The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

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 d...

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Veröffentlicht in:	Reports on progress in physics 2021-12, Vol.84 (12), p.124201
Hauptverfasser:	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
Format:	Artikel
Sprache:	eng
Schlagworte:	anomaly detection beyond the standard model Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology Humans Machine Learning model-agnostic methods Physical Phenomena Physics PHYSICS OF ELEMENTARY PARTICLES AND FIELDS semisupervised learning Supervised Machine Learning unsupervised learning weakly supervised learning
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