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21.
Biwer, Christoper M.; Capano, Collin; De, Soumi; Cabero, Miriam; Brown, Duncan; Nitz, Alexander; Raymond, Vivien
PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals Journal Article
In: Publications of the Astronomical Society of the Pacific, vol. 131, no. 996, pp. 024503, 2019.
@article{1538-3873-131-996-024503,
title = {PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals},
author = {Christoper M. Biwer and Collin Capano and Soumi De and Miriam Cabero and Duncan Brown and Alexander Nitz and Vivien Raymond},
url = {http://stacks.iop.org/1538-3873/131/i=996/a=024503},
year = {2019},
date = {2019-01-01},
journal = {Publications of the Astronomical Society of the Pacific},
volume = {131},
number = {996},
pages = {024503},
abstract = {We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters’ posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We introduce new modules in the open-source PyCBC gravitational-wave astronomy toolkit that implement Bayesian inference for compact-object binary mergers. We review the Bayesian inference methods implemented and describe the structure of the modules. We demonstrate that the PyCBC Inference modules produce unbiased estimates of the parameters of a simulated population of binary black hole mergers. We show that the parameters’ posterior distributions obtained using our new code agree well with the published estimates for binary black holes in the first Advanced LIGO–Virgo observing run.
22.
Samsel, Francesca; Wolfram, Phillip; Bares, Annie; Turton, Terece; Bujack, Roxana
Colormapping resources and strategies for organized intuitive environmental visualization Journal Article
In: Environmental Earth Sciences, vol. 78, no. 9, pp. 269, 2019, ISSN: 1866-6280, (LA-UR-19-20060).
@article{samsel2019colormapping,
title = {Colormapping resources and strategies for organized intuitive environmental visualization},
author = {Francesca Samsel and Phillip Wolfram and Annie Bares and Terece Turton and Roxana Bujack},
url = {https://doi.org/10.1007/s12665-019-8237-9},
doi = {10.1007/s12665-019-8237-9},
issn = {1866-6280},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Environmental Earth Sciences},
volume = {78},
number = {9},
pages = {269},
abstract = {Visualizations benefit from the use of intuitive organized color application, enabling a clearer understanding and communication. In this paper, we apply the concept of semantic color association to the generation of thematic colormaps for the environmental sciences in combination with principals of artistic color theory to expand feature resolution and create visual hierarchies within a visualization. In particular, we provide sets of color scales, colormaps and color organization guidance for semantically aligned water, atmosphere, land, and vegetation visualization. Strategies for directing attention via saturation levels and saturation sets of colormaps enable deployment of these techniques. All are publicly available online and accompanied by tools and strategy guidance.},
note = {LA-UR-19-20060},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Visualizations benefit from the use of intuitive organized color application, enabling a clearer understanding and communication. In this paper, we apply the concept of semantic color association to the generation of thematic colormaps for the environmental sciences in combination with principals of artistic color theory to expand feature resolution and create visual hierarchies within a visualization. In particular, we provide sets of color scales, colormaps and color organization guidance for semantically aligned water, atmosphere, land, and vegetation visualization. Strategies for directing attention via saturation levels and saturation sets of colormaps enable deployment of these techniques. All are publicly available online and accompanied by tools and strategy guidance.
23.
Dutta, Soumya; Biswas, Ayan; Ahrens, James
Multivariate Pointwise Information-Driven Data Sampling and Visualization Journal Article
In: Entropy, vol. 21, no. 7, 2019, ISSN: 1099-4300, (LA-UR-19-24243).
@article{e21070699,
title = {Multivariate Pointwise Information-Driven Data Sampling and Visualization},
author = {Soumya Dutta and Ayan Biswas and James Ahrens},
url = {https://www.mdpi.com/1099-4300/21/7/699},
doi = {10.3390/e21070699},
issn = {1099-4300},
year = {2019},
date = {2019-01-01},
journal = {Entropy},
volume = {21},
number = {7},
abstract = {With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.},
note = {LA-UR-19-24243},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.
24.
