Publications [#315434] of Mark C. Kruse

Papers Published
  1. Atlas Collaboration, ; Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Aben, R; Abolins, M; AbouZeid, OS; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, BS; Adamczyk, L; Adams, DL; Adelman, J; Adomeit, S; Adye, T; Affolder, AA; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, JA; Ahlen, SP; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, TPA; Akimov, AV; Alberghi, GL; Albert, J; Albrand, S; Alconada Verzini, MJ; Aleksa, M; Aleksandrov, IN; Alexa, C; Alexander, G; Alexopoulos, T et al., A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector., The European Physical Journal C - Particles and Fields, vol. 76 no. 5 (January, 2016), pp. 238 [doi] .

    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 [Formula: see text] for bosons in the transverse momentum range 200 GeV [Formula: see text] 400 GeV in [Formula: see text] TeV pp collisions with the ATLAS detector at the LHC. For Z-boson tagging efficiencies of [Formula: see text], 50, and [Formula: see text], one can achieve [Formula: see text]-boson tagging rejection factors ([Formula: see text]) 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 [Formula: see text], hadronically decaying Z bosons. However, the modelling of the tagger inputs for boosted W bosons is studied in data using a [Formula: see text]-enriched sample of events in 20.3 fb[Formula: see text] of data at [Formula: see text] TeV. The inputs are well modelled within uncertainties, which builds confidence in the expected tagger performance.