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 Trotta

    Research output: Contribution to journalArticle

    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 confated with simplifications to the likelihood functions from the experiments themselves.

    Original languageEnglish (US)
    Article number012
    JournalJournal of High Energy Physics
    Volume2011
    Issue number3
    DOIs
    StatePublished - 2011

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    sensitivity
    intervals
    particle mass
    profiles
    simplification
    confidence

    Keywords

    • Supersymmetry phenomenology

    ASJC Scopus subject areas

    • Nuclear and High Energy Physics

    Cite this

    A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques. / Bridges, Michael; Cranmer, Kyle; Feroz, Farhan; Hobson, Mike; De Austri, Roberto Ruiz; Trotta, Roberto.

    In: Journal of High Energy Physics, Vol. 2011, No. 3, 012, 2011.

    Research output: Contribution to journalArticle

    Bridges, Michael ; Cranmer, Kyle ; Feroz, Farhan ; Hobson, Mike ; De Austri, Roberto Ruiz ; Trotta, Roberto. / A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques. In: Journal of High Energy Physics. 2011 ; Vol. 2011, No. 3.
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