The b' search at the LHC

TL;DR

This paper investigates the production and detection of a hypothetical down-type quark at the LHC, employing full event reconstruction and neural networks to distinguish signal from background.

Contribution

It introduces a novel analysis method combining full reconstruction and neural networks for $b'$ quark search at 10 TeV LHC.

Findings

Potential to improve $b'$ detection sensitivity

Neural network effectively discriminates signal from background

Feasible with 1 fb$^{-1}$ data at 10 TeV

Abstract

We consider the production and detection of a sequential, down type quark via the mode $p p \to b' {\bar b}' \to W^+ W^- t {\bar t} \to \ell \nu_{\ell} 8 j$ at the LHC, with the collision energy $\sqrt{s}=10$ TeV and the total integrated luminosity around 1 fb$^{-1}$. We assume $m_{b'}=m_{t'}=600$ GeV. A full reconstruction is employed and the signal and background discrimination is studied within a neural network approach. Our results show that this mode can make a useful contribution to the $b'$ search.