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SciVis Contest 2015

Kyle Resse Almryde edited this page May 12, 2015 · 1 revision

SciVis Contest 2015 : Visualize the Universe

Overview

Cosmological simulations are a cornerstone of our understanding of the Universe during its 13.7 billion year progression from small fluctuations that we see in the cosmic microwave background to today, where we are surrounded by galaxies and clusters of galaxies interconnected by a vast cosmic web.


Simulations of the formation of structure in the Universe typically simulate dark matter, a collision-less fluid, as a discretized set of particles that interact only gravitationally. Ensuring adequate mass resolution within a simulation requires a large number of particles -- typically on the scale of 1024^3, 2048^3, or even 10240^3 particles in the largest simulations. Developing visualizations for these particles, and perhaps more challengingly for the structures that they form through gravitational interaction and collapse, requires first identifying the structures, developing spatial or informatics representations of the components or the structures themselves, and then correlating these visualizations across time steps.


Typically, structures are identified through a semi-local process known as halo finding, wherein dark matter halos are identified either via local particle density estimation or through simple linking-length mechanisms. Within these halos, which may represent galaxies or clusters of galaxies, substructures are identified -- within a galaxy cluster, smaller halos may be identified which correspond to the location of galaxies. As these structures and substructures interact, merge, separate and grow, the structure of the Universe grows and changes with it. Visualizing the transitions that simulated halos undergo during the lifetime of the Universe can provide necessary inputs to understanding observations from next generation telescopes.


The Data

There are three primary types of data that will be utilized in this years contest.

Raw Particle data

The raw particle data is described by the following features

  1. a position vector
  2. velocity vector
  3. unique particle identifier.
  4. Approximately 100 snapshots in time

Each Temporal snapshots is stored in a single file in a format called SDF. This format is composed of a human readable ASCII header followed by raw binary data.


Data Dimensions

Dimension Bounds

X -45417.3867188, 45417.4101562 Y -45417.3828125, 45417.3945312 Z -45417.4140625, 45417.2773438


Halo Catalog

Defines a database that groups sets of gravitationally bound particles together into coherent structures. It describes the following information about a Halo's structure, including:

  1. Position
  2. Shape
  3. Size

Additionally the following statistics are derived from the particle distribution:

  1. Angular momentum
  2. Relative concentration of the particles
  3. and many more.

These catalogs are stored in both ASCII and binary formats.


Merger Tree database

The final dataset type links the individual halo catalogs that each represent a snapshot in time, thereby creating a Merger Tree database. These merger tree datasets form a sparse graph that can then be analyzed to use quantities such as halo mass accretion and merger history to inform how galaxies form and evolve through cosmic time. Merger tree databases are also distributed in both ASCII and BINARY formats.

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