Dutta, Soumya; Brady, Riley; Maltrud, Mathew; Wolfram, Philip; Bujack, Roxana
Leveraging Lagrangian Analysis for Discriminating Nutrient Origins Proceedings Article
In: Bujack, Roxana; Feige, Kathrin; Rink, Karsten; Zeckzer, Dirk (Ed.): Workshop on Visualisation in Environmental Sciences (EnvirVis), pp. 17-24, The Eurographics Association, 2019, ISBN: 978-3-03868-086-4, (LA-UR-19-22455).
@inproceedings{N20103:2019,
title = {Leveraging Lagrangian Analysis for Discriminating Nutrient Origins},
author = {Soumya Dutta and Riley Brady and Mathew Maltrud and Philip Wolfram and Roxana Bujack},
editor = {Roxana Bujack and Kathrin Feige and Karsten Rink and Dirk Zeckzer},
url = {https://dsscale.org/wp-content/uploads/2019/07/leveraging-lagrangian-analysis-for-discriminating-nutrient-origins.pdf},
doi = {10.2312/envirvis.20191100},
isbn = {978-3-03868-086-4},
year = {2019},
date = {2019-01-01},
booktitle = {Workshop on Visualisation in Environmental Sciences (EnvirVis)},
pages = {17-24},
publisher = {The Eurographics Association},
note = {LA-UR-19-22455},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
25.
Baker, Allison; Hammerling, Dorit; Turton, Terece
Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data Journal Article
In: Computer Graphics Forum, 2019, ISSN: 1467-8659, (LA-UR-19-22420).
@article{10.1111:cgf.13707,
title = {Evaluating Image Quality Measures to Assess the Impact of Lossy Data Compression Applied to Climate Simulation Data},
author = {Allison Baker and Dorit Hammerling and Terece Turton},
url = {https://dsscale.org/wp-content/uploads/2019/07/BakerHammerlingTurtonEuroVis2019-EvalLossyCompression.pdf},
doi = {10.1111/cgf.13707},
issn = {1467-8659},
year = {2019},
date = {2019-01-01},
journal = {Computer Graphics Forum},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
note = {LA-UR-19-22420},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
26.
Banesh, Divya; Peterson, Mark; Wendelberger, Joanne; Ahrens, James; Hamann, Bernd
Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data Journal Article
In: 2019.
@article{8823794b,
title = {Comparison of piecewise linear change point detection with traditional analytical methods for ocean and climate data},
author = {Divya Banesh and Mark Peterson and Joanne Wendelberger and James Ahrens and Bernd Hamann},
url = {https://link.springer.com/article/10.1007/s12665-019-8636-y?wt_mc=Internal.Event.1.SEM.ArticleAuthorIncrementalIssue&utm_source=ArticleAuthorIncrementalIssue&utm_medium=email&utm_content=AA_en_06082018&ArticleAuthorIncrementalIssue_20191104},
year = {2019},
date = {2019-01-01},
abstract = {Earth's atmosphere and oceans are largely determined by periodic patterns of solar radiation, from daily and seasonal, to orbital variations over thousands of years. Dynamical processes alter these cycles with feedbacks and delays, so that the observed climate response is a combination of cyclical features and sudden regime changes. A primary example is the shift from a glacial (ice age) state to interglacial, which is driven by a 100-thousand year orbital cycle, while the transition occurs over a period of hundreds of years. Traditional methods of statistical analysis such as Fourier and wavelet transforms are very good at describing cyclical behavior but lack any characterization of singular events and regime changes. More recently, researchers have tested techniques in the statistical discipline of change point detection. This paper explores the unique advantages of a piecewise linear regression change point detection algorithm to identify events, regime shifts, and the direction of cyclical trends in geophysical data. It evaluates the reasons for choosing this particular change detection algorithm over other techniques by applying the technique to both observational and model data sets. A comparison of the proposed change detection algorithm to the more established statistical techniques shows the bene fits and drawbacks of each method.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Earth's atmosphere and oceans are largely determined by periodic patterns of solar radiation, from daily and seasonal, to orbital variations over thousands of years. Dynamical processes alter these cycles with feedbacks and delays, so that the observed climate response is a combination of cyclical features and sudden regime changes. A primary example is the shift from a glacial (ice age) state to interglacial, which is driven by a 100-thousand year orbital cycle, while the transition occurs over a period of hundreds of years. Traditional methods of statistical analysis such as Fourier and wavelet transforms are very good at describing cyclical behavior but lack any characterization of singular events and regime changes. More recently, researchers have tested techniques in the statistical discipline of change point detection. This paper explores the unique advantages of a piecewise linear regression change point detection algorithm to identify events, regime shifts, and the direction of cyclical trends in geophysical data. It evaluates the reasons for choosing this particular change detection algorithm over other techniques by applying the technique to both observational and model data sets. A comparison of the proposed change detection algorithm to the more established statistical techniques shows the bene fits and drawbacks of each method.
