Juhee Song | Searcharxiv

arXiv2024

DNN-based identification of additional b jets for a differential $t\bar{t}b\bar{b}$ cross section measurement

In differential measurements of the $t\bar{t}b\bar{b}$ process, observables related to the b jets not originating from top quark decays are of special interest to probe the multi-scale QCD nature of the $t\bar{t}b\bar{b}$ process, and the description of the additional b jet radiation via different simulation tools. In order to access these additional b jets, identification algorithms have to be applied to determine which of the b jets recorded in the detector are the b jets of interest. This is achieved via a DNN-based method which will be highlighted in this article.

hep-ex↗

arXiv2019

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

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.

hep-ex↗