Speaker
Dewen Zhong
(Univ. Illinois at Urbana Champaign (US))
Description
Identifying $𝑊𝑊^{(\ast)}\rightarrow\ell\nu qq$ from heavy particle decays at the LHC is an important but challenging problem due to overlapping lepton and jet signatures. We have developed a deep learning-based $𝑊𝑊^{(\ast)}$ tagger which learns from simulated calorimeter features to identify boosted $𝑊𝑊^{(\ast)}$ decays to semileptonic final states from $t\bar{t}$ and di-jet backgrounds in ATLAS. In this talk, we present the methods applied to the tagger development in the electron channel and some preliminary performance results on simulated ATLAS events at $\sqrt{s}=13$ TeV.
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Primary author
Dewen Zhong
(Univ. Illinois at Urbana Champaign (US))