Deep sets and event-level maximum-likelihood estimation for fast pile-up jet rejection in ATLAS

This article likely discusses the application of deep learning techniques, specifically deep sets and maximum-likelihood estimation, to improve the rejection of pile-up jets in the ATLAS experiment. The focus is on achieving faster and more efficient jet rejection, which is crucial for high-energy physics experiments.

• •The research focuses on improving jet rejection in the ATLAS experiment.
• •It utilizes deep learning methods, including deep sets and maximum-likelihood estimation.
• •The goal is to achieve faster and more efficient jet rejection.