Fine-tuning in GGM and the 126 GeV Higgs particle

TL;DR

This paper reanalyzes fine-tuning in supersymmetric GGM models considering recent Higgs mass measurements and muon g-2 data, finding that g-2 constraints generally increase fine-tuning but certain model adjustments can reduce it.

Contribution

It introduces simplified relations among GGM parameters to reduce fine-tuning and explores the impact of muon g-2 data on model viability and tuning levels.

Findings

GGM models can achieve correct Higgs mass with less fine-tuning than standard models.

Current g-2 data tend to increase the fine-tuning requirement.

Removing certain contributions can allow low fine-tuning solutions when g-2 constraints are ignored.

Abstract

In this paper we reanalyze the issue of fine-tuning in supersymmetric models which feature Generalized Gauge Mediation (GGM) in the light of recent measurement of the mass of the light Higgs particle and taking into account available data on the value of the muon magnetic moment $g_\mu-2$. We consider GGM models with 3, 5 and 6 input parameters and reduce the fine-tuning by assuming simple relations between them at the high scale. We are able to find solutions which give the correct value of the light Higgs mass and are less fine-tuned than models with standard gauge mediation (and with gravity mediation), however one never finds fine-tung measure lower than about $10^2$ if one neglects the data on $g_\mu-2$ and and about four times more if one takes the constraint given by $g_\mu-2$ into account. In general the current $g_\mu-2$ data push the models towards the high fine-tuning region. It is interesting to note, that once one removes the contributions to the finetuning induced by $\mu$ and $B_\mu$, then in the case with neglected $g_\mu-2$ constraint one can easily find realistic vacua with fine-tuning of order 1 or lower, while the fine-tung remains always large when the $g_\mu-2$ constraint is enforced. One should note, that in the last case even a small shift of the light Higgs mass towards smaller values both reduces fine-tuning and helps to improve agreement of a model with $g_\mu-2$ data.