Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase.

Progress on Machine and Deep Learning applications in CMS Computing

Bonacorsi, Daniele;Giommi, L.;Diotalevi, T.;Contreras, C.;
2018

Abstract

Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase.
2018
Proceedings of Science
022
031
Bonacorsi, Daniele; Kuznetsov, V.; Giommi, L.; Diotalevi, T.; Vlimant, J.R.; Abercrombie, D.; Contreras, C.; Repecka, A.; Matonis, Z.; Kancys, K.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/724422
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