27.
Maack, Robin; Rogers, David; Hagen, Hans; Gillmann, Christina
Exploring Cinema Databases using multi-dimensional Image Measures Journal Article
In: 2019.
@article{maackexploring,
title = {Exploring Cinema Databases using multi-dimensional Image Measures},
author = {Robin Maack and David Rogers and Hans Hagen and Christina Gillmann},
url = {https://dsscale.org/wp-content/uploads/2020/03/LEVIA19_paper_2.pdf},
year = {2019},
date = {2019-01-01},
booktitle = {LEVIA 2019: Leipzig Symposium on Visualization in Applications},
abstract = {Exa-scale simulations can be hard to analyze because it is nearly impossible to store all computed time-steps and other parameters. The Cinema Database provides a storage saving solution, that captures images of each simulation time-step from a variety of camera angles. Still, the resulting number of images can be overwhelming and it is hard to find interesting images and features for further analysis. We present a zoom based approach where users can utilize arbitrary image measures to explore interesting images and further analyze their behavior in detail. We showed the effectiveness of our approach by providing two real world Cinema datasets.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Exa-scale simulations can be hard to analyze because it is nearly impossible to store all computed time-steps and other parameters. The Cinema Database provides a storage saving solution, that captures images of each simulation time-step from a variety of camera angles. Still, the resulting number of images can be overwhelming and it is hard to find interesting images and features for further analysis. We present a zoom based approach where users can utilize arbitrary image measures to explore interesting images and further analyze their behavior in detail. We showed the effectiveness of our approach by providing two real world Cinema datasets.
28.
Nardini, Pascal; Chen, Min; Samsel, Francesca; Bujack, Roxana; Bottinger, Michael; Scheuermann, Gerik
The making of continuous colormaps Journal Article
In: IEEE transactions on visualization and computer graphics, vol. 27, no. 6, pp. 3048–3063, 2019.
@article{nardini2019making,
title = {The making of continuous colormaps},
author = {Pascal Nardini and Min Chen and Francesca Samsel and Roxana Bujack and Michael Bottinger and Gerik Scheuermann},
url = {http://www.informatik.uni-leipzig.de/~bujack/2020ccc.pdf},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {IEEE transactions on visualization and computer graphics},
volume = {27},
number = {6},
pages = {3048--3063},
publisher = {IEEE},
abstract = {Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of “colormap specification (CMS)” that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS’s with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Continuous colormaps are integral parts of many visualization techniques, such as heat-maps, surface plots, and flow visualization. Despite that the critiques of rainbow colormaps have been around and well-acknowledged for three decades, rainbow colormaps are still widely used today. One reason behind the resilience of rainbow colormaps is the lack of tools for users to create a continuous colormap that encodes semantics specific to the application concerned. In this paper, we present a web-based software system, CCC-Tool (short for Charting Continuous Colormaps) under the URL https://ccctool.com, for creating, editing, and analyzing such application-specific colormaps. We introduce the notion of “colormap specification (CMS)” that maintains the essential semantics required for defining a color mapping scheme. We provide users with a set of advanced utilities for constructing CMS’s with various levels of complexity, examining their quality attributes using different plots, and exporting them to external application software. We present two case studies, demonstrating that the CCC-Tool can help domain scientists as well as visualization experts in designing semantically-rich colormaps.
29.
