Speaker
Daniel Sun
(University of Washington)
Description
Anomaly detection techniques offer exciting possibilities to significantly extend the search for new physics at the Large Hadron Collider (LHC) in a model-agnostic approach. We study how Generative Adversarial Networks could be used for this purpose, using the LHC Olympics 2020 dataset as an example.
Primary authors
Ben Nachman
(Lawrence Berkeley National Lab. (US))
Daniel Sun
(University of Washington)
David Shih
(Rutgers University)
Dukaixuan Ling
(University of Washington)
Elham E Khoda
(University of Washington (US))
Htet Aung Myin
(University of Washington (US))
Ines Ochoa
(LIP Laboratorio de Instrumentacao e Fisica Experimental de Part)
Shih-Chieh Hsu
(University of Washington Seattle (US))
