A neural network clustering algorithm for the ATLAS silicon pixel detector

Allison, Lee and Barton, Adam and Borissov, Guennadi and Bouhova-Thacker, Eva and Catmore, James and Chilingarov, Alexandre and Dearnaley, William and Fox, Harald and Henderson, Robert and Hughes, Gareth and Jones, Roger William Lewis and Kartvelishvili, Vakhtang and Long, Robin and Love, Peter and Maddocks, Harvey and Smizanska, Maria and Walder, James (2014) A neural network clustering algorithm for the ATLAS silicon pixel detector. Journal of Instrumentation, 9 (9). ISSN 1748-0221

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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.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Instrumentation
Additional Information:
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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2610
Subjects:
ID Code:
71824
Deposited By:
Deposited On:
20 Nov 2014 11:13
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Mar 2020 03:12