Gospodnetic, Petra; Banesh, Divya; Wolfram, Phillip; Petersen, Mark; Hagen, Hans; Ahrens, James; Rauhut, Markus
Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation Proceedings Article
In: 2018 IEEE Scientific Visualization Conference (SciVis), pp. 62-66, 2018.
@inproceedings{8823794,
title = {Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation},
author = {Petra Gospodnetic and Divya Banesh and Phillip Wolfram and Mark Petersen and Hans Hagen and James Ahrens and Markus Rauhut},
url = {https://ieeexplore.ieee.org/abstract/document/8823794
https://dsscale.org/wp-content/uploads/2019/10/08823794_optimized.pdf},
doi = {10.1109/SciVis.2018.8823794},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE Scientific Visualization Conference (SciVis)},
pages = {62-66},
abstract = {When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.
30.
Abram, Greg; Navrátil, Paul; Grossett, Pascal; Rogers, David; Ahrens, James
Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization Proceedings Article
In: 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), pp. 72-76, 2018, ISSN: null, (LA-UR-18-26088).
@inproceedings{8739241,
title = {Galaxy: Asynchronous Ray Tracing for Large High-Fidelity Visualization},
author = {Greg Abram and Paul Navrátil and Pascal Grossett and David Rogers and James Ahrens},
doi = {10.1109/LDAV.2018.8739241},
issn = {null},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)},
pages = {72-76},
abstract = {We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.},
note = {LA-UR-18-26088},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.
31.
De, Soumi; Finstad, Daniel; Lattimer, James; Brown, Duncan; Berger, Edo; Biwer, Christopher
Tidal Deformabilities and Radii of Neutron Stars from the Observation of GW170817 Journal Article
In: Phys. Rev. Lett., vol. 121, pp. 091102, 2018.
@article{PhysRevLett.121.091102,
title = {Tidal Deformabilities and Radii of Neutron Stars from the Observation of GW170817},
author = {Soumi De and Daniel Finstad and James Lattimer and Duncan Brown and Edo Berger and Christopher Biwer},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.091102},
doi = {10.1103/PhysRevLett.121.091102},
year = {2018},
date = {2018-08-01},
journal = {Phys. Rev. Lett.},
volume = {121},
pages = {091102},
publisher = {American Physical Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
32.
Cabero, Miriam; Capano, Collin; Fischer-Birnholtz, Ofek; Krishnan, Badri; Nielsen, Alex; Nitz, Alexander; Biwer, Christopher
Observational tests of the black hole area increase law Journal Article
In: Phys. Rev. D, vol. 97, pp. 124069, 2018.
@article{PhysRevD.97.124069,
title = {Observational tests of the black hole area increase law},
author = {Miriam Cabero and Collin Capano and Ofek Fischer-Birnholtz and Badri Krishnan and Alex Nielsen and Alexander Nitz and Christopher Biwer},
url = {https://link.aps.org/doi/10.1103/PhysRevD.97.124069},
doi = {10.1103/PhysRevD.97.124069},
year = {2018},
date = {2018-06-01},
journal = {Phys. Rev. D},
volume = {97},
pages = {124069},
publisher = {American Physical Society},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
33.
Vogel, Sven; Biwer, Chris; Rogers, David; Ahrens, James; Hackenberg, Robert; Onken, Drew; Zhang, Jianzhong
Interactive visualization of multi-data-set Rietveld analyses using Cinema:Debye-Scherrer Journal Article
In: Journal of Applied Crystallography, vol. 51, no. 3, pp. 943–951, 2018.
