A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques

TL;DR

This paper evaluates the statistical coverage of confidence and credible intervals in the CMSSM parameter space using ATLAS data, introducing a neural network method to significantly speed up computations.

Contribution

It presents a novel neural network approach to efficiently approximate the mapping between CMSSM parameters and particle masses, enabling faster coverage studies.

Findings

Both Bayesian and profile likelihood intervals can over-cover due to physical boundaries.

The neural network reduces computational effort by a factor of 10,000.

Intrinsic statistical effects are intertwined with experimental likelihood simplifications.

Abstract

We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of ~ 10^4 with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves.