SCML Meeting: March 6, 2019
David Reiman, PhD student of Physics, will present on “AstroGANs: Deep Generative Models for Astrophysics and Galaxy Evolution”.
Here is his abstract:
Deep learning has revolutionized big data—from outperforming doctors in skin cancer diagnoses to precisely forecasting earthquake aftershocks. In astronomy, deep discriminative models have been applied with great success to problems like galaxy classification and exoplanet identification. On the other hand, applications of powerful generative models are scarce. Here, we apply generative adversarial networks (GANs), a model composed of two dueling neural networks, to a variety of open problems in galaxy evolution and cosmology, namely: (1) deblending superpositions of distant galaxies to salvage galaxy images captured in the densest regions of the universe by near-future surveys like LSST, (2) super-resolving optical Suprime-Cam galaxy images from the COSMOS field to near Hubble quality to recover useful features for improved study of galaxy morphology and evolution, and (3) inferring the Lyman-alpha emission of high-redshift quasars given their redward spectrum to extract information about the early universe intergalactic medium.