Machine Learning Based Top Quark and W Jet Tagging to Hadronic Four-Top Final States Induced by SM as well as BSM Processes

TL;DR

This paper explores machine learning techniques for identifying and tagging jet substructures in hadronic final states, comparing their performance to traditional cut-based methods in high-energy physics experiments.

Contribution

It introduces ML-based jet tagging methods for distinguishing hadronic final states, demonstrating their effectiveness over conventional cut-based approaches.

Findings

ML techniques outperform cut-based methods in jet tagging accuracy

Enhanced identification of hadronic substructures in simulated events

Comparison shows ML methods provide more reliable tagging results

Abstract

We study the application of selected ML techniques to the recognition of a substructure of hadronic final states (jets) and their tagging based on their possible origin in current HEP experiments using simulated events and a parameterized detector simulation. The results are then compared with the cut-based method.