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). Furthermore, 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.
Kasieczka, Gregor, et al. "The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics." Reports on Progress in Physics, vol. 84, no. 12, Dec. 2021. https://doi.org/10.1088/1361-6633/ac36b9
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., ... Yunus, Mikaeel (2021). The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics. Reports on Progress in Physics, 84(12). https://doi.org/10.1088/1361-6633/ac36b9
Kasieczka, Gregor, Nachman, Benjamin, Shih, David, et al., "The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics," Reports on Progress in Physics 84, no. 12 (2021), https://doi.org/10.1088/1361-6633/ac36b9
@article{osti_1863790,
author = {Kasieczka, Gregor and Nachman, Benjamin and Shih, David and Amram, Oz and Andreassen, Anders and Benkendorfer, Kees and Bortolato, Blaz and Brooijmans, Gustaaf and Canelli, Florencia and Collins, Jack H. and others},
title = {The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics},
annote = {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). Furthermore, 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.},
doi = {10.1088/1361-6633/ac36b9},
url = {https://www.osti.gov/biblio/1863790},
journal = {Reports on Progress in Physics},
issn = {ISSN 0034-4885},
number = {12},
volume = {84},
place = {United States},
publisher = {IOP Publishing},
year = {2021},
month = {12}}
Proceedings of 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research — PoS(ACAT2010)https://doi.org/10.22323/1.093.0057