An online neural network triggering system for the Tile Calorimeter

For the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for the online particle identification in beam tests of calorimeter prototypes. Although beam purity is usually very good for a selected particle type, background from wrong-type particles...

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Detalles Bibliográficos
Autores principales: Damazio, D O, De Seixas, J M, Magacho, P V
Lenguaje:eng
Publicado: 2002
Materias:
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1109/TNS.2002.1003739
http://cds.cern.ch/record/588647
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author Damazio, D O
De Seixas, J M
Magacho, P V
author_facet Damazio, D O
De Seixas, J M
Magacho, P V
author_sort Damazio, D O
collection CERN
description For the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for the online particle identification in beam tests of calorimeter prototypes. Although beam purity is usually very good for a selected particle type, background from wrong-type particles cannot be avoided and is routinely identified in the offline analysis. The proposed neural system is trained online to identify electrons, pions, and muons at different energy levels and it achieves more than 90% efficiency in terms of particle identification. The neural system is being implemented by integrating it to the readout drive (ROD) of the TileCal. (12 refs).
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2002
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spelling cern-5886472019-09-30T06:29:59Zdoi:10.1109/TNS.2002.1003739http://cds.cern.ch/record/588647engDamazio, D ODe Seixas, J MMagacho, P VAn online neural network triggering system for the Tile CalorimeterDetectors and Experimental TechniquesFor the hadronic calorimeter of ATLAS, TileCal, neural processing is used to establish an efficient methodology for the online particle identification in beam tests of calorimeter prototypes. Although beam purity is usually very good for a selected particle type, background from wrong-type particles cannot be avoided and is routinely identified in the offline analysis. The proposed neural system is trained online to identify electrons, pions, and muons at different energy levels and it achieves more than 90% efficiency in terms of particle identification. The neural system is being implemented by integrating it to the readout drive (ROD) of the TileCal. (12 refs).oai:cds.cern.ch:5886472002
spellingShingle Detectors and Experimental Techniques
Damazio, D O
De Seixas, J M
Magacho, P V
An online neural network triggering system for the Tile Calorimeter
title An online neural network triggering system for the Tile Calorimeter
title_full An online neural network triggering system for the Tile Calorimeter
title_fullStr An online neural network triggering system for the Tile Calorimeter
title_full_unstemmed An online neural network triggering system for the Tile Calorimeter
title_short An online neural network triggering system for the Tile Calorimeter
title_sort online neural network triggering system for the tile calorimeter
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1109/TNS.2002.1003739
http://cds.cern.ch/record/588647
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