activity
20092026
most citedIntroducing the Illustris Project: Simulating the coevolution of dark and visible matter in the Universe

2.3k citations · 32.7k across the 218 of their papers we have counts for

collaborators
Showing 2018Show all

16 papers · 1 filter

astro-ph.GA2018★ 294 cited

The star-formation activity of IllustrisTNG galaxies: main sequence, UVJ diagram, quenched fractions, and systematics

Martina Donnari, Annalisa Pillepich, Dylan Nelson +6

We select galaxies from the IllustrisTNG hydrodynamical simulations ( at ) and characterize the shapes and evolutions of their UVJ and star-format…

astro-ph.GA2018★ 18 cited

The IllustrisTNG Simulations: Public Data Release

Dylan Nelson, Volker Springel, Annalisa Pillepich +11

We present the full public release of all data from the TNG50, TNG100 and TNG300 simulations of the IllustrisTNG project. IllustrisTNG is a suite of large volume, cosmological, gra…

astro-ph.GA2018★ 147 cited

Jellyfish galaxies with the IllustrisTNG simulations: I. Gas-stripping phenomena in the full cosmological context

Kiyun Yun, Annalisa Pillepich, Elad Zinger +8

We use IllustrisTNG, a suite of gravity and MHD simulations, to study the demographics and properties of jellyfish galaxies in the full cosmological context. By jellyfish galaxies,…

astro-ph.GA2018★ 125 cited

Zooming in on accretion - II. Cold Circumgalactic Gas Simulated with a super-Lagrangian Refinement Scheme

Joshua Suresh, Dylan Nelson, Shy Genel +2

In this study we explore the complex multi-phase gas of the circumgalactic medium (CGM) surrounding galaxies. We propose and implement a novel, super-Lagrangian 'CGM zoom' scheme i…

astro-ph.GA2018★ 106 cited

Atomic hydrogen in IllustrisTNG galaxies: the impact of environment parallelled with local 21-cm surveys

Adam R. H. Stevens, Benedikt Diemer, Claudia del P. Lagos +8

We investigate the influence of environment on the cold-gas properties of galaxies at z=0 within the TNG100 cosmological, magnetohydrodynamic simulation, part of the IllustrisTNG s…

astro-ph.CO2018★ 90 cited

A Deep Learning Approach to Galaxy Cluster X-ray Masses

M. Ntampaka, J. ZuHone, D. Eisenstein +9

We present a machine-learning approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep machine learning tool…