TY  - JOUR
AU  - Kasieczka, G. et al
AU  - Sanz, V.
PY  - 2021
DA  - 2021//
TI  - The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics
T2  - Rep. Prog. Phys.
JO  - Reports on Progress in Physics
SP  - 124201
EP  - 64pp
VL  - 84
IS  - 12
PB  - IOP Publishing Ltd
KW  - anomaly detection
KW  - machine learning
KW  - unsupervised learning
KW  - weakly supervised learning
KW  - semisupervised learning
KW  - beyond the standard model
KW  - model-agnostic methods
AB  - 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.
SN  - 0034-4885
UR  - https://arxiv.org/abs/2101.08320
UR  - https://doi.org/10.1088/1361-6633/ac36b9
DO  - 10.1088/1361-6633/ac36b9
LA  - English
N1  - WOS:000727698500001
ID  - Kasieczka+Sanz2021
ER  -