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• W2578602677 abstract "One of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the $$mathrm{ND}^2mathrm{AV}$$ : N-dimensional data analysis and visualization framework, a user-friendly tool aimed at exploiting the intuition and current workflow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. $$mathrm{ND}^2mathrm{AV}$$ uses a modular design providing easy extensibility and customization for different applications. $$mathrm{ND}^2mathrm{AV}$$ is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries." @default.
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• W2578602677 date "2015-02-01" @default.
• W2578602677 modified "2023-09-27" @default.
• W2578602677 title "$$mathrm{ND}^2mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign" @default.
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• W2578602677 doi "https://doi.org/10.1007/s00791-015-0241-3" @default.
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