15-19 February 2021
Europe/Paris timezone

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

18 Feb 2021, 15:00
virtual (online only) (CERN)

virtual (online only)



Ben Nachman (Lawrence Berkeley National Lab. (US))


A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. Based on https://arxiv.org/abs/2101.08320 .

Primary authors

Ben Nachman (Lawrence Berkeley National Lab. (US)) Gregor Kasieczka (Hamburg University (DE)) David Shih (Rutgers University)

Presentation Materials