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A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton
<!--HTML-->The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose...
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Lenguaje: | eng |
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2021
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Acceso en línea: | http://cds.cern.ch/record/2766981 |
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author | Valls Canudas, Nuria |
author_facet | Valls Canudas, Nuria |
author_sort | Valls Canudas, Nuria |
collection | CERN |
description | <!--HTML-->The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of deep learning techniques and the understanding of the current algorithm, our proposal decomposes the reconstruction process into small parts that benefit the generalized learning of small neural network architectures and simplifies the training dataset. This approach takes as input the full simulation data of the calorimeter and outputs a list of reconstructed clusters in a nearly constant time without any dependency in the event complexity. |
id | cern-2766981 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2021 |
record_format | invenio |
spelling | cern-27669812022-11-02T22:25:52Zhttp://cds.cern.ch/record/2766981engValls Canudas, NuriaA Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton25th International Conference on Computing in High Energy & Nuclear PhysicsConferences<!--HTML-->The optimization of reconstruction algorithms has become a key aspect in LHCb as it is currently undergoing a major upgrade that will considerably increase the data processing rate. Aiming to accelerate the second most time consuming reconstruction process of the trigger, we propose an alternative reconstruction algorithm for the Electromagnetic Calorimeter of LHCb. Together with the use of deep learning techniques and the understanding of the current algorithm, our proposal decomposes the reconstruction process into small parts that benefit the generalized learning of small neural network architectures and simplifies the training dataset. This approach takes as input the full simulation data of the calorimeter and outputs a list of reconstructed clusters in a nearly constant time without any dependency in the event complexity.oai:cds.cern.ch:27669812021 |
spellingShingle | Conferences Valls Canudas, Nuria A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title | A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title_full | A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title_fullStr | A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title_full_unstemmed | A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title_short | A Deep Learning approach to LHCb Calorimeter reconstruction using a Cellular Automaton |
title_sort | deep learning approach to lhcb calorimeter reconstruction using a cellular automaton |
topic | Conferences |
url | http://cds.cern.ch/record/2766981 |
work_keys_str_mv | AT vallscanudasnuria adeeplearningapproachtolhcbcalorimeterreconstructionusingacellularautomaton AT vallscanudasnuria 25thinternationalconferenceoncomputinginhighenergynuclearphysics AT vallscanudasnuria deeplearningapproachtolhcbcalorimeterreconstructionusingacellularautomaton |