Daniel Kelson
The latest generation of large-aperture telescopes and advanced spectrographs allow astronomers to accurately measure properties of enormous numbers of distant galaxies. Daniel Kelson uses the Magellan 6.5-meter telescopes and high-resolution imaging from the Hubble Space Telescope to study galaxies in distant clusters—the most massive, dynamically bound systems in the universe. These clusters are so distant that they exist when the universe was only 7 billion years old. Kelson’s observations of their masses, sizes, and morphologies allow him to directly measure their stars’ aging and thus infer their formation history. His detailed measurements of the spectral properties of these objects also provide strong evidence about their chemical abundances and history of chemical enrichment. Kelson’s observational results are at odds with semi-analytical cosmological models that suggest galaxy morphologies and stellar content evolve together and in direct response to the merging and relaxation of their dark matter halos.
Because dark matter comprises most of the mass in the universe, its distribution has strong consequences for the evolution of structure, intergalactic gas, and other features of galaxies. The specific question of how dark matter is distributed in the cores of rich galactic clusters is a critical component to theories of cosmology and the evolution of structure. High-resolution cosmological simulations make strong predictions for the distribution of dark matter; however, current observational data are weak. Using the Magellan and Keck telescopes, Kelson is measuring the kinematics in the inner regions of massive clusters and determining their mass profiles. The results from these studies are also at odds with predictions from simulations.
Because Kelson's research involves making precision measurements from large quantities of data, he is keenly interested in modern numerical methods and techniques for automating processes for reducing raw data to measured, physical quantities. His work currently enables large imaging and spectroscopic datasets to be reduced with little or no human intervention, improving the efficiency of astronomers at Carnegie and around the world.
