Boosting mono-jet searches with model-agnostic machine learning
High Energy Physics - Phenomenology
2022-08-24 v2
Abstract
We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably.
Cite
@article{arxiv.2204.11889,
title = {Boosting mono-jet searches with model-agnostic machine learning},
author = {Thorben Finke and Michael Krämer and Maximilian Lipp and Alexander Mück},
journal= {arXiv preprint arXiv:2204.11889},
year = {2022}
}
Comments
19 pages, 3 figures. v2: references added