Auteur(s): Aad G., Albrand S., Brown J., Collot J., Crépé-Renaudin S., Dechenaux B., Delsart P.A., Gabaldon C., Genest M.H., Hostachy J.Y., Le B.T., Ledroit-Guillon F., Lleres A., Lucotte A., Malek F., Monini C., Stark J., Trocmé B., Wu M., Rahal G., Abdel Khalek S., Bassalat A., Becot C., Binet S., Bourdarios C., Charfeddine D., De Vivie De Regie J.B., Duflot L., Escalier M., Ughetto M.
(Article) Publié:
Journal Of Instrumentation, vol. 9 p.09009 (2014)
Texte intégral en Openaccess : 
Ref Arxiv: 1406.7690
DOI: 10.1088/1748-0221/9/09/P09009
WoS: 000343281300046
Ref. & Cit.: NASA ADS
15 Citations
Résumé:
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.
Commentaires: See paper for full list of authors - 6 pages plus author list + cover pages (38 pages total), 10 figures, 0 tables, submitted to JINST, All figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/PERF-2012-05