Deep-learned Top Tagging with a Lorentz Layer
Anja Butter, Gregor Kasieczka, Tilman Plehn, Michael Russell
SciPost Phys. 5, 028 (2018) · published 26 September 2018
- doi: 10.21468/SciPostPhys.5.3.028
- Submissions/Reports
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.
TY - JOUR
PB - SciPost Foundation
DO - 10.21468/SciPostPhys.5.3.028
TI - Deep-learned Top Tagging with a Lorentz Layer
PY - 2018/09/26
UR - https://www.scipost.org/SciPostPhys.5.3.028
JF - SciPost Physics
JA - SciPost Phys.
VL - 5
IS - 3
SP - 028
A1 - Butter, Anja
AU - Kasieczka, Gregor
AU - Plehn, Tilman
AU - Russell, Michael
AB - 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.
ER -
@Article{10.21468/SciPostPhys.5.3.028,
title={{Deep-learned Top Tagging with a Lorentz Layer}},
author={Anja Butter and Gregor Kasieczka and Tilman Plehn and Michael Russell},
journal={SciPost Phys.},
volume={5},
pages={028},
year={2018},
publisher={SciPost},
doi={10.21468/SciPostPhys.5.3.028},
url={https://scipost.org/10.21468/SciPostPhys.5.3.028},
}
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Authors / Affiliations: mappings to Contributors and Organizations
See all Organizations.- 1 Anja Butter,
- 2 3 Gregor Kasieczka,
- 1 Tilman Plehn,
- 1 4 Michael Russell
- 1 Ruprecht-Karls-Universität Heidelberg / Heidelberg University
- 2 Eidgenössische Technische Hochschule Zürich / Swiss Federal Institute of Technology in Zurich (ETH) [ETH Zurich]
- 3 Centro Svizzero di Calcolo Scientifico / Swiss National Supercomputing Centre [CSCS]
- 4 University of Glasgow
Funders for the research work leading to this publication