A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques
File(s)
Accepted version
Author(s)
Type
Journal Article
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 ~ 104 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 conated with simplifications to the likelihood functions from the experiments themselves.
Date Issued
2011-03-02
Date Acceptance
2011-02-23
Citation
Journal of High Energy Physics, 2011, 2011 (3)
ISSN
1126-6708
Publisher
Springer
Journal / Book Title
Journal of High Energy Physics
Volume
2011
Issue
3
Copyright Statement
© SISSA 2011
Subjects
Science & Technology
Physical Sciences
Physics, Particles & Fields
Physics
PHYSICS, PARTICLES & FIELDS
Supersymmetry
Phenomenology
PARAMETER SPACE
RELIC DENSITY
SUPERSYMMETRY
SUPERGRAVITY
MODEL
hep-ph
hep-ex
physics.data-an
Nuclear & Particles Physics
01 Mathematical Sciences
02 Physical Sciences
Publication Status
Published
Article Number
012