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Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at √s = 8 TeV G. Aad, B. Abbott, J. Abdallah [et al.]

Contributor(s): Abbott, B | Abdallah, J | Chelkov, G. A | Aad, GMaterial type: ArticleArticleSubject(s): ATLAS, детектор | Большой адронный коллайдер | W-бозоныGenre/Form: статьи в журналах Online resources: Click here to access online In: The European physical journal C Vol. 76, № 3. P. 154 (1-47)Abstract: This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb−1 of proton–proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy √s=8TeV. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijet background efficiencies of 2–4 % for jets with the same transverse momentum. These signal and background efficiencies are confirmed in data for a selection of tagging techniques.
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Библиогр.: 82 назв.

This paper reports a detailed study of techniques for identifying boosted, hadronically decaying W bosons using 20.3 fb−1 of proton–proton collision data collected by the ATLAS detector at the LHC at a centre-of-mass energy √s=8TeV. A range of techniques for optimising the signal jet mass resolution are combined with various jet substructure variables. The results of these studies in Monte Carlo simulations show that a simple pairwise combination of groomed jet mass and one substructure variable can provide a 50 % efficiency for identifying W bosons with transverse momenta larger than 200 GeV while maintaining multijet background efficiencies of 2–4 % for jets with the same transverse momentum. These signal and background efficiencies are confirmed in data for a selection of tagging techniques.

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