Recent jet and jet substructure measurements at the LHC, and ML based tagging

TL;DR

This paper reviews recent measurements of jets and jet substructure at the LHC, highlighting the role of machine learning techniques in tagging, and discusses their implications for testing the Standard Model and exploring new physics.

Contribution

It presents recent experimental results on jet substructure and ML-based tagging at the LHC, and compares them with Monte Carlo predictions to improve theoretical models.

Findings

Measurements agree with several Monte Carlo predictions

ML-based tagging enhances jet identification accuracy

Results constrain parameters of parton-shower models

Abstract

Recent jet and jet substructure measurements at the LHC, and of machine-learning-based tagging techniques are presented using proton-proton collision data collected by the ATLAS and CMS experiments at CERN's Large Hadron Collider. These measurements are crucial for precise tests of electroweak and pQCD calculations and searches for physics beyond the Standard Model. The measurements are compared with several Monte Carlo event generator predictions which provide valuable input to the tuning of perturbative and non-perturbative models and to constraining model parameters of advanced parton-shower Monte Carlo programs.