CPC Seminar: Insights on galaxy evolution and morphology from deep learning

  • Feb. 1, 2021, 2:00 pm US/Central
  • John Wu, Space Telescope Science Institute

Abstract: The growth of galaxies is regulated by the amount of cold gas available to form stars. In order to constrain galaxy evolution models, it is critical to measure the interstellar gas mass and the abundance of heavy elements (metallicity) in the gas phase for large samples of galaxies. However, these properties are observationally difficult to measure, and galaxies’ cold gas reservoirs are mostly invisible at optical wavelengths. One way to circumvent these challenges is to rely on the morphologies of galaxies, which are linked to their star formation and chemical enrichment histories. I will present deep learning methods for estimating the gas content and metallicity of galaxies from imaging data alone, including an overview of convolutional neural networks and some use cases in the astronomical image domain. I will also discuss novel ways to probe galaxy properties using artificial intelligence and visualization algorithms. Interpretable and accurate deep learning tools will enable us to multiply the scientific returns of wide-field imaging surveys in the coming decade.

For more information, please contact Yu-Dai Tsai at ytsaiATfnal.gov.