Jul 6 – 8, 2021
Europe/Zurich timezone

Generative Adversarial Networks for Anomaly Detection at the LHC

Not scheduled


Daniel Sun (University of Washington)


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))

Presentation materials

There are no materials yet.