Search

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter Ωm, at fixed Ωb, and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that makes use of a distinct subgrid physics: Astrid. We also find that in this case, neural networks are able to infer the value of Ωm with a ∼10% precision from the properties of individual galaxies, while accounting for astrophysics uncertainties, as modeled in Cosmology and Astrophysics with MachinE Learning (CAMELS). This tight relationship is present at all considered redshifts, z ≤ 3, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes. [ABSTRACT FROM AUTHOR]

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain 3D positions and radial velocities of ∼1000 galaxies in tiny (25 h − 1 Mpc) 3 volumes our models can infer the value of Ωm with approximately 12% precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and active galactic nucleus feedback, run with five different codes and subgrid models—IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE—we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on 1024 simulations that cover a vast region in parameter space—variations in five cosmological and 23 astrophysical parameters—finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network has likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than ∼10 h −1 kpc. [ABSTRACT FROM AUTHOR]

Significant clustering around the rarest luminous quasars is a feature predicted by dark matter theory combined with number density matching arguments. However, this expectation is not reflected by observations of quasars residing in a diverse range of environments. Here we assess the tension in the diverse clustering of visible i-band dropout galaxies around luminous z ∼ 6 quasars. Our approach uses a simple empirical method to derive the median luminosity–to–halo mass relation, Lc(Mh), for both quasars and galaxies under the assumption of lognormal luminosity scatter, ΣQ and ΣG. We show that higher ΣQ reduces the average halo mass hosting a quasar of a given luminosity, thus introducing at least a partial reversion to the mean in the number count distribution of nearby Lyman-break galaxies. We generate a large sample of mock Hubble Space Telescope fields of view centered across rare z ∼ 6 quasars by resampling pencil beams traced through the dark matter component of the BlueTides cosmological simulation. We find that diverse quasar environments are expected for ΣQ > 0.4, consistent with numerous observations and theoretical studies. However, we note that the average number of galaxies around the central quasar is primarily driven by galaxy evolutionary processes in neighboring halos, as embodied by our parameter ΣG, instead of a difference in the large-scale structure around the central quasar host, embodied by ΣQ. We conclude that models with ΣG > 0.3 are consistent with current observational constraints on high-z quasars, and that such a value is comparable to the scatter estimated from hydrodynamical simulations of galaxy formation. [ABSTRACT FROM AUTHOR]