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Machine learning in CMS

Advanced machine learning (ML) methods are increasingly used in CMS physics analyses to maximize the sensitivity of a wide range of measurements. The landscape is diverse in terms of both methods and applications. Deep learning methods, from recurrent long short-term memory (LSTM) architectures for...

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Detalles Bibliográficos
Autor principal: May, Samuel
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:https://dx.doi.org/10.1142/S0217751X22400206
http://cds.cern.ch/record/2861333
Descripción
Sumario:Advanced machine learning (ML) methods are increasingly used in CMS physics analyses to maximize the sensitivity of a wide range of measurements. The landscape is diverse in terms of both methods and applications. Deep learning methods, from recurrent long short-term memory (LSTM) architectures for classification tasks to deep autoencoders for data quality monitoring, have greatly improved the physics results delivered from the CMS experiment. Algorithms are developed both for collaboration-wide use as well as for individual physics analyses. Results from CMS, such as the measurement of the Higgs boson’s properties in the diphoton decay channel, exploit a variety of ML algorithms to reduce uncertainties on measurements.