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

arXiv: https://arxiv.org/abs/2505.07769 · HTML: https://mlsanity.com/paper/2505.07769/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 78 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 15 of 15)

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- [Explainable Equivariant Neural Networks for Particle Physics: PELICAN](https://mlsanity.com/paper/2307.16506/) (2023)
- [HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture](https://mlsanity.com/paper/2502.03933/) (2025)
- [Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube](https://mlsanity.com/paper/2209.03042/) (2022)

## Abstract (arXiv)

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%.