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.

Autore Pugliese

• Ventura Andrea , Gorini Edoardo , Spagnolo Stefania Antonia

Tutti gli autori

• G. AAD , G. CHIODINI , E. GORINI , M. PRIMAVERA , S. SPAGNOLO , A. VENTURA , ET AL.

Titolo volume/Rivista

JOURNAL OF INSTRUMENTATION

Anno di pubblicazione

2014

ISSN

1748-0221

ISBN

Non Disponibile

Numero di citazioni Wos

3

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

16

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile