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

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doi:10.1088/2632-2153/ad611e

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 pᴛ > 500 GeV.

Simultaneous energy and mass calibration of large-radius jets with the ATLAS detector using a deep neural network / G. Aad, E.A.. - In: MACHINE LEARNING: SCIENCE AND TECHNOLOGY. - ISSN 2632-2153. - 5:3(2024 Sep 03), pp. 035051.1-035051.38. [10.1088/2632-2153/ad611e]

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

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A;Zoch, K;Zorbas, T G;Zormpa, O;Zou, W;Zwalinski, L

2024

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 pᴛ > 500 GeV.

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	Settori scientifico-disciplinari dell'articolo (sola visualizzazione)
	
				Settore FIS/01 - Fisica Sperimentale
Settore ING-INF/07 - Misure Elettriche e Elettroniche
			
	Data di pubblicazione
	
				3-set-2024
			
	Rivista in ANCE
	
				MACHINE LEARNING: SCIENCE AND TECHNOLOGY
			
	DOI
	
				https://dx.doi.org/10.1088/2632-2153/ad611e
			
	URL
	
				https://iopscience.iop.org/article/10.1088/2632-2153/ad611e
			
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				01 - Articolo su periodico

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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1093891

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