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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 the properties of distant galaxies. Kelson’s observations of their masses, sizes, and morphologies allow him to directly measure their stars’ aging and thus infer their formation history. Kelson is the Principal Investigator of the Carnegie-Spitzer-IMACS Redshift Survey of galaxies back to z=1.5 and he is also a Senior Co-Investigator of the Cluster Lensing and Supernova with Hubble Multi-Cycle Treasury Program with the Hubble Space Telescope.
This spectroscopic survey of faint galaxies probes the largest unbiased volume of galaxies in the Universe as it was 5-10 Gyr ago, targeting galaxies more uniformly and over a wide area of the sky. The survey used 100 nights of Magellan time with the Inamori Magellan Areal Camera and Spectrograph, as well as almost 50 nights of 4m time at the National Optical Astronomical Observatories.
In order to better understand how galaxies have been forming and evolving over cosmic time, and to specifically interpret the data gathered in the Carnegie-Spitzer-IMACS Redshift Survey, Kelson has also begun development of a new theoretical framework. Using modern mathematical theorems about covariant stochastic processes, one can accurately model the evolution of the ensembles of galaxies over time and in groundbreaking ways that will ultimately allow astronomers, for the first time, to empirically decouple the rates of in situ and ex situ mass growth in galaxies.
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 in creating and maintaining the Carnegie Python Distribution 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.
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