1. The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
- Author
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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, Seljak, Uros, 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, Laboratoire de Physique de Clermont (LPC), and Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)
- Subjects
Physics beyond the Standard Model ,beyond the standard model ,General Physics and Astronomy ,01 natural sciences ,Mathematical Sciences ,semisupervised learning ,High Energy Physics - Experiment ,law.invention ,physics.data-an ,Machine Learning ,Physical Phenomena ,High Energy Physics - Experiment (hep-ex) ,benchmark ,High Energy Physics - Phenomenology (hep-ph) ,law ,[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] ,model-agnostic methods ,Physics ,Large Hadron Collider ,new physics ,hep-ph ,anomaly detection ,High Energy Physics - Phenomenology ,CERN LHC Coll ,Physical Sciences ,Unsupervised learning ,Anomaly detection ,Supervised Machine Learning ,Particle Physics - Experiment ,[PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an] ,weakly supervised learning ,Computer Science::Machine Learning ,General Physics ,data analysis method ,Other Fields of Physics ,FOS: Physical sciences ,unsupervised learning ,Benchmark (surveying) ,0103 physical sciences ,Leverage (statistics) ,Humans ,010306 general physics ,Collider ,activity report ,Particle Physics - Phenomenology ,hep-ex ,010308 nuclear & particles physics ,Data science ,Physics - Data Analysis, Statistics and Probability ,[PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph] ,Anomaly (physics) ,Data Analysis, Statistics and Probability (physics.data-an) - 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., 108 pages, 53 figures, 3 tables
- Published
- 2021