A neural network clustering algorithm for the ATLAS silicon pixel detector

TL;DR

This paper introduces a neural network-based clustering method for the ATLAS pixel detector, improving the identification and splitting of merged particle clusters, leading to enhanced track resolution and reduced shared clusters in high-energy jets.

Contribution

It presents a novel neural network technique for cluster splitting in the ATLAS detector, replacing traditional methods and improving accuracy in high-energy collision data.

Findings

Reduces shared clusters in jets by up to a factor of three

Improves position and error estimates of clusters

Enhances impact parameter resolution

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.