The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
Gregor Kasieczka, Benjamin Nachman, David Shih, O. Amram, Anders Andreassen, Kees Benkendorfer, Blaž Bortolato, G. Brooijmans, F. Canelli, Jack H. Collins, Biwei Dai, Felipe F. Freitas, Barry M. Dillon, I-M. Dinu, Zhongtian Dong, J. Donini, J. Duarte, Darius A. Faroughy, J. L. Gonski, Philip Harris, Alan Mathew Kahn, Jernej F. Kamenik, Charanjit K. Khosa, Patrick Komiske, L. T. Le Pottier, Pablo Martín-Ramiro, Andrej Matevc, Eric Metodiev, V. M. Mikuni, Christopher W Murphy, I. Ochoa, Sang Eon Park, M. Pierini, Dylan Rankin, Veronica Sanz, Nilai Sarda, Urŏ Seljak, Aleks Smolkovič, George Stein, Cristina Mantilla Suarez, Manuel Szewc, Jesse Thaler, Steven Tsan, Silviu‐Marian Udrescu, Louis Vaslin, Jean-Roch Vlimant, D. M. Williams, Mikaeel Yunus
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). 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.