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| Description | 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. |
| Date | Mon, February 24, 2020 |
| Time | 1:30pm-2:30pm PST |
| Duration | 1 hour |
| Access | Public |
| Created by | High-Energy Seminars |
| Updated | Tue, February 11, 2020 11:02am PST |
| Send Reminder | Yes - 0 days 4 hour 0 minutes before start |
