Abstract:
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 the study of jets at high-energy particle colliders such as the LHC, leading to new insights in the past few years. In this talk, Dr. Qu will go through the evolution of deep learning approaches for jets and discuss recent advances toward establishing a foundation model for jet physics, paving the way for a more unified and powerful framework in this domain.
About the speaker:
Dr. Huilin Qu is a staff research physicist at CERN. He received his B.S. degree from Peking University in 2014, and Ph.D. from University of California, Santa Barbara in 2019. He was a postdoctoral researcher at UCSB (2019-2020) and, subsequently, a senior research fellow at CERN (2020-2022). 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 machine learning techniques. He played a key role in searches for Higgs boson decay 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, 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.
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Meeting ID 会议号: 84737509309
Meeting URL 会议链接:: https://us02web.zoom.us/j/84737509309?pwd=OBxNlGaQZdmU8KXwEJaYtaFffDXgEB.1
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