Deep-learned Top Tagging with a Lorentz Layer
High Energy Physics - Phenomenology
2018-09-26 v3 High Energy Physics - Experiment
Abstract
We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.
Cite
@article{arxiv.1707.08966,
title = {Deep-learned Top Tagging with a Lorentz Layer},
author = {Anja Butter and Gregor Kasieczka and Tilman Plehn and Michael Russell},
journal= {arXiv preprint arXiv:1707.08966},
year = {2018}
}
Comments
v3: minor revisions following SciPost referee reports