Spectral Analysis of Jet Substructure with Neural Networks: Boosted Higgs Case
This work addresses jet substructure analysis in high-energy physics, offering an incremental improvement for particle identification in collider experiments.
The paper tackled the problem of distinguishing jets from boosted heavy particles like Higgs bosons from QCD jets by introducing a machine learning strategy using spectral analysis on angular scales, achieving performance similar to existing taggers with some improvement in cases of extra radiation.
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.