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

00925njm 22002777a 4500 dc Bernet Andrés, Sergi author Calvo Gomez, Miriam author García Piquer, Álvaro author Vilasis-Cardona, Xavier author 2024-09-25 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 In order to keep performance and speed up processing time, some of them have been replaced by machine learning algorithms.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 9781643685434 1879-8314 https://hdl.handle.net/20.500.14342/6073 https://doi.org/10.3233/FAIA240417 LHCb Particle identification Machine learning Neural networks Machine Learning for Particle Identification in LHCb