At the same time $U(1)$ extensions provide the most compelling solution to the $\mu$-problem, which is also a naturalness issue, and allow the tree level Higgs mass to be raised substantially above $M_Z$. These very well motivated supersymmetric models predict a new $Z'$ boson which could be discovered at the LHC and the naturalness of the model requires that the $Z'$ boson mass should not be too far above the TeV scale. Moreover this fine tuning appears at the tree level, making it less dependent on assumptions about the supersymmetry breaking mechanism. Here we study this fine tuning for several $U(1)$ supersymmetric extensions of the Standard Model and compare it to the situation in the MSSM where the most direct tree level fine tuning can be probed through chargino mass limits. We show that future LHC $Z'$ searches are extremely important for challenging the most natural scenarios in these models.