Cosmic Data Visualization

1. Visualizing the Life and Anatomy of
Cosmic Particles
● Subhashis Hazarika
● Rajaditya Mukherjee

2. Target Task
● T2:Halo Identification and Visualization
– Visualize the evolution of halos overtime
– Visualize the evolution of a specific halo of interest(i.e halo with highest
mass)
– Visualize the evolution of the internal structure of the halo of interest.
– Analyze the mass accrual pattern of the highest mass halo.
● T3:Diving Deep into Halo Substructures
– Provide a grid based representation scheme of the dark matter particles.
– Provide a Particle Based Volume Rendering framework to visualize the
particle layout in the dataset.

3. Evolution of Halo over time

4. Evolution of a Halo of Interest (largest mass)

5. Halo substructure

6. Halo Mass Accrual History
● The mass of a halo is estimated using a theorem called “Virial Theorem”
which is not a true estimator of mass[1]. Below is a plot showing how this
virial mass of the largest halo accumulates over time. Also shown is a plot of
the contribution of the dark matter particles in the mass of the halo.
● [1]: http://spiff.rit.edu/classes/phys440/lectures/gal_clus/gal_clus.html

7. Nearest Neighbor Particle Density Estimation
● Determining Grid Resolution: The number of grid per unit cell(cpud) is given by
● Particle Insertion: we create a 4D vector (i.e, one for every grid cell) and store all
ID's of the points associated with a particular grid location.
● Interpolation: We perform an inverse-distance based interpolation at each grid
vertex. Controlling parameters are the radius of the neighborhood and the
maximum number of contributing particles.
● Use Case: Once we have the grid we can use it for isosurface extraction and
direct volume rendering.

8. Nearest Neighbor Particle Density Estimation

9. Particle Based Volume Rendering
● Particle Generation: Traditional approach generate particles per cell by
sampling based on grid point. But here we already have a set of particle
positions. So we only have to map the particles to the individual cells,
achieved by indexing the particles to individual cells.
● Particle Projection: This involves projection from the object space to the
image plane and then designing a proper transfer function
● Spatial Superimposing: Use z-buffer to decide the particle closest to the
image plane and decide the color for a pixel. To add translucency we divide
the pixel to sub-pixels and do a weighted average to find the final value.

10. Particle Based Volume Rendering

11. Particle Based Volume Rendering

12. Thank You