Speaker
Hancheng Li
(University Of California, Santa Barbara)
Description
Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model. State-of-the-art machine learning methods offer impressive performance on anomaly detection tasks, but interpretability, resource, and memory concerns motivate considering a wide range of alternatives. We explore using the 2-Wasserstein distance from optimal transport theory, both as an anomaly score and as input to interpretable machine learning methods, for event-level anomaly detection at the Large Hadron Collider. The choice of ground space plays a key role in optimizing performance. We comment on the feasibility of implementing these methods in the L1 trigger system.
Authors
Hancheng Li
(University Of California, Santa Barbara)
Dr
Jessica Howard
(Kavli Institute for Theoretical Physics)
Prof.
Nathaniel Craig
(Kavli Institute for Theoretical Physics & University of California, Santa Barbara)
