Galaxy evolution in 3 dimensions

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Galaxies are vast collections of billions of stars, and represent the rich tapestry of cosmic history. The clues to a galaxy's formation and evolution are encoded in its light: a unique fingerprint that contains the past ensemble history of its star formation, and can be used to infer numerous physical properties, such as its mass, the rate at which it is making new stars and its chemistry. These, and other, physical characteristics can be measured from the galaxy's spectrum, i.e. by spreading out the light as a function of wavelength, much like in a rainbow. As in many branches of research, large samples of data are required in order to extract statistically meaningful results, define adequate control experiments and minimize stochastic effects. By collecting samples of hundreds of thousands of galaxies, astronomers have made huge progress in their understanding of galaxy formation and evolution in the last decade. Traditionally, these large spectroscopic surveys obtain an integrated spectrum of each galaxy – all of the light from the entire galaxy is added together and a single spectrum is used to infer global properties. However, a revolution is upon us.******In recent years, astronomical spectrographs have been developed that obtain spatially resolved spectra, i.e. hundreds or thousands of spectra are taken simultaneously across the face of each galaxy. It is the equivalent of progressing from a single pixel to a full image, and holds enormous potential. This technique, known as integral field spectroscopy (IFS), takes us beyond a single integrated metric for each galaxy, and allows us to make full maps of star formation, chemistry and mass. Critically, the first large IFS surveys are just coming online. Specifically, the MaNGA survey is obtaining spectroscopic maps for 10,000 galaxies, and all of the data are being made publically available. ******My proposed research will use the rich MaNGA dataset to tackle several pressing questions in contemporary extragalactic astronomy. I will study where and how the interaction between two galaxies causes new stars to be formed, how the products of that star formation are distributed and how the outer gas reservoir around galaxies is affected. The observational data will be complemented by state-of-the-art galaxy simulations that will allow us to test theoretical models and improve our galaxy identification algorithms, and sophisticated machine learning techniques will be developed to optimally process the millions of data points in the MaNGA dataset. Tackling these research questions with a three pronged approach, that combines observations, computer simulations and artificial neural networks, is both unique in Canada and at the international forefront, offering exceptional training opportunities for young scientists.