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NanoAODs

The scientist on LHC experiment analyse a huge amount of data every day on the Grid. Thus new methods are requested, how to make the analysis more efficient. The NanoAOD is a derived dataset from AOD, where only information necessary for the analysis is stored. Thus the analysis can be more than two...

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Autor principal: Husova, Lucia Anna
Lenguaje:eng
Publicado: 2017
Materias:
Acceso en línea:http://cds.cern.ch/record/2282534
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author Husova, Lucia Anna
author_facet Husova, Lucia Anna
author_sort Husova, Lucia Anna
collection CERN
description The scientist on LHC experiment analyse a huge amount of data every day on the Grid. Thus new methods are requested, how to make the analysis more efficient. The NanoAOD is a derived dataset from AOD, where only information necessary for the analysis is stored. Thus the analysis can be more than two times faster, because of the smaller file size, which can be read faster on the Grid. The main goal of this summer student project was to help other users to start using NanoAODs by rewriting their user tasks. Two example users tasks were converted to NanoAODs and tested with the local train test. A speed up of 3.5 was reached. The results of the analysis tasks are identical independent if they use AODs or NanoAODs.
id cern-2282534
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
record_format invenio
spelling cern-22825342019-09-30T06:29:59Zhttp://cds.cern.ch/record/2282534engHusova, Lucia AnnaNanoAODsParticle Physics - ExperimentComputing and ComputersThe scientist on LHC experiment analyse a huge amount of data every day on the Grid. Thus new methods are requested, how to make the analysis more efficient. The NanoAOD is a derived dataset from AOD, where only information necessary for the analysis is stored. Thus the analysis can be more than two times faster, because of the smaller file size, which can be read faster on the Grid. The main goal of this summer student project was to help other users to start using NanoAODs by rewriting their user tasks. Two example users tasks were converted to NanoAODs and tested with the local train test. A speed up of 3.5 was reached. The results of the analysis tasks are identical independent if they use AODs or NanoAODs.CERN-STUDENTS-Note-2017-174oai:cds.cern.ch:22825342017-09-06
spellingShingle Particle Physics - Experiment
Computing and Computers
Husova, Lucia Anna
NanoAODs
title NanoAODs
title_full NanoAODs
title_fullStr NanoAODs
title_full_unstemmed NanoAODs
title_short NanoAODs
title_sort nanoaods
topic Particle Physics - Experiment
Computing and Computers
url http://cds.cern.ch/record/2282534
work_keys_str_mv AT husovaluciaanna nanoaods