activity
20082026
most citedIntroducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe

2.3k citations · 36.1k across the 179 of their papers we have counts for

collaborators
Showing 2021Show all

12 papers · 1 filter

astro-ph.GA2021★ 1 cited

Weighing the Milky Way and Andromeda with Artificial Intelligence

Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Shy Genel +6

We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on thousands of state-of…

astro-ph.GA2021★ 72 cited

Supermassive black holes in cosmological simulations II: the AGN population and predictions for upcoming X-ray missions

Melanie Habouzit, Rachel S. Somerville, Yuan Li +13

In large-scale hydrodynamical cosmological simulations, the fate of massive galaxies is mainly dictated by the modeling of feedback from active galactic nuclei (AGN). The amount of…

astro-ph.CO2021★ 2 cited

Inferring halo masses with Graph Neural Networks

Pablo Villanueva-Domingo, Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar +7

Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work we build a model that infers the…

astro-ph.GA2021★ 25 cited

Galaxy Formation in the Santa Cruz semi-analytic model compared with IllustrisTNG -- I. Galaxy scaling relations, dispersions, and residuals at z=0

Austen Gabrielpillai, Rachel S. Somerville, Shy Genel +4

We present the first results from applying the Santa Cruz semi-analytic model (SAM) for galaxy formation on merger trees extracted from a dark matter only version of the IllustrisT…

astro-ph.CO2021★ 26 cited

HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows

Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt +11

A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop to…

cs.LG2021★ 69 cited

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…