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https://cern.zoom.us/j/68985728334?pwd=Q283oQz8WH2yn8ojwax2HbxaxeFzF8.1
Meeting ID: 689 8572 8334
Passcode: 275751
Abstract
Machine learning (ML) has become a transformative tool in high energy physics. In this talk, 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.
CV:
Dr. Rui Zhang is working with the ATLAS experiment in the University of Wisconsin-Madison, US. He obtained his Ph.D. in the University of Bonn, Germany, with the research on the single top quark production at ATLAS. He had worked in the BESIll collaboration in the UCAS on the topic of a charm quark FCNC decay for the Master degree, and studied in Tianjin University for his Bachelor's degree. His current interest includes di-Higgs searches, top quark measurements, anomaly detection, machine learning for fast calorimeter simulation and quantum machine learning applications.