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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.

Authors:
 [1];  [2];  [2];  [3];  [4];  [5];  [6];  [5];  [7];  [7];  [8];  [9];  [10];  [1];  [7];  [11];  [10];  [12];  [13];  [14] more »;  [7];  [8];  [15];  [15];  [4];  [2];  [6] « less
+ Show Author Affiliations
  1. University of Hamburg
  2. Heidelberg University
  3. New York University
  4. Rutgers, The State University of New Jersey
  5. Jožef Stefan Institute
  6. King's College London
  7. University of British Columbia
  8. University of California, Santa Barbara
  9. Jožef Stefan Institute, University of Ljubljana
  10. Massachusetts Institute of Technology
  11. New York University, Rutgers, The State University of New Jersey
  12. Université catholique de Louvain
  13. Lawrence Berkeley National Laboratory, University of California, Berkeley
  14. Laboratory of Theoretical and High Energy Physics, National Institute for Subatomic Physics
  15. RWTH Aachen University
Publication Date:
Research Org.:
Univ. of California, Santa Barbara, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); National Science Foundation (NSF)
OSTI Identifier:
1568892
Alternate Identifier(s):
OSTI ID: 1777252
Grant/Contract Number:  
AC02-05CH11231; SC-0011090; SC0012567; SC0011702; SC0011090; OAC-1836650; ACI-1450310
Resource Type:
Published Article
Journal Name:
SciPost Physics
Additional Journal Information:
Journal Name: SciPost Physics Journal Volume: 7 Journal Issue: 1; Journal ID: ISSN 2542-4653
Publisher:
Stichting SciPost
Country of Publication:
Netherlands
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, 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, and Varma, Sreedevi. The Machine Learning landscape of top taggers. Netherlands: N. p., 2019. Web. doi:10.21468/SciPostPhys.7.1.014.
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Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, 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. The Machine Learning landscape of top taggers. Netherlands. https://doi.org/10.21468/SciPostPhys.7.1.014
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Kasieczka, Gregor, Plehn, Tilman, Butter, Anja, Cranmer, Kyle, Debnath, Dipsikha, Dillon, Barry M., Fairbairn, Malcolm, Faroughy, Darius A., Fedorko, Wojtek, Gay, Christophe, Gouskos, Loukas, Kamenik, Jernej Fesel, 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, and Varma, Sreedevi. Tue . "The Machine Learning landscape of top taggers". Netherlands. https://doi.org/10.21468/SciPostPhys.7.1.014.
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@article{osti_1568892,
title = {The Machine Learning landscape of top taggers},
author = {Kasieczka, Gregor and Plehn, Tilman and Butter, Anja and Cranmer, Kyle and Debnath, Dipsikha and Dillon, Barry M. and Fairbairn, Malcolm and Faroughy, Darius A. and Fedorko, Wojtek and Gay, Christophe and Gouskos, Loukas and Kamenik, Jernej Fesel and Komiske, Patrick and Leiss, Simon and Lister, Alison and Macaluso, Sebastian and Metodiev, Eric and Moore, Liam and Nachman, Benjamin and Nordström, Karl and Pearkes, Jannicke and Qu, Huilin and Rath, Yannik and Rieger, Marcel and Shih, David and Thompson, Jennifer and Varma, Sreedevi},
abstractNote = {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.},
doi = {10.21468/SciPostPhys.7.1.014},
journal = {SciPost Physics},
number = 1,
volume = 7,
place = {Netherlands},
year = {Tue Jul 30 00:00:00 EDT 2019},
month = {Tue Jul 30 00:00:00 EDT 2019}
}
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Journal Article:
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https://doi.org/10.21468/SciPostPhys.7.1.014
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