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Columnar data analysis with ATLAS analysis formats
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....
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
2021
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Acceso en línea: | http://cds.cern.ch/record/2765404 |
_version_ | 1780971146997399552 |
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author | Hartmann, Nikolai Duckeck, Guenter Elmsheuser, Johannes |
author_facet | Hartmann, Nikolai Duckeck, Guenter Elmsheuser, Johannes |
author_sort | Hartmann, Nikolai |
collection | CERN |
description | 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-2765404 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
record_format | invenio |
spelling | cern-27654042022-08-23T08:21:57Zhttp://cds.cern.ch/record/2765404engHartmann, NikolaiDuckeck, GuenterElmsheuser, JohannesColumnar data analysis with ATLAS analysis formatsParticle Physics - ExperimentFuture 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.ATL-SOFT-SLIDE-2021-122oai:cds.cern.ch:27654042021-04-28 |
spellingShingle | Particle Physics - Experiment Hartmann, Nikolai Duckeck, Guenter Elmsheuser, Johannes 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 | Particle Physics - Experiment |
url | http://cds.cern.ch/record/2765404 |
work_keys_str_mv | AT hartmannnikolai columnardataanalysiswithatlasanalysisformats AT duckeckguenter columnardataanalysiswithatlasanalysisformats AT elmsheuserjohannes columnardataanalysiswithatlasanalysisformats |