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Title: A neural network clustering algorithm for the ATLAS silicon pixel detector

Abstract

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.

Authors:
 [1]
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  1. Aix-Marseille Univ., and CNRS/IN2P3, Marseille (France). et. al.
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
Contributing Org.:
The ATLAS collaboration
OSTI Identifier:
1523942
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 9; Journal Issue: 09; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Particle tracking detectors; Particle tracking detectors (Solid-state detectors)

Citation Formats

Aad, G. A neural network clustering algorithm for the ATLAS silicon pixel detector. United States: N. p., 2014. Web. doi:10.1088/1748-0221/9/09/P09009.
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Aad, G. A neural network clustering algorithm for the ATLAS silicon pixel detector. United States. https://doi.org/10.1088/1748-0221/9/09/P09009
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Aad, G. Mon . "A neural network clustering algorithm for the ATLAS silicon pixel detector". United States. https://doi.org/10.1088/1748-0221/9/09/P09009. https://www.osti.gov/servlets/purl/1523942.
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@article{osti_1523942,
title = {A neural network clustering algorithm for the ATLAS silicon pixel detector},
author = {Aad, G.},
abstractNote = {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.},
doi = {10.1088/1748-0221/9/09/P09009},
journal = {Journal of Instrumentation},
number = 09,
volume = 9,
place = {United States},
year = {Mon Sep 01 00:00:00 EDT 2014},
month = {Mon Sep 01 00:00:00 EDT 2014}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1088/1748-0221/9/09/P09009
Copyright Statement
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Citation Metrics:
Cited by: 15 works
Citation information provided by
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Figures / Tables:

Figure 1 Figure 1: Geometry of the ATLAS pixel detector. There are three concentric cylindrical barrel layers and
All figures and tables (10 total)
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Works referenced in this record:

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    Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
    journal, June 2017

    • Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.
    • Nature Communications, Vol. 8, Issue 1
    • DOI: 10.1038/ncomms15461

    Parameterized neural networks for high-energy physics
    journal, April 2016

    Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
    text, January 2017

    • Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.
    • Deutsches Elektronen-Synchrotron, DESY, Hamburg
    • DOI: 10.3204/pubdb-2018-00593

    Production and Integration of the ATLAS Insertable B-Layer
    text, January 2018

    Production and Integration of the ATLAS Insertable B-Layer
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    Deep learning in color: towards automated quark/gluon jet discrimination
    text, January 2016

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      Figures / Tables found in this record:

      Figure 1 (p. 7)figure
      Figure 2 (p. 8)figure
      Figure 3 (p. 9)figure
      Figure 4 (p. 10)figure
      Figure 5 (p. 15)figure
      Figure 6 (p. 16)figure
      Figure 7 (p. 17)figure
      Figure 8 (p. 18)figure
      Figure 9 (p. 18)figure
      Figure 10 (p. 19)figure
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