PT Journal
AU Bridges, M
   Cranmer, K
   Feroz, F
   Hobson, M
   Ruiz de Austri, R
   Trotta, R
TI A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques
SO Journal of High Energy Physics
JI J. High Energy Phys.
PY 2011
BP 012
EP 23pp
VL 03
IS 3
DI 10.1007/JHEP03(2011)012
LA English
DE Supersymmetry; Phenomenology
AB 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 similar to 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.
ER