Name
Title | ||
|---|---|---|
A Conceptual Framework for the Use of Graph Representation Within High Energy Physics Analysis |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CCGRID.2018.00063 | 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) |
Keywords | Field | DocType |
Graphs,Machine Learning,High energy physics,Particle Identification | Graph,Task analysis,Formal representation,Theoretical computer science,Atmospheric measurements,Conceptual framework,Detector,Graph (abstract data type),Particle identification | Conference |
ISBN | Citations | PageRank |
978-1-5386-5816-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danielle Turvill | 1 | 0 | 0.34 |
| Lee Barnby | 2 | 0 | 0.34 |
| Ashiq Anjum | 3 | 333 | 38.33 |