Simulating the LHCb hadron calorimeter with generative adversarial networks

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.

Detalles Bibliográficos
Autores principales: Lancierini, D, Owen, P, Serra, N
Lenguaje:eng
Publicado: 2019
Materias:
Particle Physics - Experiment
Computing and Computers
Acceso en línea:https://dx.doi.org/10.1393/ncc/i2019-19197-3
http://cds.cern.ch/record/2834891
Descripción
Sumario: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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