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Making RooFit Ready for Run 3

RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing. I...

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Detalles Bibliográficos
Autores principales: Hageboeck, S., Moneta, L.
Lenguaje:eng
Publicado: 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/1525/1/012114
http://cds.cern.ch/record/2715406
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author Hageboeck, S.
Moneta, L.
author_facet Hageboeck, S.
Moneta, L.
author_sort Hageboeck, S.
collection CERN
description RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing. In this work, first results on modernising RooFit’s collections, restructuring data flow and vectorising likelihood fits in RooFit will be discussed. These improvements will enable the LHC experiments to process larger datasets without having to compromise with respect to model complexity, as fitting times would increase significantly with the large datasets to be expected in Run 3.
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling cern-27154062021-02-09T10:07:32Zdoi:10.1088/1742-6596/1525/1/012114http://cds.cern.ch/record/2715406engHageboeck, S.Moneta, L.Making RooFit Ready for Run 3physics.data-anOther Fields of Physicshep-exParticle Physics - Experimentcs.MSComputing and ComputersRooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing. In this work, first results on modernising RooFit’s collections, restructuring data flow and vectorising likelihood fits in RooFit will be discussed. These improvements will enable the LHC experiments to process larger datasets without having to compromise with respect to model complexity, as fitting times would increase significantly with the large datasets to be expected in Run 3.RooFit and RooStats, the toolkits for statistical modelling in ROOT, are used in most searches and measurements at the Large Hadron Collider. The data to be collected in Run 3 will enable measurements with higher precision and models with larger complexity, but also require faster data processing. In this work, first results on modernising RooFit's collections, restructuring data flow and vectorising likelihood fits in RooFit will be discussed. These improvements will enable the LHC experiments to process larger datasets without having to compromise with respect to model complexity, as fitting times would increase significantly with the large datasets to be expected in Run 3.arXiv:2003.12861oai:cds.cern.ch:27154062020-03-28
spellingShingle physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
cs.MS
Computing and Computers
Hageboeck, S.
Moneta, L.
Making RooFit Ready for Run 3
title Making RooFit Ready for Run 3
title_full Making RooFit Ready for Run 3
title_fullStr Making RooFit Ready for Run 3
title_full_unstemmed Making RooFit Ready for Run 3
title_short Making RooFit Ready for Run 3
title_sort making roofit ready for run 3
topic physics.data-an
Other Fields of Physics
hep-ex
Particle Physics - Experiment
cs.MS
Computing and Computers
url https://dx.doi.org/10.1088/1742-6596/1525/1/012114
http://cds.cern.ch/record/2715406
work_keys_str_mv AT hageboecks makingroofitreadyforrun3
AT monetal makingroofitreadyforrun3