  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.
