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

2025-05-12

AI Summary

This study addresses the challenge of detecting vector-like B-quark pair production followed by fully hadronic decays (B → bΦ, Φ → gg/bb) at the LHC under overwhelming QCD backgrounds—particularly in lepton-free, SM-dominated final states such as 2b+4j or 6b. We propose a novel end-to-end classification architecture combining graph neural networks (GNNs) and deep neural networks (DNNs), marking the first application of GNNs to jet-level graph modeling and feature learning for fully hadronic new-physics decays. Using HL-LHC simulated data and assuming BR(B → bΦ) = 100%, our method achieves discovery and exclusion limits at M_B ≈ 1.8 TeV and 2.4 TeV, respectively—matching the sensitivity of semi-leptonic channels. This significantly extends the reach of hadron colliders for probing new neutral scalar singlets, particularly in challenging all-hadronic regimes.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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$(B o bPhi) = 100%$.

Problem

• Detecting exotic decays of vectorlike B quarks to singlet (pseudo)scalar fields
• Overcoming large Standard Model background in fully hadronic channels
• Enhancing discovery reach using hybrid graph and deep neural networks

Innovation

• Graph neural network for exotic decay tagging
• Hybrid deep learning model combining GNN and DNN
• Enhanced hadronic channel analysis for HL-LHC