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Abstract:
Machine learning (ML) has become a transformative tool in high energy physics. In this seminar, I will demonstrate how high energy physics problems can be reframed as ML tasks, and highlight the application of ML techniques to enhance particle reconstruction and identification, particularly at the Large Hadron Collider (LHC). Additionally, I will discuss unsupervised ML methods for calorimeter shower simulations and anomaly detection, aimed at uncovering potential signals of new physics. This overview will provide insights into the current state and future directions of ML in advancing particle physics research.
Biography:
Professor Rui Zhang is a member of the ATLAS experiment at the Large Hadron Collider and the BESIII experiment in Beijing. He obtained his Ph.D. from the University of Bonn, Germany, and subsequently conducted postdoctoral research at the University of Wisconsin-Madison. In 2024, he joined Nanjing University as a “Zhicheng Young Scholar”.
His research focuses on the measurement of Higgs self-coupling and searches for Beyond the Standard Model physics, using the top quark and Higgs boson as probes. He also has a strong interest in developing computational techniques, including anomaly detection, machine learning for physics simulation, and quantum machine learning applications. He is currently a convener of the ATLAS Higgs and diHiggs combination group, ATLAS fast simulation group, and a mentor of Google summer of code Quantum machine learning project group.
Alternative online link：https://meeting.tencent.com/dm/FXp28rnlzS0V
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