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

TL;DR

The paper introduces the LHC Olympics 2020, a community challenge designed to benchmark anomaly detection methods in high energy physics using standard simulated datasets, fostering development of model-agnostic new physics searches.

Contribution

It presents a new benchmark challenge with standardized datasets for anomaly detection in collider data, enabling comparison and development of novel machine learning methods.

Findings

Participants developed diverse anomaly detection methods.

Lessons learned inform future collider data analyses.

Benchmark datasets facilitate method comparison.

Abstract

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.