11 papers
Learning the Universe with the 2nd Generation of CAMELS: Varying 35 parameters of the IllustrisTNG model in (50Mpc/h)^3 boxes
Shy Genel, Yongseok Jo, Boon Kiat Oh +10
We present a new set of 1,192 cosmological simulations as part of the CAMELS project, in which a space of 35 cosmological, astrophysical, and numerical parameters is explored aroun…
Learning the Universe: The Structure of Dust Attenuation Curves in Galaxy Simulations
Laura Sommovigo, Deaglan J. Bartlett, Rachel K. Cochrane +3
Dust attenuation is a major source of systematic uncertainty in both SED fitting and forward modeling of galaxy populations, yet the functional form used to parameterize attenuatio…
CAMELS Environments: The Impact of Local Neighbours on Galaxy Evolution across the SIMBA, IllustrisTNG, ASTRID, and Swift-EAGLE Simulations
Xavier Sims, Daniel Anglés-Alcázar, Boon-Kiat Oh +6
Internal feedback from massive stars and active galactic nuclei (AGN) play a key role in galaxy evolution, but external environmental effects can also strongly influence galaxies.…
Cosmological back-reaction of baryons on dark matter in the CAMELS simulations
Matthew Gebhardt, Daniel Anglés-Alcázar, Shy Genel +13
Baryonic processes such as radiative cooling and feedback from massive stars and active galactic nuclei (AGN) directly redistribute baryons in the Universe but also indirectly redi…
Learning the Universe: Cosmological and Astrophysical Parameter Inference with Galaxy Luminosity Functions and Colours
Christopher C. Lovell, Tjitske Starkenburg, Matthew Ho +9
We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference ap…
Predicting the Subhalo Mass Functions in Simulations from Galaxy Images
Andreas Filipp, Tri Nguyen, Laurence Perreault-Levasseur +5
Strong gravitational lensing provides a powerful tool to directly infer the dark matter (DM) subhalo mass function (SHMF) in lens galaxies. However, comparing observationally infer…