A neural network clustering algorithm for the ATLAS silicon pixel detector

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are...

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Autores principales: Alconada Verzini, María Josefina, Alonso, Francisco, Anduaga, Xabier Sebastián, Dova, María Teresa, Monticelli, Fernando Gabriel, Wahlberg, Hernán Pablo
Formato: Articulo
Lenguaje:Inglés
Publicado: 2014
Materias:
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/85038
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id I19-R120-10915-85038
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Particle tracking detectors
Particle tracking detectors (solid-state detectors)
spellingShingle Física
Particle tracking detectors
Particle tracking detectors (solid-state detectors)
Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastián
Dova, María Teresa
Monticelli, Fernando Gabriel
Wahlberg, Hernán Pablo
A neural network clustering algorithm for the ATLAS silicon pixel detector
topic_facet Física
Particle tracking detectors
Particle tracking detectors (solid-state detectors)
description A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
format Articulo
Articulo
author Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastián
Dova, María Teresa
Monticelli, Fernando Gabriel
Wahlberg, Hernán Pablo
author_facet Alconada Verzini, María Josefina
Alonso, Francisco
Anduaga, Xabier Sebastián
Dova, María Teresa
Monticelli, Fernando Gabriel
Wahlberg, Hernán Pablo
author_sort Alconada Verzini, María Josefina
title A neural network clustering algorithm for the ATLAS silicon pixel detector
title_short A neural network clustering algorithm for the ATLAS silicon pixel detector
title_full A neural network clustering algorithm for the ATLAS silicon pixel detector
title_fullStr A neural network clustering algorithm for the ATLAS silicon pixel detector
title_full_unstemmed A neural network clustering algorithm for the ATLAS silicon pixel detector
title_sort neural network clustering algorithm for the atlas silicon pixel detector
publishDate 2014
url http://sedici.unlp.edu.ar/handle/10915/85038
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