Speaker
Description
Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model (BSM) in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have the potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.
