41 citations · 76 across the 2 of their papers we have counts for
5 papers
HIGAN: Cosmic Neutral Hydrogen with Generative Adversarial Networks
Juan Zamudio-Fernandez, Atakan Okan, Francisco Villaescusa-Navarro +5
One of the most promising ways to observe the Universe is by detecting the 21cm emission from cosmic neutral hydrogen (HI) through radio-telescopes. Those observations can shed lig…
From Dark Matter to Galaxies with Convolutional Networks
Xinyue Zhang, Yanfang Wang, Wei Zhang +5
Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Univers…
Learning to Predict the Cosmological Structure Formation
Siyu He, Yin Li, Yu Feng +4
Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features…
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Amrita Mathuriya, Deborah Bard, Peter Mendygral +14
Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow:…
Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III
Yen-Chi Chen, Shirley Ho, Jonathan Blazek +4
Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital S…