Using a neural network approach for muon reconstruction and triggering

The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedic...

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Detalles Bibliográficos
Autores principales: Etzion, E., Abramowicz, H., Benhammou, Y., Dror, G., Horn, D., Levinson, L., Livneh, R.
Lenguaje:eng
Publicado: 2004
Materias:
Other Fields of Physics
Acceso en línea:https://dx.doi.org/10.1016/j.nima.2004.07.091
http://cds.cern.ch/record/820652
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author Etzion, E.
Abramowicz, H.
Benhammou, Y.
Dror, G.
Horn, D.
Levinson, L.
Livneh, R.
author_facet Etzion, E.
Abramowicz, H.
Benhammou, Y.
Dror, G.
Horn, D.
Levinson, L.
Livneh, R.
author_sort Etzion, E.
collection CERN
description The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.
id cern-820652
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2004
record_format invenio
spelling cern-8206522023-03-20T10:34:50Zdoi:10.1016/j.nima.2004.07.091http://cds.cern.ch/record/820652engEtzion, E.Abramowicz, H.Benhammou, Y.Dror, G.Horn, D.Levinson, L.Livneh, R.Using a neural network approach for muon reconstruction and triggeringOther Fields of PhysicsThe extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.The extremely high rate of events that will be produced in the future Large Hadron Collider requires the triggering mechanism to take precise decisions in a few nano-seconds. We present a study which used an artificial neural network triggering algorithm and compared it to the performance of a dedicated electronic muon triggering system. Relatively simple architecture was used to solve a complicated inverse problem. A comparison with a realistic example of the ATLAS first level trigger simulation was in favour of the neural network. A similar architecture trained after the simulation of the electronics first trigger stage showed a further background rejection.physics/0402070TAUP-2764-04TAUP-2764oai:cds.cern.ch:8206522004-02-16
spellingShingle Other Fields of Physics
Etzion, E.
Abramowicz, H.
Benhammou, Y.
Dror, G.
Horn, D.
Levinson, L.
Livneh, R.
Using a neural network approach for muon reconstruction and triggering
title Using a neural network approach for muon reconstruction and triggering
title_full Using a neural network approach for muon reconstruction and triggering
title_fullStr Using a neural network approach for muon reconstruction and triggering
title_full_unstemmed Using a neural network approach for muon reconstruction and triggering
title_short Using a neural network approach for muon reconstruction and triggering
title_sort using a neural network approach for muon reconstruction and triggering
topic Other Fields of Physics
url https://dx.doi.org/10.1016/j.nima.2004.07.091
http://cds.cern.ch/record/820652
work_keys_str_mv AT etzione usinganeuralnetworkapproachformuonreconstructionandtriggering
AT abramowiczh usinganeuralnetworkapproachformuonreconstructionandtriggering
AT benhammouy usinganeuralnetworkapproachformuonreconstructionandtriggering
AT drorg usinganeuralnetworkapproachformuonreconstructionandtriggering
AT hornd usinganeuralnetworkapproachformuonreconstructionandtriggering
AT levinsonl usinganeuralnetworkapproachformuonreconstructionandtriggering
AT livnehr usinganeuralnetworkapproachformuonreconstructionandtriggering

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