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A new paradigm for data-driven, model-agnostic new physics searches atcolliders is emerging, and aims to leverage recent breakthroughs in anomalydetection and machine learning. In order to develop and benchmark new anomalydetection methods within this framework, it is essential to have standarddatasets. To this end, we have created the LHC Olympics 2020, a communitychallenge accompanied by a set of simulated collider events. Participants inthese Olympics have developed their methods using an R&D dataset and thentested them on black boxes: datasets with an unknown anomaly (or not). Thispaper will review the LHC Olympics 2020 challenge, including an overview of thecompetition, a description of methods deployed in the competition, lessonslearned from the experience, and implications for data analyses with futuredatasets as well as future colliders.

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