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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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Lenguaje: | eng |
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2020
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Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/1525/1/012114 http://cds.cern.ch/record/2715406 |
_version_ | 1780965432095670272 |
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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. |
id | cern-2715406 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2020 |
record_format | invenio |
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 |