The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
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Published:2021-12-01
Issue:12
Volume:84
Page:124201
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ISSN:0034-4885
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Container-title:Reports on Progress in Physics
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language:
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Short-container-title:Rep. Prog. Phys.
Author:
Kasieczka GregorORCID, Nachman BenjaminORCID, Shih DavidORCID, Amram OzORCID, Andreassen Anders, Benkendorfer Kees, Bortolato Blaz, Brooijmans Gustaaf, Canelli Florencia, Collins Jack H, Dai BiweiORCID, De Freitas Felipe F, Dillon Barry M, Dinu Ioan-MihailORCID, Dong Zhongtian, Donini JulienORCID, Duarte JavierORCID, Faroughy D A, Gonski Julia, Harris PhilipORCID, Kahn Alan, Kamenik Jernej FORCID, Khosa Charanjit KORCID, Komiske Patrick, Le Pottier Luc, Martín-Ramiro PabloORCID, Matevc Andrej, Metodiev Eric, Mikuni Vinicius, Murphy Christopher WORCID, Ochoa InêsORCID, Park Sang EonORCID, Pierini MaurizioORCID, Rankin DylanORCID, Sanz VeronicaORCID, Sarda Nilai, Seljak Urŏ, Smolkovic Aleks, Stein GeorgeORCID, Suarez Cristina MantillaORCID, Szewc Manuel, Thaler JesseORCID, Tsan StevenORCID, Udrescu Silviu-Marian, Vaslin LouisORCID, Vlimant Jean-RochORCID, Williams Daniel, Yunus Mikaeel
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.
Funder
National Science Foundation Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung Deutsche Forschungsgemeinschaft ‘la Caixa’ Foundation U.S. Department of Energy
Subject
General Physics and Astronomy
Cited by
87 articles.
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