College of Science & Engineering
The primary goal of this project is the reconstruction of sky-projected density distribution within clusters of galaxies. Most of the mass is composed of dark matter whose distribution cannot be seen directly, so has to be inferred. The inference is done using gravitationally lensed background sources like galaxies. Currently, a typical rich cluster has about 30-100 lensed images. Because this number is relatively small compared to the spatial detail needed for adequate mapping of the clusters, the problem of cluster mass reconstruction is very under-constrained. There are several methods in use that do mass reconstruction, each with its own assumptions and priors. This group's method is based on a genetic algorithm, called GRALE, which is a free-form, adaptive grid method that uses a genetic algorithm to iteratively refine the mass map solution. It efficiently explores the model space, does not get stuck in local minima, and explores the range of mass uncertainties quite differently from other existing methods. The reconstructed maps are very accurate, but the computation time is large, and only possible on supercomputers.
Given maps of reconstructed mass distribution with accompanying uncertainties, they can be used for two main purposes. One is to discover and examine the very first generation of galaxies that formed in the universe. The light travel time to them is nearly the age of the universe, making them faint and difficult to observe. Most of these are so faint that existing and future telescopes are not adequate. To solve the problem astronomers use "nature's telescopes," clusters of galaxies that act as gravitational lenses to amplify distant sources, by typical factors of between 10 and 50. However, these "telescopes" have very uneven "optics," i.e. mass distributions, hence the need for accurate mass reconstruction. The second purpose was as stated above, to map out the mass distribution in clusters with the goal of constraining particle properties of dark matter.
This research was featured on the MSI website in July 2015: Investigations of Dark Matter.