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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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Lenguaje: | eng |
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2021
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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 |