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Columnar data analysis with ATLAS analysis formats

<!--HTML-->Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific inter...

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Detalles Bibliográficos
Autor principal: Hartmann, Nikolai
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2767264
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author Hartmann, Nikolai
author_facet Hartmann, Nikolai
author_sort Hartmann, Nikolai
collection CERN
description <!--HTML-->Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.
id cern-2767264
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27672642022-11-02T22:25:37Zhttp://cds.cern.ch/record/2767264engHartmann, NikolaiColumnar data analysis with ATLAS analysis formats25th International Conference on Computing in High Energy & Nuclear PhysicsConferences<!--HTML-->Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.oai:cds.cern.ch:27672642021
spellingShingle Conferences
Hartmann, Nikolai
Columnar data analysis with ATLAS analysis formats
title Columnar data analysis with ATLAS analysis formats
title_full Columnar data analysis with ATLAS analysis formats
title_fullStr Columnar data analysis with ATLAS analysis formats
title_full_unstemmed Columnar data analysis with ATLAS analysis formats
title_short Columnar data analysis with ATLAS analysis formats
title_sort columnar data analysis with atlas analysis formats
topic Conferences
url http://cds.cern.ch/record/2767264
work_keys_str_mv AT hartmannnikolai columnardataanalysiswithatlasanalysisformats
AT hartmannnikolai 25thinternationalconferenceoncomputinginhighenergynuclearphysics