Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
High Energy Physics - Experiment
2020-10-06 v2 Machine Learning
High Energy Physics - Phenomenology
Abstract
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. Training the ALAD algorithm on 4.4 fb-1 of 8 TeV CMS Open Data, we show how a data-driven anomaly detection and characterization would work in real life, re-discovering the top quark by identifying the main features of the t-tbar experimental signature at the LHC.
Cite
@article{arxiv.2005.01598,
title = {Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark},
author = {Oliver Knapp and Guenther Dissertori and Olmo Cerri and Thong Q. Nguyen and Jean-Roch Vlimant and Maurizio Pierini},
journal= {arXiv preprint arXiv:2005.01598},
year = {2020}
}
Comments
16 pages, 9 figures