Astronomy Re-envisioned: Investigating the Physics of Galaxy Evolution with Machine Learning
Abstract:
Astronomical imaging of galaxies reveals how they formed and evolved. While spectroscopy is necessary for measuring galaxies' physical properties, such as their cold gas content or metallicity, it is now possible to reliably predict these properties direct from three-color optical image cutouts by using convolutional neural networks (CNNs). Even the entire optical spectrum can be determined purely from galaxy images. We have also found that highly optimized CNNs can robustly identify nearby dwarf galaxies from large-area imaging surveys, resulting in a dramatic increase in the total number of satellite galaxy systems we can study at low redshifts. These applications are prime examples of how deep learning can facilitate new science in galaxy evolution and near-field cosmology. With the upcoming Wide Field Instrument (WFI) aboard the Roman Space Telescope, cutting-edge machine learning techniques will further transform our ability to study the cosmos.
