Learning to increase matching efficiency in identifying additional b-jets in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process

TL;DR

This paper introduces a deep learning approach with specialized loss functions to improve the identification of additional b-jets in $ ext{t}ar{ ext{t}} ext{b}ar{ ext{b}}$ events, enhancing the accuracy of matching efficiency crucial for Higgs property studies.

Contribution

The paper proposes novel loss functions tailored for deep learning to directly optimize matching efficiency in identifying additional b-jets in $ ext{t}ar{ ext{t}} ext{b}ar{ ext{b}}$ events, outperforming traditional binary classification methods.

Findings

Enhanced matching efficiency in identifying additional b-jets.

Deep learning with specialized loss functions outperforms binary classification.

Applicable to simulated $ ext{t}ar{ ext{t}} ext{b}ar{ ext{b}}$ event data at 13 TeV.

Abstract

The $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process is an essential channel to reveal the Higgs properties but has an irreducible background from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, which produces a top quark pair in association with a b quark pair. Therefore, understanding the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process is crucial for improving the sensitivity of a search for the $\text{t}\bar{\text{t}}\text{H}(\text{b}\bar{\text{b}})$ process. To this end, when measuring the differential cross-section of the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ process, we need to distinguish the b-jets originated from top quark decays, and additional b-jets originated from gluon splitting. Since there are no simple identification rules, we adopt deep learning methods to learn from data to identify the additional b-jets from the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ events. Specifically, by exploiting the special structure of the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data, we propose several loss functions that can be minimized to directly increase the matching efficiency, the accuracy of identifying additional b-jets. We discuss the difference between our method and another deep learning-based approach based on binary classification arXiv:1910.14535 using synthetic data. We then verify that additional b-jets can be identified more accurately by increasing matching efficiency directly rather than the binary classification accuracy, using simulated $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ event data in the lepton+jets channel from pp collision at $\sqrt{s}$ = 13 TeV.