Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data
Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data
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Full title
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Publisher
Les Ulis: EDP Sciences
Journal title
Language
English
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Publisher
Les Ulis: EDP Sciences
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Scope and Contents
Contents
Machine learning algorithms are gaining ground in high energy physics for applications in particle and event identification, physics analysis, detector reconstruction, simulation and trigger. Currently, most data-analysis tasks at LHC experiments benefit from the use of machine learning. Incorporating these computational tools in the experimental f...
Alternative Titles
Full title
Accelerating End-to-End Deep Learning for Particle Reconstruction using CMS open data
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_45d5fcb893f94bdaa449c946063df085
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_45d5fcb893f94bdaa449c946063df085
Other Identifiers
ISSN
2100-014X,2101-6275
E-ISSN
2100-014X
DOI
10.1051/epjconf/202125103057
