A heavy gluino can push the physical stop mass above LHC bounds while the underlying stop mass parameter stays near the electroweak scale, solving the little fine-tuning problem when higher-order corrections are resummed.
Bayesian naturalness, simplicity, and testability applied to the $B-L$ MSSM GUT
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Recent years have seen increased use of Bayesian model comparison to quantify notions such as naturalness, simplicity, and testability, especially in the area of supersymmetric model building. After demonstrating that Bayesian model comparison can resolve a paradox that has been raised in the literature concerning the naturalness of the proton mass, we apply Bayesian model comparison to GUTs, an area to which it has not been applied before. We find that the GUTs are substantially favored over the non-unifying puzzle model. Of the GUTs we consider, the $B-L$ MSSM GUT is the most favored, but the MSSM GUT is almost equally favored.
fields
hep-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Little hierarchies solve the little fine-tuning problem: a case study in supersymmetry with heavy guinos
A heavy gluino can push the physical stop mass above LHC bounds while the underlying stop mass parameter stays near the electroweak scale, solving the little fine-tuning problem when higher-order corrections are resummed.