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). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. 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.

中文翻译：

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2020 年 LHC 奥运会是高能物理异常检测的社区挑战

在对撞机上进行数据驱动、与模型无关的新物理搜索的新范式正在出现，旨在利用最近在异常检测和机器学习方面的突破。为了在此框架内开发和基准测试新的异常检测方法，必须拥有标准数据集。为此，我们创建了 LHC Olympics 2020，这是一个社区挑战，伴随着一组模拟对撞机事件。这些奥运会的参与者已经使用研发数据集开发了他们的方法，然后在黑盒上对其进行了测试：具有未知异常（或没有异常）的数据集。方法利用现代机器学习工具，并基于无监督学习（自动编码器、生成对抗网络、规范化流）、弱监督学习和半监督学习。