High-Dimensional Visual Analytics of Particle Kinematics
| dc.contributor.author | Polys, Nicholas F. | en |
| dc.contributor.author | Diefenthaler, Markus | en |
| dc.contributor.author | Rajamohan, Srijith | en |
| dc.contributor.author | Whang, JooYoung | en |
| dc.contributor.author | Romanov, Dmitry | en |
| dc.contributor.author | Dahshan, Mai | en |
| dc.date.accessioned | 2020-03-31T17:31:19Z | en |
| dc.date.available | 2020-03-31T17:31:19Z | en |
| dc.date.issued | 2020-03-31 | en |
| dc.description.abstract | The goal of this project was to explore the feasibility of Semantic Interaction (SI) methods [SI1, SI2] for Nuclear Femtography. Semantic Interaction is an approach to Human and Machine learning that enables the users to explore and refine their understanding of correlations and inter-relationships within large amounts of multidimensional data. Semantic Interaction combines statistical mathematics and machine learning with real-time scientific visualization. While a variety of visualization techniques can help scientists to gain a more comprehensive understandings of their data, Semantic Interaction uses the history of the user’s interaction to learn about what the user considers as relevant features and allows to map the n-dimensional correlations in a n-dimensional data set. Toward the exploration of high-dimensional nuclear physics data, we pursued two objectives: 1) adapt our Graphically-Linked Ensemble Explorer (GLEE) to load the results of nuclear physics experiments and 2) evaluate the results with Jefferson Lab scientists and the CNF community. | en |
| dc.description.notes | Final Project Report | en |
| dc.identifier.uri | http://hdl.handle.net/10919/97511 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en |
| dc.title | High-Dimensional Visual Analytics of Particle Kinematics | en |
| dc.type | Report | en |