Tagging fully hadronic exotic decays of the vectorlike $\mathbf{B}$ quark using a graph neural network

TL;DR

This paper develops a graph neural network-based deep learning method to detect fully hadronic exotic decays of vectorlike B quarks at the LHC, achieving sensitivity comparable to semi-leptonic channels despite large backgrounds.

Contribution

It introduces a hybrid deep learning approach combining graph neural networks with deep neural networks for challenging fully hadronic decay channels.

Findings

Potential to discover B quarks up to 1.8 TeV at HL-LHC

Exclusion limits extend to 2.4 TeV for B quark mass

Deep learning improves sensitivity in hadronic decay channels

Abstract

Following up on our earlier study in [J. Bardhan et al., Machine learning-enhanced search for a vectorlike singlet B quark decaying to a singlet scalar or pseudoscalar, Phys. Rev. D 107 (2023) 115001; arXiv:2212.02442], we investigate the LHC prospects of pair-produced vectorlike $B$ quarks decaying exotically to a new gauge-singlet (pseudo)scalar field $\Phi$ and a $b$ quark. After the electroweak symmetry breaking, the $\Phi$ decays predominantly to $gg/bb$ final states, leading to a fully hadronic $2b+4j$ or $6b$ signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semi-leptonic mode, taking the discovery (exclusion) reach up to about $M_B=1.8\:(2.4)$ TeV at HL-LHC when $B$ decays fully exotically, i.e., BR$(B \to b\Phi) = 100\%$.