@article{9232ecb036374cc796db8ec8c049001b,
title = "The Machine Learning landscape of top taggers",
abstract = "Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.",
author = "Gregor Kasieczka and Tilman Plehn and Anja Butter and Kyle Cranmer and Dipsikha Debnath and Dillon, {Barry M.} and Malcolm Fairbairn and Faroughy, {Darius A.} and Wojtek Fedorko and Christophe Gay and Loukas Gouskos and Kamenik, {Jernej F.} and Komiske, {Patrick T.} and Simon Leiss and Alison Lister and Sebastian Macaluso and Metodiev, {Eric M.} and Liam Moore and Ben Nachman and Karl Nordstr{\"o}m and Jannicke Pearkes and Huilin Qu and Yannik Rath and Marcel Rieger and David Shih and Thompson, {Jennifer M.} and Sreedevi Varma",
note = "Publisher Copyright: {\textcopyright} 2019 SciPost Physics. All rights reserved.",
year = "2019",
month = jul,
doi = "10.21468/SciPostPhys.7.1.014",
language = "English (US)",
volume = "7",
journal = "SciPost Physics",
issn = "2542-4653",
publisher = "SciPost Foundation",
number = "1",
}