Below is a little animation that I made yesterday that shows what I have been doing lately. The green dots correspond to the positions of dark matter particles, while the little yellow squares are the locations of dark matter haloes that I’m interested in. The white lines are the edges of the simulation volume, and you can see the axes triad in the bottom-left corner. To give you an idea of the scale of what’s depicted here, each edge of the cube is 50 Mpc, or 163 million light years. Our entire galaxy is only about 20 kpc or 65,000 light years across.
In the first part of the movie, the cube is rotated about its center. Next, while looking along the z-axis, the volume of the cube plotted is reduced until only 1/10th of the cube is shown. Then, this 1/10th thickness is scanned through the entire cube, and then the volume plotted is replenished back to full.
The things to notice are how the dark matter form areas of high density, which are connected by filaments. Between these are areas of relatively few particles, which are called voids. This is how our universe really looks, with huge collections of galaxies clustered together, separated by huge expanses of nearly empty space. I should point out that there are many more galaxy haloes in this box besides the yellow boxes.
What I was looking for was one of those yellow boxes which is fairly isolated from areas of high density dark matter. I picked one, and now I am doing this same simulation again, but with higher resolution boxes centered on the area of interest. The simulations I’m doing right now are fairly cheap (in computer time currency). I want to be sure that when I run big, time-intensive simulations in the future that I’ve picked a good area to focus my attention on.
I did this visualization with Visit, a stereo, 4D visualization tool out of the Livermore National Lab. The ‘stereo’ means that it can create two images of the same data that are slightly offset, which create a 3D effect if viewed correctly. The fourth dimension is for time, as it can handle time-ordered data sets. I then used Visit’s Python scripting features to output 800 individual PNGs, which I then stitched together (exactly like my time lapse movies) to make this movie.