Maximum performance of strange-jet tagging at hadron colliders

TL;DR

This study estimates the maximum potential and detector design impacts on strange-jet tagging performance at hadron colliders using neural networks and various detector simulations.

Contribution

It introduces a comprehensive simulation-based analysis of strange-jet tagging performance limits and detector effects using advanced neural network techniques.

Findings

Optimal tagging performance limits identified

Impact of detector components on tagging efficiency quantified

Manual strange hadron decay reconstruction evaluated

Abstract

The maximum achievable performance of strange-jet tagging at hadron colliders and the loss in performance in different detector designs is estimated based on simulated truth jets from strange-quark and down-quark hadronisation. Both jet types are classified with a recurrent neural network using long short-term memory units, at first using all available truth particles and then applying selections to study the impacts of ideal tracking detectors, Cherenkov detectors, and calorimeters. Additionally, a manual reconstruction of strange hadron decays such as $K_S\rightarrow \pi^+ \pi^-$ from charged tracks is considered.