Application of Machine Learning Techniques to study Jet Substructure

High Lorentz boosts pose a challenge to the reconstruction of hadronically decaying heavy particles (top, W, Z, H) as their decay products are collimated. An efficient identification of hadronically decaying heavy particles increases the sensitivity in searches for heavy new (beyond the standard model) particles and opens the high momentum phase space for standard model measurements of the top quark, W, Z and H. Machine learning for heavy flavor jet-tagging have been increasingly explored. In this talk, we present the machine learning based heavy-tagging algorithms studied in the CMS experiment at 13 TeV. The performance of these algorithms is studied in simulation.