Identification of additional jets in the t-tbar b-bbar events using a deep neural network

TL;DR

This paper demonstrates that a deep neural network can effectively identify additional b jets in t-tbar b-bbar events, achieving a 40% matching efficiency and outperforming traditional methods, thereby aiding precise differential cross-section measurements.

Contribution

The study introduces a deep neural network approach for identifying additional b jets in t-tbar b-bbar events, improving matching efficiency over traditional methods.

Findings

Deep neural network achieves ~40% matching efficiency.

Performance exceeds minimum Delta R(b,bbar) method by 8%.

Results are consistent with boosted decision tree within uncertainties.

Abstract

In the top quark pair production in association with the Higgs boson decaying to a b quark pair t-tbar H (b-bbar), the final state has an irreducible nonresonant background from the production of a top quark pair in association with a b quark pair t-tbar b-bbar. Therefore, understanding of the t-tbar b-bbar 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-tbar H b-bbar 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. This study provides valuable information towards the precise measurement of differential cross-sections as a function of properties of the additional b jets in the t-tbar b-bbar events. 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 Delta R(b,bbar) method. This is consistent with the boosted decision tree method within its statistical uncertainty.