Szczegóły publikacji

Opis bibliograficzny

A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector / G. Aad, [et al.], L. ADAMCZYK, [et al.], T. BOŁD, [et al.], W. DĄBROWSKI, [et al.], M. DYNDAŁ, [et al.], G. P. GACH, [et al.], I. GRABOWSKA-BOŁD, [et al.], D. KISIELEWSKA, [et al.], S. KOPERNY, [et al.], T. Z. KOWALSKI, [et al.], B. MINDUR, [et al.], M. PRZYBYCIEŃ, [et al.], A. ZEMŁA, [et al.] // The European Physical Journal. C, Particles and Fields ; ISSN 1434-6044. — 2016 — vol. 76 iss. 5, s. 238-1–238-33. — Bibliogr. s. 238-20–238-21, Abstr. — Publikacja dostępna online od: 2016-04-28

Autorzy (3000)

Dane bibliometryczne

ID BaDAP98190
Data dodania do BaDAP2016-07-14
Tekst źródłowyURL
DOI10.1140/epjc/s10052-016-4065-1
Rok publikacji2016
Typ publikacjiartykuł w czasopiśmie
Otwarty dostęptak
Creative Commons
Czasopismo/seriaEuropean Physical Journal, C

Abstract

The distribution of particles inside hadronic jets produced in the decay of boosted W and Z bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the hadronic decay of a boosted W or Z boson, this paper presents a technique for further differentiating Z bosons from W bosons. The variables used are jet mass, jet charge, and a b-tagging discriminant. A likelihood tagger is constructed from these variables and tested in the simulation of W′→WZ for bosons in the transverse momentum range 200 GeV <pT< 400 GeV in √s=8 TeV pp collisions with the ATLAS detector at the LHC. For Z-boson tagging efficiencies of ϵZ=90, 50, and 10%, one can achieve W+-boson tagging rejection factors (1/ϵW+) of 1.7, 8.3 and 1000, respectively. It is not possible to measure these efficiencies in the data due to the lack of a pure sample of high pT, hadronically decaying Z bosons. However, the modelling of the tagger inputs for boosted W bosons is studied in data using a tˉt-enriched sample of events in 20.3 fb−1 of data at √s=8 TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.

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