Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case

TL;DR

This paper presents a neural network-based spectral analysis method for jet substructure, effectively distinguishing boosted Higgs jets from QCD jets by analyzing angular spectra and capturing multi-point correlations.

Contribution

Introduces a spectral function approach combined with neural networks for jet substructure analysis, capturing multi-point correlations and improving tagging performance.

Findings

Performance comparable to existing taggers on Higgs and QCD jets

Enhanced discrimination with additional radiations

Automatically captures multi-point correlations in jets

Abstract

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.