Towards Reliable Neural Generative Modeling of Detectors

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The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to us...

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Autores principales: Anderlini, Lucio, Barbetti, Matteo, Derkach, Denis, Kazeev, Nikita, Maevskiy, Artem, Mokhnenko, Sergei
Lenguaje:eng
Publicado: 2023
Materias:
Particle Physics - Experiment
Computing and Computers
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/2438/1/012130
http://cds.cern.ch/record/2808834
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author Anderlini, Lucio
Barbetti, Matteo
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Mokhnenko, Sergei
author_facet Anderlini, Lucio
Barbetti, Matteo
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Mokhnenko, Sergei
author_sort Anderlini, Lucio
collection CERN
description The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.
id cern-2808834
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28088342023-08-23T14:34:14Zdoi:10.1088/1742-6596/2438/1/012130http://cds.cern.ch/record/2808834engAnderlini, LucioBarbetti, MatteoDerkach, DenisKazeev, NikitaMaevskiy, ArtemMokhnenko, SergeiTowards Reliable Neural Generative Modeling of DetectorsParticle Physics - ExperimentComputing and ComputersDetectors and Experimental TechniquesThe increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.arXiv:2204.09947oai:cds.cern.ch:28088342023
spellingShingle Particle Physics - Experiment
Computing and Computers
Detectors and Experimental Techniques
Anderlini, Lucio
Barbetti, Matteo
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Mokhnenko, Sergei
Towards Reliable Neural Generative Modeling of Detectors
title Towards Reliable Neural Generative Modeling of Detectors
title_full Towards Reliable Neural Generative Modeling of Detectors
title_fullStr Towards Reliable Neural Generative Modeling of Detectors
title_full_unstemmed Towards Reliable Neural Generative Modeling of Detectors
title_short Towards Reliable Neural Generative Modeling of Detectors
title_sort towards reliable neural generative modeling of detectors
topic Particle Physics - Experiment
Computing and Computers
Detectors and Experimental Techniques
url https://dx.doi.org/10.1088/1742-6596/2438/1/012130
http://cds.cern.ch/record/2808834
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AT derkachdenis towardsreliableneuralgenerativemodelingofdetectors
AT kazeevnikita towardsreliableneuralgenerativemodelingofdetectors
AT maevskiyartem towardsreliableneuralgenerativemodelingofdetectors
AT mokhnenkosergei towardsreliableneuralgenerativemodelingofdetectors

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