Finding physics signals with event deconstruction

Abstract

We develop a matrix element based reconstruction method called event deconstruction. The method uses information from the hard matrix element and a parton shower to assign probabilities to whether a final state was initiated by a signal or background process. We apply this method in the signal process of a Z' decaying to boosted top quarks in an all hadronic final state and discuss envisioned improvements of the method. We find that event deconstruction can considerably improve on existing reconstruction techniques.

17 pages, 4 figures

References in corpus (12)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• Event generation with SHERPA 1.1
• Herwig++ Physics and Manual
• Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions
• Performance of jet substructure techniques for large-R jets in proton-proton collisions at sqrt(s) = 7 TeV using the ATLAS detector
• Automation of the matrix element reweighting method
• A parton shower based on factorization of the quantum density matrix
• Unravelling $t\bar{t}h$ via the matrix element method
• Precision studies of the Higgs boson decay channel H -> ZZ -> 4l with MEKD
• Maximum Significance at the LHC and Higgs Decays to Muons
• Finding the Higgs boson in decays to Z gamma using the matrix element method at Next-to-Leading Order

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