@article{Vogel:ks5597,
title = {Interactive visualization of multi-data-set Rietveld analyses using \textit{Cinema:Debye-Scherrer}},
author = {Sven Vogel and Chris Biwer and David Rogers and James Ahrens and Robert Hackenberg and Drew Onken and Jianzhong Zhang},
url = {https://doi.org/10.1107/S1600576718003989},
doi = {10.1107/S1600576718003989},
year = {2018},
date = {2018-06-01},
journal = {Journal of Applied Crystallography},
volume = {51},
number = {3},
pages = {943--951},
abstract = {A tool named \textit{Cinema:Debye-Scherrer} to visualize the results of a series of Rietveld analyses is presented. The multi-axis visualization of the high-dimensional data sets resulting from powder diffraction analyses allows identification of analysis problems, prediction of suitable starting values, identification of gaps in the experimental parameter space and acceleration of scientific insight from the experimental data. The tool is demonstrated with analysis results from 59 U—Nb alloy samples with different compositions, annealing times and annealing temperatures as well as with a high-temperature study of the crystal structure of CsPbBr_{3}. A script to extract parameters from a series of Rietveld analyses employing the widely used \textit{GSAS} Rietveld software is also described. Both software tools are available for download.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A tool named Cinema:Debye-Scherrer to visualize the results of a series of Rietveld analyses is presented. The multi-axis visualization of the high-dimensional data sets resulting from powder diffraction analyses allows identification of analysis problems, prediction of suitable starting values, identification of gaps in the experimental parameter space and acceleration of scientific insight from the experimental data. The tool is demonstrated with analysis results from 59 U—Nb alloy samples with different compositions, annealing times and annealing temperatures as well as with a high-temperature study of the crystal structure of CsPbBr3. A script to extract parameters from a series of Rietveld analyses employing the widely used GSAS Rietveld software is also described. Both software tools are available for download.
34.
Pulido, Jesus; Livescu, Daniel; Kanov, Kalin; Burns, Randal; Canada, Curtis; Ahrens, James; Hamann, Bernd
Remote Visual Analysis of Large Turbulence Databases at Multiple Scales Journal Article
In: Journal of Parallel and Distributed Computing, 2018, ISBN: 0743-7315, (LA-UR-17-20757).
@article{info:lanl-repo/lareport/LA-UR-17-20757,
title = {Remote Visual Analysis of Large Turbulence Databases at Multiple Scales},
author = {Jesus Pulido and Daniel Livescu and Kalin Kanov and Randal Burns and Curtis Canada and James Ahrens and Bernd Hamann},
url = {https://www.sciencedirect.com/science/article/pii/S0743731518303927},
doi = {https://doi.org/10.1016/j.jpdc.2018.05.011},
isbn = {0743-7315},
year = {2018},
date = {2018-01-01},
journal = {Journal of Parallel and Distributed Computing},
abstract = {The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.},
note = {LA-UR-17-20757},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.
35.
Finstad, Daniel; De, Soumi; Brown, Duncan; Berger, Edo; Biwer, Christopher
Measuring the Viewing Angle of GW170817 with Electromagnetic and Gravitational Waves Journal Article
In: The Astrophysical Journal Letters, vol. 860, no. 1, pp. L2, 2018.
@article{2041-8205-860-1-L2,
title = {Measuring the Viewing Angle of GW170817 with Electromagnetic and Gravitational Waves},
author = {Daniel Finstad and Soumi De and Duncan Brown and Edo Berger and Christopher Biwer},
url = {http://stacks.iop.org/2041-8205/860/i=1/a=L2},
year = {2018},
date = {2018-01-01},
journal = {The Astrophysical Journal Letters},
volume = {860},
number = {1},
pages = {L2},
abstract = {The joint detection of gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 ushered in a new era of multi-messenger astronomy. Joint GW–EM observations can be used to measure the parameters of the binary with better precision than either observation alone. Here, we use joint GW–EM observations to measure the viewing angle of GW170817, the angle between the binary’s angular momentum and the line of sight. We combine a direct measurement of the distance to the host galaxy of GW170817 (NGC 4993) of 40.7 ± 2.36 Mpc with the Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo GW data and find that the viewing angle is 32_{-13}^{+10}± 1.7 degrees (90% confidence, statistical, and systematic errors). We place a conservative lower limit on the viewing angle of ≥13°, which is robust to the choice of prior. This measurement provides a constraint on models of the prompt γ -ray and radio/X-ray afterglow emission associated with the merger; for example, it is consistent with the off-axis viewing angle inferred for a structured jet model. We provide for the first time the full posterior samples from Bayesian parameter estimation of LIGO/Virgo data to enable further analysis by the community.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The joint detection of gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 ushered in a new era of multi-messenger astronomy. Joint GW–EM observations can be used to measure the parameters of the binary with better precision than either observation alone. Here, we use joint GW–EM observations to measure the viewing angle of GW170817, the angle between the binary’s angular momentum and the line of sight. We combine a direct measurement of the distance to the host galaxy of GW170817 (NGC 4993) of 40.7 ± 2.36 Mpc with the Laser Interferometer Gravitational-wave Observatory (LIGO)/Virgo GW data and find that the viewing angle is 32-13+10± 1.7 degrees (90% confidence, statistical, and systematic errors). We place a conservative lower limit on the viewing angle of ≥13°, which is robust to the choice of prior. This measurement provides a constraint on models of the prompt γ -ray and radio/X-ray afterglow emission associated with the merger; for example, it is consistent with the off-axis viewing angle inferred for a structured jet model. We provide for the first time the full posterior samples from Bayesian parameter estimation of LIGO/Virgo data to enable further analysis by the community.
