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Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network

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Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network. / The ATLAS collaboration.
In: Machine Learning: Science and Technology, Vol. 5, No. 3, 035051, 03.09.2024.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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The ATLAS collaboration. Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network. Machine Learning: Science and Technology. 2024 Sept 3;5(3):035051. doi: 10.1088/2632-2153/ad611e

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The ATLAS collaboration. / Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network. In: Machine Learning: Science and Technology. 2024 ; Vol. 5, No. 3.

Bibtex

@article{7f6d50242d754bdf8cb9bcf7fd38cd0e,
title = "Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network",
abstract = "The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT>500 GeV.",
keywords = "detector, calibrations, CERN jets, ATLAS",
author = "{The ATLAS collaboration} and A.E. Barton and G. Borissov and E.V. Bouhova-Thacker and Ruby Ferguson and James Ferrando and H. Fox and Alina Hagan and R.C.W. Henderson and R.W.L. Jones and V. Kartvelishvili and P.A. Love and E.J. Marshall and L. Meng and D. Muenstermann and N. Ribaric and K. Rybacki and M. Smizanska and S. Spinali and A.M. Wharton",
year = "2024",
month = sep,
day = "3",
doi = "10.1088/2632-2153/ad611e",
language = "English",
volume = "5",
journal = "Machine Learning: Science and Technology",
issn = "2632-2153",
publisher = "IOP Publishing",
number = "3",

}

RIS

TY - JOUR

T1 - Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network

AU - The ATLAS collaboration

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 - Rybacki, K.

AU - Smizanska, M.

AU - Spinali, S.

AU - Wharton, A.M.

PY - 2024/9/3

Y1 - 2024/9/3

N2 - The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT>500 GeV.

AB - The energy and mass measurements of jets are crucial tasks for the Large Hadron Collider experiments. This paper presents a new calibration method to simultaneously calibrate these quantities for large-radius jets measured with the ATLAS detector using a deep neural network (DNN). To address the specificities of the calibration problem, special loss functions and training procedures are employed, and a complex network architecture, which includes feature annotation and residual connection layers, is used. The DNN-based calibration is compared to the standard numerical approach in an extensive series of tests. The DNN approach is found to perform significantly better in almost all of the tests and over most of the relevant kinematic phase space. In particular, it consistently improves the energy and mass resolutions, with a 30% better energy resolution obtained for transverse momenta pT>500 GeV.

KW - detector

KW - calibrations

KW - CERN jets

KW - ATLAS

U2 - 10.1088/2632-2153/ad611e

DO - 10.1088/2632-2153/ad611e

M3 - Journal article

VL - 5

JO - Machine Learning: Science and Technology

JF - Machine Learning: Science and Technology

SN - 2632-2153

IS - 3

M1 - 035051

ER -