Convergent Bayesian Global Fits of 4D Composite Higgs Models
Ethan Carragher, Will Handley, Daniel Murnane, Peter Stangl, Wei Su,, Martin White, Anthony G. Williams

TL;DR
This paper performs Bayesian global fits on three minimal composite Higgs models to identify parameter regions consistent with experimental constraints, analyze fine-tuning, and explore collider phenomenology, highlighting potential experimental signatures.
Contribution
It introduces a novel Bayesian fitting approach to 4D composite Higgs models, quantifies fine-tuning via Kullback-Leibler divergence, and assesses collider phenomenology of viable models.
Findings
All models satisfy constraints at 3σ level.
Two models predict Higgs to diphoton cross section below 90% of SM.
Lightest fermions are above 1.1 TeV, with promising collider signatures.
Abstract
Models in which the Higgs boson is a composite pseudo-Nambu-Goldstone boson offer attractive solutions to the Higgs mass naturalness problem. We consider three such models based on the minimal symmetry breaking pattern, and perform convergent global fits on the models under a Bayesian framework in order to find the regions of their parameter spaces that best fit a wide range of constraints, including recent Higgs measurements. We use a novel technique to analyse the fine-tuning of the models, quantifying the tuning as the Kullback-Leibler divergence from the prior to the posterior probability on the parameter space. Each model is found to be able to satisfy all constraints at the level simultaneously. As a by-product of the fits, we analyse the collider phenomenology of our models in these viable regions. In two of the three models, we find that the…
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