Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/102347
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | A neural network clustering algorithm for the ATLAS silicon pixel detector |
Author: | The ATLAS Collaboration, Aad, G. |
Citation: | Journal of Instrumentation, 2014; 9(9):P09009-1-P09009-33 |
Publisher: | IOP Publishing |
Issue Date: | 2014 |
ISSN: | 1748-0221 1748-0221 |
Statement of Responsibility: | G. Aad … P. Jackson … L. Lee … A. Petridis … N. Soni … M. White ... et al. (The ATLAS Collaboration) |
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. |
Keywords: | Particle tracking detectors; particle tracking detectors (solid-state detectors) |
Rights: | © CERN 2014 for the benefit of the ATLAS collaboration, published under the terms of the Creative Commons Attribution 3.0 License by IOP Publishing Ltd and Sissa Medialab srl. Any further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation and DOI.. |
DOI: | 10.1088/1748-0221/9/09/P09009 |
Grant ID: | ARC |
Published version: | http://dx.doi.org/10.1088/1748-0221/9/09/p09009 |
Appears in Collections: | Aurora harvest 3 Chemistry and Physics publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
hdl_102347.pdf | Published version | 1.5 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.