Jet Tagging in the Era of Deep Learning

Machine learning has revolutionized the analysis of large-scale data samples in particle physics and greatly increased the discovery potential for new fundamental laws of nature. Specifically, deep learning has transformed how jet tagging, a critical classification task at high-energy particle colliders such as the CERN LHC, is performed, leading to a drastic improvement in its performance in the past few years. In this talk, Dr. Qu will go through recent progress in deep learning approaches for jet tagging and their applications in Higgs boson studies and new physics searches at the LHC. Prospects and possible future directions will also be discussed.

Dr. Huilin Qu is a Senior Research Fellow at CERN. He received his B.S. degree from Peking University in 2014, and Ph.D. from the University of California, Santa Barbara in 2019. His research has focused on searches for new physics and measurements of the Higgs boson properties with the CMS experiment at the CERN LHC, particularly using novel approaches and advanced techniques such as machine learning. He played a key role in searches for the Higgs boson decaying to a pair of charm quarks, for Higgs boson pair production in the high-momentum regime, and for supersymmetric partners of the top quark. In addition, Huilin is active in machine learning research for jet physics. He proposed a series of novel deep-learning approaches for jet tagging, which substantially improved the performance and have been widely adopted at the LHC and beyond.