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Author  |
ATLAS Collaboration (Aad, G. et al); Aikot, A.; Amos, K.R.; Bouchhar, N.; Cabrera Urban, S.; Cantero, J.; Castillo Gimenez, V.; Chitishvili, M.; Costa, M.J.; Curcio, F.; Didenko, M.; Escobar, C.; Fiorini, L.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gomez Delegido, A.J.; Gonzalez de la Hoz, S.; Guerrero Rojas, J.G.R.; Lacasta, C.; Marti-Garcia, S.; Martinez Agullo, P.; Melini, D.; Miralles Lopez, M.; Mitsou, V.A.; Monsonis Romero, L.; Moreno Llacer, M.; Munoz Perez, D.; Navarro-Gonzalez, J.; Poveda, J.; Rubio Jimenez, A.; Ruiz-Martinez, A.; Saibel, A.; Salt, J.; Sanchez Sebastian, V.; Senthilkumar, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valiente Moreno, E.; Valls Ferrer, J.A.; Varriale, L.; Villaplana Perez, M.; Vos, M.; Zakareishvili, T. |
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Title |
Accuracy versus precision in boosted top tagging with the ATLAS detector |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
8 |
Pages |
P08018 - 44pp |
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Keywords |
Analysis and statistical methods; Performance of High Energy Physics Detectors |
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Abstract |
The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at root s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available. |
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Address |
[Filmer, E. K.; Grant, C. M.; Green, M. J.; Jackson, P.; Kong, A. X. Y.; Pandya, H. D.; Ruggeri, T. A.; Saha, S.; Ting, E. X. L.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
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Conference |
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Notes |
WOS:001381766600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6432 |
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