New Methods for Observational Cosmology and Galactic Archaeology

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Can we use machine intelligence to discover complexities and relationships within huge astronomy data sets to understand cosmic structure and stellar systems?******The enormous quantities of data generated from sky surveys require efficient techniques to distill raw survey data into physical insights. This research proposal aims at developing novel methods to maximize the extraction of physical quantities jointly from multiple sky surveys. We will take advantage of scalable machine intelligence and statistical techniques to analyze multi-wavelength astrophysical data with a focus on galactic archaeology and cosmology.******By assembling spectra of millions of stars, from public sky surveys and state-of-the-art simulations, we will implement smart machines to map our universe. The systems will learn the complexities of the multi-wavelength data with a special emphasis on instrumental and atmospheric contaminants, traditionally difficult to account for. The resulting systems will predict consistent temperatures, metallicities, di and chemical abundances of the stars with probabilistic outputs. We will evolve our systems by including information from wide field imaging surveys, giving the extra capacity to predict mass, age and distances of the stars. Our goal is to build a Milky Way learning machine to study the chemical evolution of stellar systems, and the nucleosynthesis of elements at an unprecedented level.******In parallel we will design smart systems for extragalactic sources. The Euclid consortium is a prime example for such an application: ground and space telescopes collecting millions of images and spectra of the extragalactic sky at optical and infrared wavelengths, to probe the dark sector of the universe. We will evolve our Milky Way machine to learn from galaxy pixels, spatial distributions and spectra together with simulations, adding a capacity to predict extragalactic redshifts and constrain cosmological models.******The program has a strong focus on training graduate students to create and develop novel machine learning techniques in this Golden Era of Big Astronomy Data. Young researchers in Canada supported by this program will acquire strong skills for both academic research and data science in industry.*****