Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4854
Title: A neural network clustering algorithm for the ATLAS silicon pixel detector
Authors: Aad, G.
Abbott, B.
Abdallah, J.
Khalek, S. Abdel
Abdinov, O.
Aben, R.
The ATLAS Collaboration
Sultansoy, Saleh
Keywords: Particle tracking detectors
Particle tracking detectors (Solid-state detectors)
Issue Date: 2014
Publisher: Iop Publishing Ltd
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.
URI: https://doi.org/10.1088/1748-0221/9/09/P09009
https://hdl.handle.net/20.500.11851/4854
ISSN: 1748-0221
Appears in Collections:Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü / Department of Material Science & Nanotechnology Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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