340 citations · 1k across the 12 of their papers we have counts for
12 papers
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
Drew Jamieson, Yin Li, Siyu He +4
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approx…
From Dark Matter to Galaxies with Convolutional Neural Networks
Jacky H. T. Yip, Xinyue Zhang, Yanfang Wang +7
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to co…
Learning neutrino effects in Cosmology with Convolutional Neural Networks
Elena Giusarma, Mauricio Reyes Hurtado, Francisco Villaescusa-Navarro +3
Measuring the sum of the three active neutrino masses, , is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on se…
The Quijote simulations
Francisco Villaescusa-Navarro, ChangHoon Hahn, Elena Massara +26
The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the hyperplane.…
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…
The detection of the imprint of filaments on cosmic microwave background lensing
Siyu He, Shadab Alam, Simone Ferraro +2
Galaxy redshift surveys, such as 2dF, SDSS, 6df, GAMA and VIPERS, have shown that the spatial distribution of matter forms a rich web, known as the cosmic web. The majority of gala…
Observational Constraints on First-Star Nucleosynthesis. I. Evidence for Multiple Progenitors of CEMP-no Stars
Jinmi Yoon, Timothy C. Beers, Vinicius M. Placco +6
We investigate anew the distribution of absolute carbon abundance, (C) (C), for carbon-enhanced metal-poor (CEMP) stars in the halo of the Milky Way, based on high-re…
Constraining Gravity at the Largest Scales through CMB Lensing and Galaxy Velocities
Anthony R. Pullen, Shadab Alam, Siyu He +1
We demonstrate a new method to constrain gravity on the largest cosmological scales by combining measurements of cosmic microwave background (CMB) lensing and the galaxy velocity f…