  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.