36.
Ware, Colin; Turton, Terece; Bujack, Roxana; Samsel, Francesca; Shrivastava, Piyush; Rogers, David
Measuring and Modeling the Feature Detection Threshold Functions of Colormaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, 2018, ISSN: 1077-2626, (LA-UR-18-21476).
@article{8413174,
title = {Measuring and Modeling the Feature Detection Threshold Functions of Colormaps},
author = {Colin Ware and Terece Turton and Roxana Bujack and Francesca Samsel and Piyush Shrivastava and David Rogers},
url = {https://ieeexplore.ieee.org/document/8413174},
doi = {10.1109/TVCG.2018.2855742},
issn = {1077-2626},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1-1},
abstract = {Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.},
note = {LA-UR-18-21476},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.
37.
Biswas, Ayan; Dutta, Soumya; Pulido, Jesus; Ahrens, James
In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization Proceedings Article
In: Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, pp. 13–18, ACM, Dallas, Texas, 2018, ISBN: 978-1-4503-6579-6.
@inproceedings{Biswas:2018:SDA:3281464.3281467,
title = {In Situ Data-driven Adaptive Sampling for Large-scale Simulation Data Summarization},
author = {Ayan Biswas and Soumya Dutta and Jesus Pulido and James Ahrens},
url = {http://doi.acm.org/10.1145/3281464.3281467
https://datascience.dsscale.org/wp-content/uploads/2019/01/LA-UR-18-28035.pdf},
doi = {10.1145/3281464.3281467},
isbn = {978-1-4503-6579-6},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization},
pages = {13--18},
publisher = {ACM},
address = {Dallas, Texas},
series = {ISAV '18},
abstract = {Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent advancements in high-performance computing have enabled scientists to model various scientific phenomena in great detail. However, the analysis and visualization of the output data from such large-scale simulations are posing significant challenges due to their excessive size and disk I/O bottlenecks. One viable solution to this problem is to create a sub-sampled dataset which is able to preserve the important information of the data and also is significantly smaller in size compared to the raw data. Creating an in situ workflow for generating such intelligently sub-sampled datasets is of prime importance for such simulations. In this work, we propose an information-driven data sampling technique and compare it with two well-known sampling methods to demonstrate the superiority of the proposed method. The in situ performance of the proposed method is evaluated by applying it to the Nyx Cosmology simulation. We compare and contrast the performance of these various sampling algorithms and provide a holistic view of all the methods so that the scientists can choose appropriate sampling schemes based on their analysis requirements.
38.
