From Dark Matter to Galaxies with Convolutional Networks Xinyue Zhang*, Yanfang Wang*, Wei Zhang*, Siyu He Yueqiu Sun*∗ Department of Physics, Carnegie Mellon University Center for Data Science, New York University Center for Computational Astrophysics, Flatiron Institute xz2139,yw1007,wz1218,
[email protected] [email protected] Gabriella Contardo, Francisco Shirley Ho Villaescusa-Navarro Center for Computational Astrophysics, Flatiron Institute Center for Computational Astrophysics, Flatiron Institute Department of Astrophysical Sciences, Princeton gcontardo,
[email protected] University Department of Physics, Carnegie Mellon University
[email protected] ABSTRACT 1 INTRODUCTION Cosmological surveys aim at answering fundamental questions Cosmology focuses on studying the origin and evolution of our about our Universe, including the nature of dark matter or the rea- Universe, from the Big Bang to today and its future. One of the holy son of unexpected accelerated expansion of the Universe. In order grails of cosmology is to understand and define the physical rules to answer these questions, two important ingredients are needed: and parameters that led to our actual Universe. Astronomers survey 1) data from observations and 2) a theoretical model that allows fast large volumes of the Universe [10, 12, 17, 32] and employ a large comparison between observation and theory. Most of the cosmolog- ensemble of computer simulations to compare with the observed ical surveys observe galaxies, which are very difficult to model theo- data in order to extract the full information of our own Universe. retically due to the complicated physics involved in their formation The constant improvement of computational power has allowed and evolution; modeling realistic galaxies over cosmological vol- cosmologists to pursue elucidating the fundamental parameters umes requires running computationally expensive hydrodynamic and laws of the Universe by relying on simulations as their theory simulations that can cost millions of CPU hours.