Identification of Additional Jets in the $${\rm{t}}{\rm\bar{t}b}{\rm\bar{b}}$$ Events by Using Deep Neural Network

In the top quark pair production in association with the Higgs boson decaying to a b quark pair (t¯tH(b¯b)), the final state has an irreducible nonresonant background from the production of a top quark pair in association with a b quark pair (t¯tb¯b). Therefore, understanding of the t¯tb¯b process precisely in particular differential cross-section as functions of the properties of the additional b jets not from the top quark decay is essential for improving the sensitivity of a search for the t¯tH(b¯b) process. The two additional b jets can be identified by using various approaches. In this paper, the performances are compared quantitatively in the lepton+jets decay channel in terms of the matching efficiency of assigning two additional b jets as a figure of merit. We showed that a matching efficiency of around 40% could be achieved using a deep neural network method. In the events with at least 4 b jets, this performance is 8% better than that achieved using minimum Δ R ( b, ¯ b ) method. This is consistent with the boosted decision tree method within its statistical uncertainty.

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Identification of Additional Jets in the $${\rm{t}}{\rm\bar{t}b}{\rm\bar{b}}$$ Events by Using Deep Neural Network

Semantic Scholar · Physics · 2020

Abstract

In the top quark pair production in association with the Higgs boson decaying to a b quark pair (t¯tH(b¯b)), the final state has an irreducible nonresonant background from the production of a top quark pair in association with a b quark pair (t¯tb¯b). Therefore, understanding of the t¯tb¯b process precisely in particular differential cross-section as functions of the properties of the additional b jets not from the top quark decay is essential for improving the sensitivity of a search for the t¯tH(b¯b) process. The two additional b jets can be identified by using various approaches. In this paper, the performances are compared quantitatively in the lepton+jets decay channel in terms of the matching efficiency of assigning two additional b jets as a figure of merit. We showed that a matching efficiency of around 40% could be achieved using a deep neural network method. In the events with at least 4 b jets, this performance is 8% better than that achieved using minimum Δ R ( b, ¯ b ) method. This is consistent with the boosted decision tree method within its statistical uncertainty.

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