Higgs boson tagging with the Lund jet plane

TL;DR

This paper introduces a novel Higgs boson tagging method using the Lund jet plane and CNNs to improve discrimination of boosted Higgs decays from background, outperforming traditional techniques.

Contribution

The study develops a new jet tagging approach based on Lund jet plane images and deep learning, enhancing Higgs identification in boosted regimes.

Findings

The Lund jet plane-based tagger outperforms traditional single-variable methods.

The approach is effective for different decay modes and boost scenarios.

Deep learning improves Higgs signal-background separation.

Abstract

We construct a procedure to separate boosted Higgs bosons decaying into hadrons, from the background due to strong interactions. We employ the Lund jet plane to obtain a theoretically well-motivated representation of the jets of interest and we use the resulting images as the input to a convolutional neural network classifier. In particular, we consider two different decay modes of the Higgs boson, namely into a pair of bottom quarks or into light jets, against the respective backgrounds. For each case, we consider both a moderate- and high- boost scenario. The performance of the tagger is compared to what is achieved using a traditional single-variable analysis which exploits a QCD inspired color-singlet tagger, namely the jet color ring observable.