Physics – High Energy Physics – High Energy Physics - Phenomenology
Scientific paper
2010-11-18
JHEP 1103:012,2011
Physics
High Energy Physics
High Energy Physics - Phenomenology
Further checks about accuracy of neural network approximation, fixed typos, added refs. Main results unchanged. Matches versio
Scientific paper
10.1007/JHEP03(2011)012
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.
Bridges Michael
Cranmer Kyle
Feroz Farhan
Hobson Michael
Ruiz de Austri Roberto
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