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The LHCb ultra-fast simulation option, Lamarr: design and validation

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data sampl...

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Autores principales: Anderlini, Lucio, Barbetti, Matteo, Capelli, Simone, Corti, Gloria, Davis, Adam, Derkach, Denis, Kazeev, Nikita, Maevskiy, Artem, Martinelli, Maurizio, Mokonenko, Sergei, Siddi, Benedetto Gianluca, Xu, Zehua
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:http://cds.cern.ch/record/2875421
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author Anderlini, Lucio
Barbetti, Matteo
Capelli, Simone
Corti, Gloria
Davis, Adam
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Martinelli, Maurizio
Mokonenko, Sergei
Siddi, Benedetto Gianluca
Xu, Zehua
author_facet Anderlini, Lucio
Barbetti, Matteo
Capelli, Simone
Corti, Gloria
Davis, Adam
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Martinelli, Maurizio
Mokonenko, Sergei
Siddi, Benedetto Gianluca
Xu, Zehua
author_sort Anderlini, Lucio
collection CERN
description Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28754212023-10-24T02:43:53Zhttp://cds.cern.ch/record/2875421engAnderlini, LucioBarbetti, MatteoCapelli, SimoneCorti, GloriaDavis, AdamDerkach, DenisKazeev, NikitaMaevskiy, ArtemMartinelli, MaurizioMokonenko, SergeiSiddi, Benedetto GianlucaXu, ZehuaThe LHCb ultra-fast simulation option, Lamarr: design and validationphysics.ins-detDetectors and Experimental Techniquescs.LGComputing and Computershep-exParticle Physics - ExperimentDetailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase.arXiv:2309.13213oai:cds.cern.ch:28754212023-09-22
spellingShingle physics.ins-det
Detectors and Experimental Techniques
cs.LG
Computing and Computers
hep-ex
Particle Physics - Experiment
Anderlini, Lucio
Barbetti, Matteo
Capelli, Simone
Corti, Gloria
Davis, Adam
Derkach, Denis
Kazeev, Nikita
Maevskiy, Artem
Martinelli, Maurizio
Mokonenko, Sergei
Siddi, Benedetto Gianluca
Xu, Zehua
The LHCb ultra-fast simulation option, Lamarr: design and validation
title The LHCb ultra-fast simulation option, Lamarr: design and validation
title_full The LHCb ultra-fast simulation option, Lamarr: design and validation
title_fullStr The LHCb ultra-fast simulation option, Lamarr: design and validation
title_full_unstemmed The LHCb ultra-fast simulation option, Lamarr: design and validation
title_short The LHCb ultra-fast simulation option, Lamarr: design and validation
title_sort lhcb ultra-fast simulation option, lamarr: design and validation
topic physics.ins-det
Detectors and Experimental Techniques
cs.LG
Computing and Computers
hep-ex
Particle Physics - Experiment
url http://cds.cern.ch/record/2875421
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