Name
Title | ||
|---|---|---|
Joint Contour Net Analysis for Feature Detection in Lattice Quantum Chromodynamics Data. |
Abstract | ||
|---|---|---|
In this paper we demonstrate the use of multivariate topological algorithms to analyse and interpret Lattice Quantum Chromodynamics (QCD) data. Lattice QCD is a long established field of theoretical physics research in the pursuit of understanding the strong nuclear force. Complex computer simulations model interactions between quarks and gluons to test theories regarding the behaviour of matter in a range of extreme environments. Data sets are typically generated using Monte Carlo methods, providing an ensemble of configurations, from which observable averages must be computed. This presents issues with regard to visualisation and analysis of the data as a typical ensemble study can generate hundreds or thousands of unique configurations. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.bdr.2019.02.003 | Big Data Research |
Keywords | Field | DocType |
Multivariate,Topology driven visualisation,Temporal data,Analysis,Volume visualisation,Lattice quantum chromodynamics | Quantum chromodynamics,Statistical physics,Data mining,Monte Carlo method,Data set,Observable,Visualization,Computer science,Quark–gluon plasma,Lattice QCD,Strong interaction | Journal |
Volume | ISSN | Citations |
15 | 2214-5796 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dean P. Thomas | 1 | 0 | 0.34 |
| Rita Borgo | 2 | 266 | 18.44 |
| Robert S. Laramee | 3 | 1405 | 85.31 |
| S. Hands | 4 | 0 | 1.01 |