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Simulating the LHCb hadron calorimeter with generative adversarial networks


Lancierini, D; Owen, P; Serra, Nicola (2019). Simulating the LHCb hadron calorimeter with generative adversarial networks. Il nuovo cimento 42 C, University of Zurich.

Abstract

Generative adversarial networks are known as a tool for fast simulation of data. Our aim is to research and develop a physical application of these tools by simulating LHCb hadron calorimeter (HCAL) in order to speed up the Monte Carlo datasets production.

Abstract

Generative adversarial networks are known as a tool for fast simulation of data. Our aim is to research and develop a physical application of these tools by simulating LHCb hadron calorimeter (HCAL) in order to speed up the Monte Carlo datasets production.

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Additional indexing

Item Type:Working Paper
Communities & Collections:07 Faculty of Science > Physics Institute
Dewey Decimal Classification:530 Physics
Language:English
Date:31 January 2019
Deposited On:06 Jan 2020 15:21
Last Modified:07 Jan 2020 04:16
Series Name:Il nuovo cimento
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:https://www.sif.it/riviste/sif/ncc/econtents/2019/042/04/article/52

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