Lund jet plane for Higgs tagging

TL;DR

This paper explores a novel Higgs boson tagging method using Lund jet plane images and convolutional neural networks, demonstrating improved efficiency over traditional cut-based techniques across various decay modes and momentum ranges.

Contribution

It introduces a new jet tagging approach leveraging Lund jet plane images and CNNs, outperforming traditional methods in Higgs identification.

Findings

Lund jet plane images enable effective Higgs tagging.

CNN-based classification improves tagging efficiency.

Method performs well across different decay modes and momenta.

Abstract

We study the boosted Higgs tagging using the Lund jet plane. The convolutional neural network is used for the Lund images data set to classify hadronically decaying Higgs from the QCD background. We consider $H\to b \bar{b}$ and $H \to gg$ decay for moderate and high Higgs transverse momentum and compare the performance with the cut based approach using the jet color ring observable. The approach using Lund plane images provides good tagging efficiency for all the cases.