TY  - JOUR
AU  - Aarrestad, T. et al
AU  - Mamuzic, J.
AU  - Ruiz de Austri, R.
PY  - 2022
DA  - 2022//
TI  - Benchmark data and model independent event classification for the large hadron collider
T2  - SciPost Phys.
JO  - Scipost Physics
SP  - 043
EP  - 57pp
VL  - 12
IS  - 1
PB  - Scipost Foundation
AB  - We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.
SN  - 2542-4653
UR  - https://arxiv.org/abs/2105.14027
UR  - https://doi.org/10.21468/SciPostPhys.12.1.043
DO  - 10.21468/SciPostPhys.12.1.043
LA  - English
N1  - WOS:000807448000038
ID  - Aarrestad_etal2022
ER  -