Abstract:
Ten years after the Higgs boson discovery, there are still many unknowns about this particle. While its interactions with the vector bosons and the third generation fermions have been observed, it remains a significant challenge to establish the Higgs couplings to lighter particles, such as the charm quark, a second generation fermion. In recent years, the rise of deep learning has revolutionized the analysis of large-scale data samples in particle physics and opened up many new possibilities. Empowered by state-of-the-art deep learning techniques, novel concepts in charm jet reconstruction, identification and calibration have been developed to search for the Higgs boson decay to a charm quark-antiquark pair, leading to the most stringent constraint on the Higgs-charm coupling to date, and the first observation of a similar process, the Z boson decay to a pair of charm quarks, at a hadron collider. Prospects of the Higgs-charm coupling measurement at the future HL-LHC will also be discussed.
Biography:
Dr. Huilin Qu is a Senior Research Fellow at CERN and will start as a Research Physicist in September. He received his B.S. degree from Peking University in 2014, and Ph.D. from University of California, Santa Barbara in 2019. As a member of the CMS experiment since 2014, Huilin has focused on searches for new physics and measurements of the Higgs boson properties at the LHC, particularly with novel approaches and new 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. He now serves as the co-convener of the Dibosons subgroup in the CMS Beyond-two-generations physics group. In addition, Huilin proposed several novel machine learning-based approaches for boosted jet tagging, which substantially improved the performance and have become the standard algorithms in CMS. He is also actively involved in the Phase 2 Upgrade of the CMS High Granularity Calorimeter.
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