A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques

  • Michael Bridges
  • Kyle Cranmer
  • Farhan Feroz
  • Mike Hobson
  • Roberto Ruiz de Austri
  • Roberto TrottaEmail author


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.


Supersymmetry Phenomenology 


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Copyright information

© SISSA, Trieste, Italy 2011

Authors and Affiliations

  • Michael Bridges
    • 1
  • Kyle Cranmer
    • 2
  • Farhan Feroz
    • 1
  • Mike Hobson
    • 1
  • Roberto Ruiz de Austri
    • 3
  • Roberto Trotta
    • 4
    Email author
  1. 1.Astrophysics Group, Cavendish LaboratoryUniversity of CambridgeCambridgeU.K.
  2. 2.Center for Cosmology and Particle PhysicsNew York UniversityNew YorkU.S.A.
  3. 3.Instituto de Física Corpuscular, IFIC-UV/CSICValenciaSpain
  4. 4.Astrophysics Group, Imperial College LondonBlackett LaboratoryLondonU.K.

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