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Generative Adversarial Networks for LHCb Fast Simulation

LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will st...

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Autor principal: Ratnikov, Fedor
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1051/epjconf/202024502026
http://cds.cern.ch/record/2759255
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author Ratnikov, Fedor
author_facet Ratnikov, Fedor
author_sort Ratnikov, Fedor
collection CERN
description LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. Generative models, which are nowadays widely used for computer vision and image processing, are being investigated in LHCb to accelerate generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results and discuss possible implications of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.
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spelling cern-27592552023-10-12T05:37:18Zdoi:10.1051/epjconf/202024502026doi:10.1051/epjconf/202024502026http://cds.cern.ch/record/2759255engRatnikov, FedorGenerative Adversarial Networks for LHCb Fast Simulationhep-exParticle Physics - Experimentcs.LGComputing and Computersphysics.ins-detDetectors and Experimental TechniquesLHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. Generative models, which are nowadays widely used for computer vision and image processing, are being investigated in LHCb to accelerate generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results and discuss possible implications of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.LHCb is one of the major experiments operating at the Large Hadron Collider at CERN. The richness of the physics program and the increasing precision of the measurements in LHCb lead to the need of ever larger simulated samples. This need will increase further when the upgraded LHCb detector will start collecting data in the LHC Run 3. Given the computing resources pledged for the production of Monte Carlo simulated events in the next years, the use of fast simulation techniques will be mandatory to cope with the expected dataset size. In LHCb generative models, which are nowadays widely used for computer vision and image processing are being investigated in order to accelerate the generation of showers in the calorimeter and high-level responses of Cherenkov detector. We demonstrate that this approach provides high-fidelity results along with a significant speed increase and discuss possible implication of these results. We also present an implementation of this algorithm into LHCb simulation software and validation tests.arXiv:2003.09762oai:cds.cern.ch:27592552020
spellingShingle hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
Ratnikov, Fedor
Generative Adversarial Networks for LHCb Fast Simulation
title Generative Adversarial Networks for LHCb Fast Simulation
title_full Generative Adversarial Networks for LHCb Fast Simulation
title_fullStr Generative Adversarial Networks for LHCb Fast Simulation
title_full_unstemmed Generative Adversarial Networks for LHCb Fast Simulation
title_short Generative Adversarial Networks for LHCb Fast Simulation
title_sort generative adversarial networks for lhcb fast simulation
topic hep-ex
Particle Physics - Experiment
cs.LG
Computing and Computers
physics.ins-det
Detectors and Experimental Techniques
url https://dx.doi.org/10.1051/epjconf/202024502026
https://dx.doi.org/10.1051/epjconf/202024502026
http://cds.cern.ch/record/2759255
work_keys_str_mv AT ratnikovfedor generativeadversarialnetworksforlhcbfastsimulation