Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network

<!--HTML-->Based on the fact that showers in calorimeters depend on the type of particle, this note attempts to perform a particle classifier for electromagnetic and hadronic particles on an electromagnetic calorimeter, based on the energy deposit of individual cells. Using data from a Geant4...

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Autor principal: Rua Herrera, Alex
Lenguaje:eng
Publicado: 2021
Materias:
Conferences
Acceso en línea:http://cds.cern.ch/record/2766982
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author Rua Herrera, Alex
author_facet Rua Herrera, Alex
author_sort Rua Herrera, Alex
collection CERN
description <!--HTML-->Based on the fact that showers in calorimeters depend on the type of particle, this note attempts to perform a particle classifier for electromagnetic and hadronic particles on an electromagnetic calorimeter, based on the energy deposit of individual cells. Using data from a Geant4 simulation of a proposal of a Crystal Fiber Calorimeter (SPACAL), foreseen for a future upgrade of the LHCb detector, a classifier is built using Convolutional Neural Networks. Results obtained demonstrate that the higher resolution of this ECAL allows to attain over 95% precision in some classifications such as photons against neutrons.
id cern-2766982
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
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spelling cern-27669822022-11-02T22:25:52Zhttp://cds.cern.ch/record/2766982engRua Herrera, AlexParticle identification with an electromagnetic calorimeter using a Convolutional Neural Network25th International Conference on Computing in High Energy & Nuclear PhysicsConferences<!--HTML-->Based on the fact that showers in calorimeters depend on the type of particle, this note attempts to perform a particle classifier for electromagnetic and hadronic particles on an electromagnetic calorimeter, based on the energy deposit of individual cells. Using data from a Geant4 simulation of a proposal of a Crystal Fiber Calorimeter (SPACAL), foreseen for a future upgrade of the LHCb detector, a classifier is built using Convolutional Neural Networks. Results obtained demonstrate that the higher resolution of this ECAL allows to attain over 95% precision in some classifications such as photons against neutrons.oai:cds.cern.ch:27669822021
spellingShingle Conferences
Rua Herrera, Alex
Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title_full Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title_fullStr Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title_full_unstemmed Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title_short Particle identification with an electromagnetic calorimeter using a Convolutional Neural Network
title_sort particle identification with an electromagnetic calorimeter using a convolutional neural network
topic Conferences
url http://cds.cern.ch/record/2766982
work_keys_str_mv AT ruaherreraalex particleidentificationwithanelectromagneticcalorimeterusingaconvolutionalneuralnetwork
AT ruaherreraalex 25thinternationalconferenceoncomputinginhighenergynuclearphysics

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