Speaker
Rui Zhang
(University of Wisconsin-Madison)
Description
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.
Primary author
Rui Zhang
(University of Wisconsin-Madison)
