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

Jets from boosted heavy particles have a typical angular scale which can be used to distinguish it 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 discrimination of Higgs jets from QCD jets as an example, we show that the ANN based on the angular spectrum has similar performance to existing taggers. In addition, some improvement is seen in the case that additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multi-point correlations in the jet.