Zeyen, Max; Post, Tobias; Hagen, Hans; Ahrens, James; Rogers, David; Bujack, Roxana
Color Interpolation for Non-Euclidean Color Spaces Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
@inproceedings{zeyen2018interpolation,
title = {Color Interpolation for Non-Euclidean Color Spaces},
author = {Max Zeyen and Tobias Post and Hans Hagen and James Ahrens and David Rogers and Roxana Bujack},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/ColorInterpolationforNon-EuclideanColorSpaces.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Color interpolation is critical to many applications across a variety of domains, like color mapping or image processing. Due to the characteristics of the human visual system, color spaces whose distance measure is designed to mimic perceptual color differences tend to be non-Euclidean. In this setting, a generalization of established interpolation schemes is not trivial. This paper presents an approach to generalize linear interpolation to colors for color spaces equipped with an arbitrary non-Euclidean distance measure. It makes use of the fact that in Euclidean spaces, a straight line coincides with the shortest path between two points. Additionally, we provide an interactive implementation of our method for the CIELAB color space using the CIEDE2000 distance measure integrated into VTK and ParaView.
39.
Bujack, Roxana; Turton, Terece; Rogers, David; Ahrens, James
Ordering Perceptions about Perceptual Order Proceedings Article
In: IEEE Scientific Visualization Conference (SciVis) Short Papers, IEEE, 2018.
@inproceedings{bujack2018ordering,
title = {Ordering Perceptions about Perceptual Order},
author = {Roxana Bujack and Terece Turton and David Rogers and James Ahrens},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/OrderingPerceptionsaboutPerceptualOrder.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {IEEE Scientific Visualization Conference (SciVis) Short Papers},
publisher = {IEEE},
abstract = {One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
One of the most important properties that inherently defines a good colormap is perceptual order. In the literature, we find a wide range of recommendations and hypotheses regarding order. Properties such as monotonicity in luminance, saturation, or hue are/are not stated as necessary/sufficient to ensure perceptual order. In this paper, we gather the most common statements about perceptual order and, when possible, prove or disprove them.
40.
Sane, Sudhanshu; Bujack, Roxana; Childs, Hank
Revisiting the Evaluation of In Situ Lagrangian Analysis Proceedings Article
In: Childs, Hank; Cucchietti, Fernando (Ed.): Eurographics Symposium on Parallel Graphics and Visualization, The Eurographics Association, 2018, ISSN: 1727-348X.
@inproceedings{sane2018revisiting,
title = {Revisiting the Evaluation of In Situ Lagrangian Analysis},
author = {Sudhanshu Sane and Roxana Bujack and Hank Childs},
editor = {Hank Childs and Fernando Cucchietti},
url = {https://datascience.dsscale.org/wp-content/uploads/2019/01/RevisitingtheEvaluationofInSituLagrangianAnalysis.pdf},
doi = {10.2312/pgv.20181096},
issn = {1727-348X},
year = {2018},
date = {2018-01-01},
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
publisher = {The Eurographics Association},
abstract = {In situ usage of Lagrangian techniques has proven to be superior with respect to emerging supercomputing trends than the traditional Eulerian approach for scientific flow analysis. However, previous studies have not informed two key points: (1) the accuracy of the post hoc interpolated trajectory as a whole and (2) the spatiotemporal tradeoffs involved when using Lagrangian analysis. With this short paper, we address these points. We first conduct a more comprehensive evaluation via additional accuracy metrics tailored for evaluating Lagrangian trajectories. Second, we provide an understanding of the configurations where the Lagrangian approach works well by studying spatiotemporal tradeoffs. In addition, our study highlights the effects of error propagation and accumulation when performing Lagrangian interpolation for large numbers of steps. We believe our study is significant for better understanding the use of in situ Lagrangian techniques, as well as serving as a baseline for future Lagrangian research.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In situ usage of Lagrangian techniques has proven to be superior with respect to emerging supercomputing trends than the traditional Eulerian approach for scientific flow analysis. However, previous studies have not informed two key points: (1) the accuracy of the post hoc interpolated trajectory as a whole and (2) the spatiotemporal tradeoffs involved when using Lagrangian analysis. With this short paper, we address these points. We first conduct a more comprehensive evaluation via additional accuracy metrics tailored for evaluating Lagrangian trajectories. Second, we provide an understanding of the configurations where the Lagrangian approach works well by studying spatiotemporal tradeoffs. In addition, our study highlights the effects of error propagation and accumulation when performing Lagrangian interpolation for large numbers of steps. We believe our study is significant for better understanding the use of in situ Lagrangian techniques, as well as serving as a baseline for future Lagrangian research.