TY - JOUR
T1 - A neural network clustering algorithm for the ATLAS silicon pixel detector
AU - Clark, Philip James
AU - Leonidopoulos, Christos
AU - Martin, Victoria Jane
AU - Mills, Corrinne
AU - Collaboration, Atlas
PY - 2014/9/15
Y1 - 2014/9/15
N2 - 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.
AB - 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.
U2 - 10.1088/1748-0221/9/09/P09009
DO - 10.1088/1748-0221/9/09/P09009
M3 - Article
SN - 1748-0221
VL - 9
SP - P09009
JO - Journal of Instrumentation
JF - Journal of Instrumentation
M1 - Aad:2014yva
ER -