Speaker: David Shih
Title: New Approaches to Anomaly Detection at the LHC and Beyond
Host: Da Liu
Room: 432
Abstract:
Deep learning is having a major impact on many aspects of LHC physics. One especially exciting area with many recent developments is that of model-independent searches for new physics. This can be framed as a classic anomaly detection problem in unsupervised machine learning. In this talk, I will give a comprehensive overview of a number of recently proposed methods for anomaly detection motivated by the search for new physics at the LHC. This includes methods based on autoencoders, weak supervision, density estimation and simulation-assisted reweighting. I will also summarize the status of the ongoing "LHC Olympics 2020" anomaly detection data challenge, where many of these techniques are being applied to "black box" datasets by a number of groups from around the world.
