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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A neural network clustering algorithm for the ATLAS silicon pixel detector
AU - Allison, Lee
AU - Barton, Adam
AU - Borissov, Guennadi
AU - Bouhova-Thacker, Eva
AU - Catmore, James
AU - Chilingarov, Alexandre
AU - Dearnaley, William
AU - Fox, Harald
AU - Henderson, Robert
AU - Hughes, Gareth
AU - Jones, Roger William Lewis
AU - Kartvelishvili, Vakhtang
AU - Long, Robin
AU - Love, Peter
AU - Maddocks, Harvey
AU - Smizanska, Maria
AU - Walder, James
AU - The ATLAS collaboration
N1 - Published under the terms of the Creative Commons Attribution 3.0 License by IOP Publishing Ltd and Sissa Medialab srl. Any further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation and DOI.
PY - 2014/9
Y1 - 2014/9
N2 - 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.
AB - 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.
KW - Particle tracking detectors (Solid-state detectors)
KW - Particle tracking detectors
U2 - 10.1088/1748-0221/9/09/P09009
DO - 10.1088/1748-0221/9/09/P09009
M3 - Journal article
VL - 9
JO - Journal of Instrumentation
JF - Journal of Instrumentation
SN - 1748-0221
IS - 9
M1 - P09009
ER -