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oai:recercat.cat:20.500.14342/60732026-03-20T01:12:25Zcom_2072_482405com_2072_183628col_2072_482415

Machine Learning for Particle Identification in LHCb Bernet Andrés, Sergi Calvo Gomez, Miriam García Piquer, Álvaro Vilasis-Cardona, Xavier Universitat Ramon Llull. La Salle LHCb Particle identification Machine learning Neural networks 004 539 LHCb is one of the four largest high-energy physics experiments at CERN focused in high precision measurements of particle physics. The LHCb detector has undergone a recent upgrade [1] implying changes at subdetectors, data taking conditions and data processing model. Information from subdetectors is processed at 30MHz at a first trigger phase builded entirely with GPUs to reduce this rate down to 1MHz. Afterwards, the same information is processed in a second trigger phase that runs in CPUs, performing a complete reconstruction and identification of particles. This upgrade implies an evolution of the algorithms used at trigger level. In order to keep performance and speed up processing time, some of them have been replaced by machine learning algorithms. To perform particle identification, one of the LHCb approaches uses a neural network using the information from all subdetectors. In this paper we explain the advantages of this method and the capabilities that machine learning brings to LHCb focused info:eu-repo/semantics/publishedVersion 2024-09-25 info:eu-repo/semantics/article 9781643685434 1879-8314 https://hdl.handle.net/20.500.14342/6073 https://doi.org/10.3233/FAIA240417 eng Artificial Intelligence Research and Development - Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence © L'autor/a Attribution-NonCommercial 4.0 International http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess 4 p. IOS Press