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Robustness of the ATLAS pixel clustering neural network algorithm
Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify a...
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Lenguaje: | eng |
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2016
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Acceso en línea: | http://cds.cern.ch/record/2162774 |
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author | Sidebo, Per Edvin |
author_facet | Sidebo, Per Edvin |
author_sort | Sidebo, Per Edvin |
collection | CERN |
description | Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The method recovers otherwise lost tracks in dense environments where particles are separated by distances comparable to the size of the detector read-out elements. Such environments are highly relevant for LHC run 2, e.g. in searches for heavy resonances. Within the scope of run 2 track reconstruction performance and upgrades, the robustness of the neural network algorithm will be presented. The robustness has been studied by evaluating the stability of the algorithm’s performance under a range of variations in the pixel detector conditions. |
id | cern-2162774 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
record_format | invenio |
spelling | cern-21627742019-09-30T06:29:59Zhttp://cds.cern.ch/record/2162774engSidebo, Per EdvinRobustness of the ATLAS pixel clustering neural network algorithmParticle Physics - ExperimentProton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. The algorithms depend heavily on accurate estimation of the position of particles as they traverse the inner detector elements. An artificial neural network algorithm is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The method recovers otherwise lost tracks in dense environments where particles are separated by distances comparable to the size of the detector read-out elements. Such environments are highly relevant for LHC run 2, e.g. in searches for heavy resonances. Within the scope of run 2 track reconstruction performance and upgrades, the robustness of the neural network algorithm will be presented. The robustness has been studied by evaluating the stability of the algorithm’s performance under a range of variations in the pixel detector conditions.ATL-PHYS-SLIDE-2016-356oai:cds.cern.ch:21627742016-06-21 |
spellingShingle | Particle Physics - Experiment Sidebo, Per Edvin Robustness of the ATLAS pixel clustering neural network algorithm |
title | Robustness of the ATLAS pixel clustering neural network algorithm |
title_full | Robustness of the ATLAS pixel clustering neural network algorithm |
title_fullStr | Robustness of the ATLAS pixel clustering neural network algorithm |
title_full_unstemmed | Robustness of the ATLAS pixel clustering neural network algorithm |
title_short | Robustness of the ATLAS pixel clustering neural network algorithm |
title_sort | robustness of the atlas pixel clustering neural network algorithm |
topic | Particle Physics - Experiment |
url | http://cds.cern.ch/record/2162774 |
work_keys_str_mv | AT sideboperedvin robustnessoftheatlaspixelclusteringneuralnetworkalgorithm |