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Deep Learning Approaches for LHCb ECAL Reconstruction

Deep Learning Approaches for LHCb ECAL Reconstruction

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e56c2c29e4f643f9a54a838bc86be624

Deep Learning Approaches for LHCb ECAL Reconstruction

About this item

Full title

Deep Learning Approaches for LHCb ECAL Reconstruction

Publisher

Les Ulis: EDP Sciences

Journal title

EPJ Web of conferences, 2024, Vol.295, p.9008

Language

English

Formats

Publication information

Publisher

Les Ulis: EDP Sciences

More information

Scope and Contents

Contents

Calorimeters are a crucial component for most detectors mounted on modern colliders. Their tasks include identifying and measuring the energy of photons and neutral hadrons, recording energetic hadronic jets, and contributing to the identification of electrons, muons, and charged hadrons. To fulfill these many tasks while keeping costs reasonable,...

Alternative Titles

Full title

Deep Learning Approaches for LHCb ECAL Reconstruction

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_e56c2c29e4f643f9a54a838bc86be624

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_e56c2c29e4f643f9a54a838bc86be624

Other Identifiers

ISSN

2100-014X,2101-6275

E-ISSN

2100-014X

DOI

10.1051/epjconf/202429509008

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