High-Dimensional Visual Analytics of Particle Kinematics

dc.contributor.authorPolys, Nicholas F.en
dc.contributor.authorDiefenthaler, Markusen
dc.contributor.authorRajamohan, Srijithen
dc.contributor.authorWhang, JooYoungen
dc.contributor.authorRomanov, Dmitryen
dc.contributor.authorDahshan, Maien
dc.date.accessioned2020-03-31T17:31:19Zen
dc.date.available2020-03-31T17:31:19Zen
dc.date.issued2020-03-31en
dc.description.abstractThe 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.notesFinal Project Reporten
dc.identifier.urihttp://hdl.handle.net/10919/97511en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.titleHigh-Dimensional Visual Analytics of Particle Kinematicsen
dc.typeReporten
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