CERN Accelerating science

Article
Report number	arXiv:2101.08320
Title	The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
Related title	The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Author(s)	Kasieczka, Gregor (Hamburg U.) ; Nachman, Benjamin (LBL, Berkeley ; UC, Berkeley, Miller Inst.) ; Shih, David (Rutgers U., Piscataway) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Benkendorfer, Kees (LBL, Berkeley ; Reed Coll.) ; Bortolato, Blaz (Stefan Inst., Ljubljana) ; Brooijmans, Gustaaf (Nevis Labs, Columbia U.) ; Canelli, Florencia (Zurich U.) ; Collins, Jack H. (SLAC) ; Dai, Biwei (BCCP, Berkeley) ; De Freitas, Felipe F. (Aveiro U.) ; Dillon, Barry M. (Stefan Inst., Ljubljana ; U. Heidelberg, ITP) ; Dinu, Ioan-Mihail (Johns Hopkins U.) ; Dong, Zhongtian (Kansas U.) ; Donini, Julien (Clermont-Ferrand U.) ; Duarte, Javier (UC, San Diego) ; Faroughy, D.A. (Zurich U.) ; Gonski, Julia (Nevis Labs, Columbia U.) ; Harris, Philip (MIT, LNS) ; Kahn, Alan (Nevis Labs, Columbia U.) ; Kamenik, Jernej F. (Stefan Inst., Ljubljana ; Ljubljana U.) ; Khosa, Charanjit K. (Sussex U. ; Genoa U.) ; Komiske, Patrick (MIT, Cambridge, CTP) ; Le Pottier, Luc (LBL, Berkeley ; Michigan U.) ; Martín-Ramiro, Pablo (LBL, Berkeley ; Madrid, IFT) ; Matevc, Andrej (Stefan Inst., Ljubljana ; Ljubljana U.) ; Metodiev, Eric (MIT, Cambridge, CTP) ; Mikuni, Vinicius (Zurich U.) ; Murphy, Christopher W. (Unlisted) ; Ochoa, Inês (LIP, Lisbon) ; Park, Sang Eon (MIT, LNS) ; Pierini, Maurizio (CERN) ; Rankin, Dylan (MIT, LNS) ; Sanz, Veronica (Sussex U. ; Valencia U., IFIC) ; Sarda, Nilai (MIT, Cambridge, CTP) ; Seljak, Urŏ (LBNL, Berkeley ; UC, Berkeley ; LBL, Berkeley) ; Seljak, Uros (LBL, Berkeley ; UC, Berkeley, Miller Inst. ; BCCP, Berkeley) ; Smolkovic, Aleks (Stefan Inst., Ljubljana) ; Stein, George (LBL, Berkeley ; BCCP, Berkeley) ; Suarez, Cristina Mantilla (Johns Hopkins U.) ; Szewc, Manuel (ITeDA, Buenos Aires) ; Thaler, Jesse (MIT, Cambridge, CTP) ; Tsan, Steven (UC, San Diego) ; Udrescu, Silviu-Marian (MIT, LNS) ; Vaslin, Louis (Clermont-Ferrand U.) ; Vlimant, Jean-Roch (Caltech) ; Williams, Daniel (Nevis Labs, Columbia U.) ; Yunus, Mikaeel (MIT, LNS)
Publication	2021-12-07
Imprint	2021-01-20
Number of pages	108
Note	108 pages, 53 figures, 3 tables
In:	Rep. Prog. Phys. 84 (2021) 124201
DOI	10.1088/1361-6633/ac36b9
Subject category	physics.data-an ; Other Fields of Physics ; hep-ex ; Particle Physics - Experiment ; hep-ph ; Particle Physics - Phenomenology
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.
Copyright/License	publication: © 2021-2025 IOP Publishing Ltd preprint: (License: CC-BY-4.0)

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