2006
Ahrens, James; Heitmann, Katrin; Habib, Salman; Ankeny, Lee; McCormick, Patrick; Inman, Jeff; Armstrong, Ryan; Ma, Kwan-Liu
Quantitative and comparative visualization applied to cosmological simulations Proceedings Article
In: Journal of Physics: Conference Series, pp. 526, IOP Publishing 2006, (LA-UR-06-4416).
Abstract | Links | BibTeX | Tags: comparative visualization, cosmology, Quantitative visualization
@inproceedings{ahrens2006quantitative,
title = {Quantitative and comparative visualization applied to cosmological simulations},
author = {James Ahrens and Katrin Heitmann and Salman Habib and Lee Ankeny and Patrick McCormick and Jeff Inman and Ryan Armstrong and Kwan-Liu Ma},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/QuantitativeAndComparativeVisualizationAppliedToCosmologicalSimulations.pdf},
year = {2006},
date = {2006-01-01},
booktitle = {Journal of Physics: Conference Series},
volume = {46},
number = {1},
pages = {526},
organization = {IOP Publishing},
abstract = {Cosmological simulations follow the formation of nonlinear structure in dark and luminous matter. The associated simulation volumes and dynamic range are very large, making visualization both a necessary and challenging aspect of the analysis of these datasets. Our goal is to understand sources of inconsistency between different simulation codes that are started from the same initial conditions. Quantitative visualization supports the definition and reasoning about analytically defined features of interest. Comparative visualization supports the ability to visually study, side by side, multiple related visualizations of these simulations. For instance, a scientist can visually distinguish that there are fewer halos (localized lumps of tracer particles) in low-density regions for one simulation code out of a collection. This qualitative result will enable the scientist to develop a hypothesis, such as loss of halos in low-density regions due to limited resolution, to explain the inconsistency between the different simulations. Quantitative support then allows one to confirm or reject the hypothesis. If the hypothesis is rejected, this step may lead to new insights and a new hypothesis, not available from the purely qualitative analysis. We will present methods to significantly improve the scientific analysis process by incorporating quantitative analysis as the driver for visualization. Aspects of this work are included as part of two visualization tools, ParaView, an open-source large data visualization tool, and Scout, an analysis-language based, hardware-accelerated visualization tool.},
note = {LA-UR-06-4416},
keywords = {comparative visualization, cosmology, Quantitative visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Cosmological simulations follow the formation of nonlinear structure in dark and luminous matter. The associated simulation volumes and dynamic range are very large, making visualization both a necessary and challenging aspect of the analysis of these datasets. Our goal is to understand sources of inconsistency between different simulation codes that are started from the same initial conditions. Quantitative visualization supports the definition and reasoning about analytically defined features of interest. Comparative visualization supports the ability to visually study, side by side, multiple related visualizations of these simulations. For instance, a scientist can visually distinguish that there are fewer halos (localized lumps of tracer particles) in low-density regions for one simulation code out of a collection. This qualitative result will enable the scientist to develop a hypothesis, such as loss of halos in low-density regions due to limited resolution, to explain the inconsistency between the different simulations. Quantitative support then allows one to confirm or reject the hypothesis. If the hypothesis is rejected, this step may lead to new insights and a new hypothesis, not available from the purely qualitative analysis. We will present methods to significantly improve the scientific analysis process by incorporating quantitative analysis as the driver for visualization. Aspects of this work are included as part of two visualization tools, ParaView, an open-source large data visualization tool, and Scout, an analysis-language based, hardware-accelerated visualization tool.
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1.
Ahrens, James; Heitmann, Katrin; Habib, Salman; Ankeny, Lee; McCormick, Patrick; Inman, Jeff; Armstrong, Ryan; Ma, Kwan-Liu
Quantitative and comparative visualization applied to cosmological simulations Proceedings Article
In: Journal of Physics: Conference Series, pp. 526, IOP Publishing 2006, (LA-UR-06-4416).
@inproceedings{ahrens2006quantitative,
title = {Quantitative and comparative visualization applied to cosmological simulations},
author = {James Ahrens and Katrin Heitmann and Salman Habib and Lee Ankeny and Patrick McCormick and Jeff Inman and Ryan Armstrong and Kwan-Liu Ma},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/QuantitativeAndComparativeVisualizationAppliedToCosmologicalSimulations.pdf},
year = {2006},
date = {2006-01-01},
booktitle = {Journal of Physics: Conference Series},
volume = {46},
number = {1},
pages = {526},
organization = {IOP Publishing},
abstract = {Cosmological simulations follow the formation of nonlinear structure in dark and luminous matter. The associated simulation volumes and dynamic range are very large, making visualization both a necessary and challenging aspect of the analysis of these datasets. Our goal is to understand sources of inconsistency between different simulation codes that are started from the same initial conditions. Quantitative visualization supports the definition and reasoning about analytically defined features of interest. Comparative visualization supports the ability to visually study, side by side, multiple related visualizations of these simulations. For instance, a scientist can visually distinguish that there are fewer halos (localized lumps of tracer particles) in low-density regions for one simulation code out of a collection. This qualitative result will enable the scientist to develop a hypothesis, such as loss of halos in low-density regions due to limited resolution, to explain the inconsistency between the different simulations. Quantitative support then allows one to confirm or reject the hypothesis. If the hypothesis is rejected, this step may lead to new insights and a new hypothesis, not available from the purely qualitative analysis. We will present methods to significantly improve the scientific analysis process by incorporating quantitative analysis as the driver for visualization. Aspects of this work are included as part of two visualization tools, ParaView, an open-source large data visualization tool, and Scout, an analysis-language based, hardware-accelerated visualization tool.},
note = {LA-UR-06-4416},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Cosmological simulations follow the formation of nonlinear structure in dark and luminous matter. The associated simulation volumes and dynamic range are very large, making visualization both a necessary and challenging aspect of the analysis of these datasets. Our goal is to understand sources of inconsistency between different simulation codes that are started from the same initial conditions. Quantitative visualization supports the definition and reasoning about analytically defined features of interest. Comparative visualization supports the ability to visually study, side by side, multiple related visualizations of these simulations. For instance, a scientist can visually distinguish that there are fewer halos (localized lumps of tracer particles) in low-density regions for one simulation code out of a collection. This qualitative result will enable the scientist to develop a hypothesis, such as loss of halos in low-density regions due to limited resolution, to explain the inconsistency between the different simulations. Quantitative support then allows one to confirm or reject the hypothesis. If the hypothesis is rejected, this step may lead to new insights and a new hypothesis, not available from the purely qualitative analysis. We will present methods to significantly improve the scientific analysis process by incorporating quantitative analysis as the driver for visualization. Aspects of this work are included as part of two visualization tools, ParaView, an open-source large data visualization tool, and Scout, an analysis-language based, hardware-accelerated visualization tool.