Multiple Uncertainties in Time-Variant Cosmological Particle Data

Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables

Interactive Visual Analytics for Large-scale Particle Simulations

Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters. The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization

Doctor of Philosophy

dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets

Felix:A Topology Based Framework for Visual Exploration of Cosmic Filaments

The large-scale structure of the universe is comprised of virialized blob-like clusters, linear filaments, sheet-like walls and huge near empty three-dimensional voids. Characterizing the large scale universe is essential to our understanding of the formation and evolution of galaxies. The density range of clusters, walls and voids are relatively well separated, when compared to filaments, which span a relatively larger range. The large scale filamentary network thus forms an intricate part of the cosmic web. In this paper, we describe Felix, a topology based framework for visual exploration of filaments in the cosmic web. The filamentary structure is represented by the ascending manifold geometry of the 2-saddles in the Morse-Smale complex of the density field. We generate a hierarchy of Morse-Smale complexes and query for filaments based on the density ranges at the end points of the filaments. The query is processed efficiently over the entire hierarchical Morse-Smale complex, allowing for interactive visualization. We apply Felix to computer simulations based on the heuristic Voronoi kinematic model and the standard LCDM cosmology, and demonstrate its usefulness through two case studies. First, we extract cosmic filaments within and across cluster like regions in Voronoi kinematic simulation datasets. We demonstrate that we produce similar results to existing structure finders. Second, we extract different classes of filaments based on their density characteristics from the LCDM simulation datasets. Filaments that form the spine of the cosmic web, which exist in high density regions in the current epoch, are isolated using Felix. Also, filaments present in void-like regions are isolated and visualized. These filamentary structures are often over shadowed by higher density range filaments and are not easily characterizable and extractable using other filament extraction methodologies

DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment

Hybrid virtual reality environments allow analysts to choose how much of the screen real estate they want to use for Virtual Reality (VR) immersion, and how much they want to use for displaying different types of 2D data. We present the use-based design and evaluation of an immersive visual analytics application for cosmological data that uses such a 2D/3D hybrid environment. The applications is a first-in-kind immersive instantiation of the Activity-Centered-Design theoretical paradigm, as well as a first documented immersive instantiation of a details-first paradigm based on scientific workflow theory. Based on a rigorous analysis of the user activities and on a details-first paradigm, the application was designed to allow multiple domain experts to interactively analyze visual representations of spatial (3D) and nonspatial (2D) cosmology data pertaining to dark matter formation. These hybrid data are represented at multiple spatiotemporal scales as time-aligned merger trees, pixel-based heatmaps, GPU-accelerated point clouds and geometric primitives, which can further be animated according to simulation data and played back for analysis. We have demonstrated this multi-scale application to several groups of lay users and domain experts, as well as to two senior domain experts from the Adler Planetarium, who have significant experience in immersive environments. Their collective feedback shows that this hybrid, immersive application can assist researchers in the interactive visual analysis of large-scale cosmological simulation data while overcoming navigation limitations of desktop visualizations

The Millennium Run Observatory: First Light

Simulations of galaxy evolution aim to capture our current understanding as well as to make predictions for testing by future experiments. Simulations and observations are often compared in an indirect fashion: physical quantities are estimated from the data and compared to models. However, many applications can benefit from a more direct approach, where the observing process is also simulated and the models are seen fully from the observer's perspective. To facilitate this, we have developed the Millennium Run Observatory (MRObs), a theoretical virtual observatory which uses virtual telescopes to `observe' semi-analytic galaxy formation models based on the suite of Millennium Run dark matter simulations. The MRObs produces data that can be processed and analyzed using the standard software packages developed for real observations. At present, we produce images in forty filters from the rest-frame UV to IR for two stellar population synthesis models, three different models of IGM absorption, and two cosmologies (WMAP1/7). Galaxy distributions for a large number of mock lightcones can be `observed' using models of major ground- and space-based telescopes. The data include lightcone catalogues linked to structural properties of galaxies, pre-observation model images, mock telescope images, and Source Extractor products that can all be traced back to the higher level dark matter, semi-analytic galaxy, and lightcone catalogues available in the Millennium database. Here, we describe our methods and announce a first public release of simulated surveys (e.g., SDSS, CFHT-LS, GOODS, GOODS/ERS, CANDELS, and HUDF). The MRObs browser, an online tool, further facilitates exploration of the simulated data. We demonstrate the benefits of a direct approach through a number of example applications (galaxy number counts in CANDELS, clusters, morphologies, and dropout selections).Comment: MNRAS, in press. Millennium Run Observatory data products, online tools, and more available through http://galformod.mpa-garching.mpg.de/mrobs

Incorporating interactive 3-dimensional graphics in astronomy research papers

Most research data collections created or used by astronomers are intrinsically multi-dimensional. In contrast, all visual representations of data presented within research papers are exclusively 2-dimensional. We present a resolution of this dichotomy that uses a novel technique for embedding 3-dimensional (3-d) visualisations of astronomy data sets in electronic-format research papers. Our technique uses the latest Adobe Portable Document Format extensions together with a new version of the S2PLOT programming library. The 3-d models can be easily rotated and explored by the reader and, in some cases, modified. We demonstrate example applications of this technique including: 3-d figures exhibiting subtle structure in redshift catalogues, colour-magnitude diagrams and halo merger trees; 3-d isosurface and volume renderings of cosmological simulations; and 3-d models of instructional diagrams and instrument designs.Comment: 18 pages, 7 figures, submitted to New Astronomy. For paper with 3-dimensional embedded figures, see http://astronomy.swin.edu.au/s2plot/3dpd

Incorporating interactive 3-dimensional graphics in astronomy research papers

Most research data collections created or used by astronomers are intrinsically multi-dimensional. In contrast, all visual representations of data presented within research papers are exclusively 2-dimensional. We present a resolution of this dichotomy that uses a novel technique for embedding 3-dimensional (3-d) visualisations of astronomy data sets in electronic-format research papers. Our technique uses the latest Adobe Portable Document Format extensions together with a new version of the S2PLOT programming library. The 3-d models can be easily rotated and explored by the reader and, in some cases, modified. We demonstrate example applications of this technique including: 3-d figures exhibiting subtle structure in redshift catalogues, colour-magnitude diagrams and halo merger trees; 3-d isosurface and volume renderings of cosmological simulations; and 3-d models of instructional diagrams and instrument designs.Comment: 18 pages, 7 figures, submitted to New Astronomy. For paper with 3-dimensional embedded figures, see http://astronomy.swin.edu.au/s2plot/3dpd