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A neural network clustering algorithm for the ATLAS silicon pixel detector

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journal contribution
posted on 2023-06-09, 06:13 authored by Benedict AllbrookeBenedict Allbrooke, Lily AsquithLily Asquith, Alessandro CerriAlessandro Cerri, C A Chavez Barajas, Antonella De SantoAntonella De Santo, Fabrizio SalvatoreFabrizio Salvatore, I Santoyo Castillo, K Suruliz, Mark SuttonMark Sutton, Iacopo Vivarelli, The ATLAS Collaboration
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.

Funding

ATLAS; G0275; STFC-SCIENCE AND TECHNOLOGY FACILITIES COUNCIL; ST/I006048/1

History

Publication status

  • Published

File Version

  • Published version

Journal

Journal of Instrumentation

ISSN

1748-0221

Publisher

Institute of Physics

Volume

9

Page range

P09009

Department affiliated with

  • Physics and Astronomy Publications

Research groups affiliated with

  • Experimental Particle Physics Research Group Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-05-09

First Open Access (FOA) Date

2017-05-09

First Compliant Deposit (FCD) Date

2017-05-09

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