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Rutgers University Astrophysics Colloquium
Probing Dark Energy and Dark Matter with Distant Galaxies
The spatial clustering of distant galaxies is a powerful cosmological probe. As the leading example of Big Data in astrophysics, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce over 50 petabytes of images, yielding a catalog of billions of distant galaxies. Using this catalog, the LSST Dark Energy Science Collaboration (DESC) seeks to determine the evolution of the dark energy equation-of-state, search for modifications to General Relativity, measure the masses of cosmological neutrinos, and test the Cosmological Principle of homogeneity and isotropy. I will describe a new method that uses Machine Learning to increase the discriminating power of these measurements via optimal tomographic binning of galaxy samples.
Improving our understanding of galaxies allows us to mitigate systematic errors when studying cosmology and teaches us about the formation of our own Milky Way. I will present a novel approach using Gaussian Processes to reconstruct galaxy star formation histories from ultraviolet-through-near-infrared photometry. This method has revealed evidence for synchronized star formation in dwarf galaxies located within 10 million light-years of the Milky Way.