Allbrooke, B M M, Asquith, L, Cerri, A, Chavez Barajas, C A, De Santo, A, Salvatore, F, Santoyo Castillo, I, Suruliz, K, Sutton, M R, Vivarelli, I and The ATLAS Collaboration, (2014) A neural network clustering algorithm for the ATLAS silicon pixel detector. Journal of Instrumentation, 9. P09009. ISSN 1748-0221

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB)

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:	Article
Schools and Departments:	School of Mathematical and Physical Sciences > Physics and Astronomy
Research Centres and Groups:	Experimental Particle Physics Research Group
Subjects:	Q Science > QC Physics
Depositing User:	Billy Wichaidit
Date Deposited:	09 May 2017 14:44
Last Modified:	09 May 2017 14:53
URI:	http://srodev.sussex.ac.uk/id/eprint/68021

View download statistics for this item

📧 Request an update