Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.
Title resolution pending
5 Pith papers cite this work, alongside 45 external citations. Polarity classification is still indexing.
5
Pith papers citing it
45
external citations · OpenAlex
representative citing papers
Simulation comparison finds bulgeless galaxies host more centrally concentrated, disc-aligned satellites with steeper faint-end luminosity functions than bulge-dominated controls, reflecting co-evolution and quieter merger histories.
Bulgeless galaxies trace the upper envelope of the mass-R1 relation with scatter driven by central stellar density and the spatial configuration of mergers rather than their number.
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.
citing papers explorer
-
The DREAMS Project: Disentangling the Impact of Halo-to-Halo Variance and Baryonic Feedback on Milky Way Dark Matter Density Profiles
Milky Way-mass dark matter density profiles in IllustrisTNG are largely insensitive to astrophysics and cosmology variations, dominated by halo-to-halo variance instead.
-
Bulgeless Evolution And the Rise of Discs (BEARD) III. A numerical simulation view of satellites around Milky-Way analogues
Simulation comparison finds bulgeless galaxies host more centrally concentrated, disc-aligned satellites with steeper faint-end luminosity functions than bulge-dominated controls, reflecting co-evolution and quieter merger histories.
-
Bulgeless Evolution And the Rise of Discs (BEARD) I. Physical drivers of the mass-size relation for Milky Way-like galaxies
Bulgeless galaxies trace the upper envelope of the mass-R1 relation with scatter driven by central stellar density and the spatial configuration of mergers rather than their number.
-
Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.
- Universal Fitting Formulae for the Peak Concentration of Dark Matter Halos