A neural network clustering algorithm for the ATLAS silicon pixel detector

Aad, G.

doi:10.1088/1748-0221/9/09/P09009

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

Journal Article · Mon Sep 01 00:00:00 EDT 2014 · Journal of Instrumentation

DOI:https://doi.org/10.1088/1748-0221/9/09/P09009· OSTI ID:1523942

Aad, G. ^[1]

Aix-Marseille Univ., and CNRS/IN2P3, Marseille (France). et. al.

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.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Contributing Organization:: The ATLAS collaboration

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1523942

Journal Information:: Journal of Instrumentation, Vol. 9, Issue 09; ISSN 1748-0221

Publisher:: Institute of Physics (IOP)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 15 works

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