Dust grain size distributions evolve from large-grain dominated at high redshift to MRN-like at low redshift, driven primarily by shattering and ISM accretion after stars supply initial large grains, reproducing z=0 dust masses and Milky Way extinction properties.
W., et al., 2025, @doi [ ] 10.1093/mnras/staf2040 , https://ui.adsabs.harvard.edu/abs/2025MNRAS.tmp.1935T
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Cosmological zoom-in simulations find that grain-grain shattering in diffuse ISM gas drives rising PAH mass fraction with time, naturally producing the observed PAH-metallicity relation and inverse qPAH-molecular gas trends.
Four parameters suffice to describe dust attenuation curve diversity in TNG simulations, yielding a new symbolic-regression model that recovers curves and fluxes better than existing parameterizations while linking parameters to SFR surface density, metallicity, and geometry.
COLIBRE simulations find the galaxy gas-phase MZR already in place at z≈10 with little evolution until z≈5, then shallowens at low z, with high-mass turnover set by AGN feedback and low-mass end by core-collapse supernovae.
New hydrodynamical simulations show that dwarf galaxy stellar mass-halo mass relations and star formation histories are more influenced by host halo concentration than by the 5 cMpc scale environment.
COLIBRE simulations underpredict bright-end UV galaxy luminosities by 1 to 2.5 magnitudes at z=7-15 compared with observations, with the discrepancy persisting after dust attenuation and uncertainty accounting.
COLIBRE simulations with SKIRT post-processing match observed galaxy luminosity functions from FUV to submm at z=0, except underpredicting bright mid-IR galaxies.
COLIBRE simulations find kinematic galaxy morphology peaks in rotational support at stellar masses of 1-2 x 10^10 solar masses and correlates more with internal properties like gas richness than with host halo properties.
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Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
Four parameters suffice to describe dust attenuation curve diversity in TNG simulations, yielding a new symbolic-regression model that recovers curves and fluxes better than existing parameterizations while linking parameters to SFR surface density, metallicity, and geometry.