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

Gregor Kasieczka
Benjamin Nachman
David Shih
Oz Amram
Kees Benkendorfer
Blaz Bortolato
Gustaaf Brooijmans
Florencia Canelli
Jack H. Collins
Biwei Dai
Felipe F. De Freitas
Barry M. Dillon
Ioan-Mihail Dinu
Zhongtian Dong
Julien Donini
Javier Duarte
A. Faroughy
Julia Gonski
Philip Harris
Alan Kahn
Jernej F. Kamenik
Charanjit K. Khosa
Patrick Komiske
Luc Le Pottier
Pablo Mart´ın-Ramiro
Andrej Matevc
Eric Metodiev
Vinicius Mikuni
Inˆes Ochoa
Sang Eon Park
Maurizio Pierini
Dylan Rankin
Veronica Sanz
Nilai Sarda
Uro˘s Seljak
Aleks Smolkovic
George Stein
Cristina Mantilla Suarez
Manuel Szewc
Jesse Thaler
Steven Tsan
Silviu-Marian Udrescu
Louis Vaslin
Jean-Roch Vlimant
Daniel Williams
Mikaeel Yunus
Rept.Prog.Phys., 84 (2021)
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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.

Research Areas