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; Kuznetsov, V.; Giommi, L.; Diotalevi, T.; Vlimant, J.R.; Abercrombie, D.; Contreras, C.; Repecka, A.; Matonis, Z.; Kancys, K.. - In: POS PROCEEDINGS OF SCIENCE. - ISSN 1824-8039. - ELETTRONICO. - (2018), pp. 022-031. (Intervento presentato al convegno International Symposium on Grids and Clouds tenutosi a Taipei nel 16-23 Marzo 2018) [10.22323/1.327.0022].
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.