Tagging fully hadronic exotic decays of the vectorlike B quark using a graph neural network

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 MB=1.8:(2.4) TeV at HL-LHC when B decays fully exotically, i.e., BR(B → bPhi) = 100%.