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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Accuracy versus precision in boosted top tagging with the ATLAS detector
AU - The ATLAS collaboration
AU - Alsolami, Z.M.K.
AU - Barton, A.E.
AU - Borissov, G.
AU - Bouhova-Thacker, E.V.
AU - Ferguson, Ruby
AU - Ferrando, James
AU - Fox, H.
AU - Hagan, Alina
AU - Henderson, R.C.W.
AU - Jones, R.W.L.
AU - Kartvelishvili, V.
AU - Love, P.A.
AU - Marshall, E.J.
AU - Meng, L.
AU - Muenstermann, D.
AU - Ribaric, N.
AU - Sampson, Elliot
AU - Smizanska, M.
AU - Wharton, A.M.
PY - 2024/8/27
Y1 - 2024/8/27
N2 - 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 √ 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.
AB - 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 √ 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.
KW - Performance of High Energy Physics Detectors
KW - Analysis and statistical methods
U2 - 10.1088/1748-0221/19/08/p08018
DO - 10.1088/1748-0221/19/08/p08018
M3 - Journal article
VL - 19
JO - Journal of Instrumentation
JF - Journal of Instrumentation
SN - 1748-0221
IS - 08
M1 - P08018
ER -