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

Understand

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.