Neural network based cluster reconstruction in the ATLAS pixel detector

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The ATLAS Pixel detector currently determining particle positions at 8 TeV proton-proton collisions is working with a dense track environment. Due to these tiny particle separations, shared cluster are produced. Thus, the aim of the NN implementation is to identify merged clusters and improve the pa...

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Autor principal: Selbach, K E
Lenguaje:eng
Publicado: 2012
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/1455934
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author Selbach, K E
author_facet Selbach, K E
author_sort Selbach, K E
collection CERN
description The ATLAS Pixel detector currently determining particle positions at 8 TeV proton-proton collisions is working with a dense track environment. Due to these tiny particle separations, shared cluster are produced. Thus, the aim of the NN implementation is to identify merged clusters and improve the particle position resolution. By combining many variables with non-linear correlations, the NN is ideal to estimate the number of particles passing through a cluster and each of their position and uncertainty. As a result of the NN reconstruction, the impact parameter improves by ~15% which indicates boosted prospects for physics analysis.
id cern-1455934
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2012
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spelling cern-14559342019-09-30T06:29:59Zhttp://cds.cern.ch/record/1455934engSelbach, K ENeural network based cluster reconstruction in the ATLAS pixel detectorDetectors and Experimental TechniquesThe ATLAS Pixel detector currently determining particle positions at 8 TeV proton-proton collisions is working with a dense track environment. Due to these tiny particle separations, shared cluster are produced. Thus, the aim of the NN implementation is to identify merged clusters and improve the particle position resolution. By combining many variables with non-linear correlations, the NN is ideal to estimate the number of particles passing through a cluster and each of their position and uncertainty. As a result of the NN reconstruction, the impact parameter improves by ~15% which indicates boosted prospects for physics analysis.ATL-PHYS-PROC-2012-099oai:cds.cern.ch:14559342012-06-14
spellingShingle Detectors and Experimental Techniques
Selbach, K E
Neural network based cluster reconstruction in the ATLAS pixel detector
title Neural network based cluster reconstruction in the ATLAS pixel detector
title_full Neural network based cluster reconstruction in the ATLAS pixel detector
title_fullStr Neural network based cluster reconstruction in the ATLAS pixel detector
title_full_unstemmed Neural network based cluster reconstruction in the ATLAS pixel detector
title_short Neural network based cluster reconstruction in the ATLAS pixel detector
title_sort neural network based cluster reconstruction in the atlas pixel detector
topic Detectors and Experimental Techniques
url http://cds.cern.ch/record/1455934
work_keys_str_mv AT selbachke neuralnetworkbasedclusterreconstructionintheatlaspixeldetector

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