The Machine Learning landscape of top taggers

Kasieczka, Gregor; Plehn, Tilman; Butter, Anja; Cranmer, Kyle; Debnath, Dipsikha; Dillon, Barry M.; Fairbairn, Malcolm; Faroughy, Darius A.; Fedorko, Wojtek; Gay, Christophe; Gouskos, Loukas; F. Kamenik, Jernej; Komiske, Patrick; Leiss, Simon; Lister, Alison; Macaluso, Sebastian; Metodiev, Eric; Moore, Liam; Nachman, Benjamin; Nordström, Karl; Pearkes, Jannicke; Qu, Huilin; Rath, Yannik; Rieger, Marcel; Shih, David; Thompson, Jennifer; Varma, Sreedevi

doi:10.21468/SciPostPhys.7.1.014

SciPost Physics

The Machine Learning landscape of top taggers

Gregor Kasieczka, Tilman Plehn, Anja Butter, Kyle Cranmer, Dipsikha Debnath, Barry M. Dillon, Malcolm Fairbairn, Darius A. Faroughy, Wojtek Fedorko, Christophe Gay, Loukas Gouskos, Jernej F. Kamenik, Patrick T. Komiske, Simon Leiss, Alison Lister, Sebastian Macaluso, Eric M. Metodiev, Liam Moore, Ben Nachman, Karl Nordström, Jannicke Pearkes, Huilin Qu, Yannik Rath, Marcel Rieger, David Shih, Jennifer M. Thompson, Sreedevi Varma

SciPost Phys. 7, 014 (2019) · published 30 July 2019

doi: 10.21468/SciPostPhys.7.1.014
pdf
Submissions/Reports

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.

TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.7.1.014
TI  - The Machine Learning landscape of top taggers
PY  - 2019/07/30
UR  - https://scipost.org/SciPostPhys.7.1.014
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 7
IS  - 1
SP  - 014
A1  - Kasieczka, Gregor
AU  - Plehn, Tilman
AU  - Butter, Anja
AU  - Cranmer, Kyle
AU  - Debnath, Dipsikha
AU  - Dillon, Barry M.
AU  - Fairbairn, Malcolm
AU  - Faroughy, Darius A.
AU  - Fedorko, Wojtek
AU  - Gay, Christophe
AU  - Gouskos, Loukas
AU  - F. Kamenik, Jernej
AU  - Komiske, Patrick
AU  - Leiss, Simon
AU  - Lister, Alison
AU  - Macaluso, Sebastian
AU  - Metodiev, Eric
AU  - Moore, Liam
AU  - Nachman, Benjamin
AU  - Nordström, Karl
AU  - Pearkes, Jannicke
AU  - Qu, Huilin
AU  - Rath, Yannik
AU  - Rieger, Marcel
AU  - Shih, David
AU  - Thompson, Jennifer
AU  - Varma, Sreedevi
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.7.1.014,
	title={{The Machine Learning landscape of top taggers}},
	author={Gregor Kasieczka and Tilman Plehn and Anja Butter and Kyle Cranmer and Dipsikha Debnath and Barry M. Dillon and Malcolm Fairbairn and Darius A. Faroughy and Wojtek Fedorko and Christophe Gay and Loukas Gouskos and Jernej F. Kamenik and Patrick T. Komiske and Simon Leiss and Alison Lister and Sebastian Macaluso and Eric M. Metodiev and Liam Moore and Ben Nachman and Karl Nordström and Jannicke Pearkes and Huilin Qu and Yannik Rath and Marcel Rieger and David Shih and Jennifer M. Thompson and Sreedevi Varma},
	journal={SciPost Phys.},
	volume={7},
	pages={014},
	year={2019},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.7.1.014},
	url={https://scipost.org/10.21468/SciPostPhys.7.1.014},
}

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Authors / Affiliations: mappings to Contributors and Organizations

See all Organizations.

¹ Gregor Kasieczka,
² Tilman Plehn,
² Anja Butter,
³ Kyle Cranmer,
⁴ Dipsikha Debnath,
⁵ Barry M. Dillon,
⁶ Malcolm Fairbairn,
⁵ Darius A. Faroughy,
⁷ Wojtek Fedorko,
⁷ Christophe Gay,
⁸ Loukas Gouskos,
⁵ ⁹ Jernej F. Kamenik,
¹⁰ Patrick Komiske,
¹ Simon Leiss,
⁷ Alison Lister,
³ ⁴ Sebastian Macaluso,
¹⁰ Eric Metodiev,
¹¹ Liam Moore,
¹² ¹³ Benjamin Nachman,
¹⁴ ¹⁵ Karl Nordström,
⁷ Jannicke Pearkes,
⁸ Huilin Qu,
¹⁶ Yannik Rath,
¹⁶ Marcel Rieger,
⁴ David Shih,
² Jennifer Thompson,
⁶ Sreedevi Varma

Funders for the research work leading to this publication

National Science Foundation [NSF]
Nvidia (through Organization: Nvidia (United States))
United States Department of Energy [DOE]