@Article{ATLASCollaborationAad_etal2014,
author="ATLAS Collaboration (Aad, G. et al
and Cabrera Urban, S.
and Castillo Gimenez, V.
and Costa, M. J.
and Ferrer, A.
and Fiorini, L.
and Fuster, J.
and Garcia, C.
and Garcia Navarro, J. E.
and Gonzalez de la Hoz, S.
and Hernandez Jimenez, Y.
and Higon-Rodriguez, E.
and Irles Quiles, A.
and Kaci, M.
and King, M.
and Lacasta, C.
and Lacuesta, V. R.
and March, L.
and Marti-Garcia, S.
and Mitsou, V. A.
and Moles-Valls, R.
and Oliver Garcia, E.
and Pedraza Lopez, S.
and Perez Garcia-Esta{\~A}{\textpm}, M. T.
and Romero Adam, E.
and Ros, E.
and Salt, J.
and Sanchez Martinez, V.
and Soldevila, U.
and Sanchez, J.
and Torro Pastor, E.
and Valero, A.
and Valladolid Gallego, E.
and Valls Ferrer, J. A.
and Vos, M.",
title="A neural network clustering algorithm for the ATLAS silicon pixel detector",
journal="Journal of Instrumentation",
year="2014",
publisher="Iop Publishing Ltd",
volume="9",
pages="P09009 - 34pp",
optkeywords="Particle tracking detectors; Particle tracking detectors (Solid-state detectors)",
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.",
optnote="WOS:000343281300046",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=1972), last updated on Thu, 13 Nov 2014 12:22:03 +0000",
issn="1748-0221",
doi="10.1088/1748-0221/9/09/P09009",
opturl="http://arxiv.org/abs/arXiv:1406.7690",
opturl="https://doi.org/10.1088/1748-0221/9/09/P09009",
archivePrefix="arXiv",
eprint="arXiv:1406.7690",
language="English"
}