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Abstract Recent works have discovered a relatively tight correlation between Ω_mand the properties of individual simulated galaxies. Because of this, it has been shown that constraints on Ω_mcan be placed using the properties of individual galaxies while accounting for uncertainties in astrophysical processes such as feedback from supernovae and active galactic nuclei. In this work, we quantify whether using the properties of multiple galaxies simultaneously can tighten those constraints. For this, we train neural networks to perform likelihood-free inference on the value of two cosmological parameters (Ω_mandσ₈) and four astrophysical parameters using the properties of several galaxies from thousands of hydrodynamic simulations of the CAMELS project. We find that using properties of more than one galaxy increases the precision of the Ω_minference. Furthermore, using multiple galaxies enables the inference of other parameters that were poorly constrained with one single galaxy. We show that the same subset of galaxy properties are responsible for the constraints on Ω_mfrom one and multiple galaxies. Finally, we quantify the robustness of the model and find that without identifying the model range of validity, the model does not perform well when tested on galaxies from other galaxy formation models. 

Abstract Recent work has pointed out the potential existence of a tight relation between the cosmological parameter Ω m , at fixed Ω b , 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 Ω m 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 We present the empirical dust attenuation (EDA) framework—a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color–magnitude relations, ( g − r ) − M r and (far-UV −near-UV) − M r , of the simulations to a M r < − 20 and UV complete Sloan Digital Sky Survey galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color–magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation–slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low A V since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations. 

Abstract The 3D geometries of high-redshift galaxies remain poorly understood. We build a differentiable Bayesian model and use Hamiltonian Monte Carlo to efficiently and robustly infer the 3D shapes of star-forming galaxies in James Webb Space Telescope Cosmic Evolution Early Release Science observations with $\log M_{*}/M_{\odot }=9.0–10.5$atz= 0.5–8.0. We reproduce previous results from the Hubble Space Telescope Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey in a fraction of the computing time and constrain the mean ellipticity, triaxiality, size, and covariances with samples as small as ∼50 galaxies. We find high 3D ellipticities for all mass–redshift bins, suggesting oblate (disky) or prolate (elongated) geometries. We break that degeneracy by constraining the mean triaxiality to be ∼1 for $\log M_{*}/M_{\odot }=9.0–9.5$dwarfs atz> 1 (favoring the prolate scenario), with significantly lower triaxialities for higher masses and lower redshifts indicating the emergence of disks. The prolate population traces out a “banana” in the projected $b/a–\log a$diagram with an excess of low-b/a, large- $\log a$galaxies. The dwarf prolate fraction rises from ∼25% atz= 0.5–1.0 to ∼50%–80% atz= 3–8. Our results imply a second kind of disk settling from oval (triaxial) to more circular (axisymmetric) shapes with time. We simultaneously constrain the 3D size–mass relation and its dependence on 3D geometry. High-probability prolate and oblate candidates show remarkably similar Sérsic indices (n∼ 1), nonparametric morphological properties, and specific star formation rates. Both tend to be visually classified as disks or irregular, but edge-on oblate candidates show more dust attenuation. We discuss selection effects, follow-up prospects, and theoretical implications. 

Abstract The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N -body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Ly α spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N -body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at https://camels.readthedocs.io . 

ABSTRACT We present the steps taken to produce a reliable and complete input galaxy catalogue for the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) using the photometric Legacy Survey DR8 DECam. We analyse some of the main issues faced in the selection of targets for the DESI BGS, such as star–galaxy separation, contamination by fragmented stars and bright galaxies. Our pipeline utilizes a new way to select BGS galaxies using Gaia photometry and we implement geometrical and photometric masks that reduce the number of spurious objects. The resulting catalogue is cross-matched with the Galaxy And Mass Assembly (GAMA) survey to assess the completeness of the galaxy catalogue and the performance of the target selection. We also validate the clustering of the sources in our BGS catalogue by comparing with mock catalogues and the Sloan Digital Sky Survey (SDSS) data. Finally, the robustness of the BGS selection criteria is assessed by quantifying the dependence of the target galaxy density on imaging and other properties. The largest systematic correlation we find is a 7 per cent suppression of the target density in regions of high stellar density. 

Abstract Over the next 5 yr, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4 m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage IV dark energy galaxy survey. Atz< 0.6, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the universe during the dark-energy-dominated epoch with redshifts of >10 million galaxies spanning 14,000 deg². In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target anr< 19.5 mag limited sample (BGS Bright), a fainter 19.5 <r< 20.175 color-selected sample (BGS Faint), and a smaller low-zquasar sample. BGS will observe these targets using exposure times scaled to achieve homogeneous completeness and cover the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with simulations to show that BGS can complete its strategy and make optimal use of “bright” time. BGS targets have stellar contamination <1%, and their densities do not depend strongly on imaging properties. BGS Bright will achieve >80% fiber assignment efficiency. Finally, BGS Bright and BGS Faint will achieve >95% redshift success over any observing condition. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise baryon acoustic oscillation and redshift-space distortion measurements atz< 0.4. It presents opportunities for new methods that require highly complete and dense samples (e.g.,N-point statistics, multitracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter.