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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 384-385
ABSTRACT
A new method is presented for improvement of the particle identification analysis process in a way which combines both the measured features, from detectors, and physics parameters. It is proposed that a graph representation can effectively express data in a format allowing for simpler interpretation and exploitation of all data available for analysis purposes. Nodes will represent entities and edges will represent the relation between them. Not only are graphs able to provide this useful structure and formal representation of knowledge but they can also be managed efficiently. Overall, this graphical representation will allow for the study of relationships between tracks, enable better pattern recognition and, as a result, improve the classification of particles.
INDEX TERMS
data analysis, graph theory, pattern classification, physics computing
CITATION

D. Turvill, L. Barnby and A. Anjum, "A Conceptual Framework for the Use of Graph Representation Within High Energy Physics Analysis," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 384-385.
doi:10.1109/CCGRID.2018.00063
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