Speaker
Meenakshi Narain
(Brown University (US))
Description
Recent advances in neural networks and harsh pileup conditions in the second half on LHC Run 2 with on average 38 PU interactions, have sparked significant developments in techniques for jet tagging. Through the study of jet substructure properties, jets originating from quarks, gluons, W/ Z/Higgs bosons, top quarks and pileup interactions are distinguished, surpassing previous performance at lower pileup conditions by using new approaches. This talk will give an overview of the development of machine learning based jet substructure algorithms and their validation using the data collected by the CMS Experiment.
