The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics. (7th December 2021)

• Record Type: Journal Article
• Authors: Kasieczka, Gregor; Nachman, Benjamin; Shih, David; Amram, Oz; Andreassen, Anders; Benkendorfer, Kees; Bortolato, Blaz; Brooijmans, Gustaaf; Canelli, Florencia; Collins, Jack H; Dai, Biwei; De Freitas, Felipe F; Dillon, Barry M; Dinu, Ioan-Mihail; Dong, Zhongtian; Donini, Julien; Duarte, Javier; Faroughy, D A; Gonski, Julia; Harris, Philip; Kahn, Alan; Kamenik, Jernej F; Khosa, Charanjit K; Komiske, Patrick; Le Pottier, Luc; Martín-Ramiro, Pablo; Matevc, Andrej; Metodiev, Eric; Mikuni, Vinicius; Murphy, Christopher W; Ochoa, Inês; Park, Sang Eon; Pierini, Maurizio; Rankin, Dylan; Sanz, Veronica; Sarda, Nilai; Seljak, Urŏ; Smolkovic, Aleks; Stein, George; Suarez, Cristina Mantilla; Szewc, Manuel; Thaler, Jesse; Tsan, Steven; Udrescu, Silviu-Marian; Vaslin, Louis; Vlimant, Jean-Roch; Williams, Daniel; Yunus, Mikaeel;
• Is Part Of: Reports on progress in physics. Volume 84:Number 12(2021)
• Publication Date: 2021-12-07
• Subjects: anomaly detection -- machine learning -- unsupervised learning -- weakly supervised learning -- semisupervised learning -- beyond the standard model -- model-agnostic methods; Physics -- Periodicals; 530.05
• DOI: 10.1088/1361-6633/ac36b9
• Languages: English
• ISSNs: 0034-4885

Abstract

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.