
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 at the LHC, with the collision energy TeV and the total integrated luminosity around 1 fb. We assume 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 search.
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