Probing Singlet Vector-Like Top Quarks in the Hadronic tZ Channel at the HL-LHC using Machine and Deep Learning Architectures

Abstract

In this work, we study the single production of a vector-like singlet top partner \( T \) at the 14 TeV HL-LHC in the channel \( pp \to T j \) with \( T \to t Z \), \( t \to b W \to b j j \), and \( Z \to \nu \bar{\nu} \). Signal and background samples are generated with MadGraph5\_aMC@NLO v3.5.11, showered with Pythia 8, and passed through Delphes. The dominant backgrounds are \( t \bar{t} \), \( t Z j \), \( ZZ j j \), and \( W Z j j \) (including charge conjugates). A hadronic pre-selection (\( N_j \geq 3 \), \( N_b \geq 1 \), \( N_\ell = 0 \)) is imposed as trigger, followed by optimized kinematic cuts. We perform multivariate classification with Extreme Gradient Boosting (XGBoost) and a Graph Neural Network (GNN) based on jet-level features. Sensitivities at 3000 fb\(^{-1}\) are quoted using the Asimov significance, \( S / \sqrt{S + B} \), and an Asimov variant with a 20\% background systematic. The model parameters \( g^* \) and \( R_L \) are defined in Sec.~2, and a single global working point is used to avoid per-mass tuning bias. In the \( (g^*, m_T) \) scan, we present 2\(\sigma\) exclusion and 5\(\sigma\) discovery contours for \( R_L = 0 \) and \( R_L = 0.5 \). For \( R_L = 0 \), 2\(\sigma\) exclusion corresponds to \( g^* \in [0.17, 0.49] \) (\( 0.16, 0.43 \)) over \( m_T \in [1.8, 2.7] \) TeV, while 5\(\sigma\) discovery corresponds to \( g^* \in [0.27, 0.44] \) (\( 0.26, 0.40 \)) over \( m_T \in [1.8, 2.2] \) TeV for XGBoost and GNN respectively. For \( R_L = 0.5 \), the 2\(\sigma\) reach is \( g^* \in [0.21, 0.48] \) (\( 0.20, 0.43 \)) over \( m_T \in [1.8, 2.5] \) TeV, and the 5\(\sigma\) reach is \( g^* \in [0.33, 0.43] \) (\( 0.31, 0.49 \)) over \( m_T \in [1.8, 2.2] \) TeV, with the GNN yielding slightly stronger and smoother limits across the scan.