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20132025
most citedIntroducing the Illustris Project: the evolution of galaxy populations across cosmic time

1.1k citations · 7.1k across the 74 of their papers we have counts for

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Showing 2019Show all

7 papers · 1 filter

astro-ph.GA2019★ 38 cited

Galaxy interactions in IllustrisTNG-100, I: The power and limitations of visual identification

Kelly Blumenthal, Jorge Moreno, Joshua E. Barnes +6

We present a sample of 446 galaxy pairs constructed using the cosmological simulation IllustrisTNG-100 at z = 0, with M = 10-10 M. We produce…

astro-ph.GA2019★ 41 cited

Correlations Between Black Holes and Host Galaxies in the Illustris and IllustrisTNG Simulations

Yuan Li, Melanie Habouzit, Shy Genel +11

We study black hole - host galaxy correlations, and the relation between the over-massiveness (the distance from the average relation) of super-massive black holes (SMBH…

astro-ph.GA2019★ 34 cited

A study of stellar orbit fractions: simulated IllustrisTNG galaxies compared to CALIFA observations

Dandan Xu, Ling Zhu, Robert Grand +16

Motivated by the recently discovered kinematic "Hubble sequence" shown by the stellar orbit-circularity distribution of 260 CALIFA galaxies, we make use of a comparable galaxy samp…

astro-ph.GA2019★ 187 cited

Morphology and star formation in IllustrisTNG: the build-up of spheroids and discs

Sandro Tacchella, Benedikt Diemer, Lars Hernquist +8

Using the IllustrisTNG simulations, we investigate the connection between galaxy morphology and star formation in central galaxies with stellar masses in the range $10^9-10^{11.5}~…

astro-ph.GA2019★ 21 cited

Studying the Physical Properties of Tidal Features I. Extracting Morphological Substructure in CANDELS Observations and VELA Simulations

Kameswara Bharadwaj Mantha, Daniel H. McIntosh, Cody P. Ciaschi +17

The role of major mergers in galaxy evolution remains a key open question. Existing empirical merger identification methods use non-parametric and subjective visual classifications…

astro-ph.GA2019★ 92 cited

The Hubble Sequence at in the IllustrisTNG simulation with deep learning

M. Huertas-Company, V. Rodriguez-Gomez, D. Nelson +7

We analyze the optical morphologies of galaxies in the IllustrisTNG simulation at with a Convolutional Neural Network trained on visual morphologies in the Sloan Digital S…

astro-ph.GA2019★ 120 cited

Atomic and molecular gas in IllustrisTNG galaxies at low redshift

Benedikt Diemer, Adam R. H. Stevens, Claudia del P. Lagos +9

We have recently developed a post-processing framework to estimate the abundance of atomic and molecular hydrogen (HI and H2, respectively) in galaxies in large-volume cosmological…