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ABSTRACT Galaxy environment plays an important role in driving the transformation of galaxies from blue and star forming to red and quenched. Recent works have focused on the role of cosmic web filaments in galaxy evolution and have suggested that stellar mass segregation, quenching of star formation, and gas-stripping may occur within filaments. We study the relationship between distance to filament and the stellar mass, colour, and H i gas content of galaxies using data from the REsolved Spectroscopy of a Local VolumE survey and Environmental COntext (ECO) catalogue, two overlapping census-style, volume-complete surveys. We use the Discrete Persistence Structures Extractor to identify cosmic web filaments over the full ECO area. We find that galaxies close to filaments have higher stellar masses, in agreement with previous results. Controlling for stellar mass, we find that galaxies also have redder colours and are more gas poor closer to filaments. When accounting for group membership and halo mass, we find that these trends in colour and gas content are dominated by the increasing prevalence of galaxy group environments close to filaments, particularly for high-halo mass and low-stellar mass galaxies. Filaments have an additional small effect on the gas content of galaxies in low-mass haloes, possibly due to cosmic web stripping. 

ABSTRACT We study specific star formation rate (sSFR) and gas profiles of star-forming (SF) and green valley (GV) galaxies in the simba cosmological hydrodynamic simulation. SF galaxy half-light radii (Rhalf) at z = 0 and their evolution (∝(1 + z)−0.78) agree with observations. Passive galaxy Rhalf agree with observations at high redshift, but by z = 0 are too large, owing to numerical heating. We compare simbaz = 0 sSFR radial profiles for SF and GV galaxies to observations. simba shows strong central depressions in star formation rate (SFR), sSFR, and gas fraction in GV galaxies and massive SF systems, qualitatively as observed, owing to black hole X-ray feedback, which pushes central gas outwards. Turning off X-ray feedback leads to centrally peaked sSFR profiles as in other simulations. In conflict with observations, simba yields GV galaxies with strongly dropping sSFR profiles beyond ≳Rhalf, regardless of active galactic nucleus feedback. The central depression owes to lowering molecular gas content; the drop in the outskirts owes to reduced star formation efficiency. simba’s satellites have higher central sSFR and lower outskirts sSFR than centrals, in qualitative agreement with observations. At z = 2, simba does not show central depressions in massive SF galaxies, suggesting simba’s X-ray feedback should be more active at high-z. High-resolution tests indicate central sSFR suppression is not sensitive to numerical resolution. Reproducing the central sSFR depression in z = 0 GV galaxies represents a unique success of simba. The remaining discrepancies highlight the importance of SFR and gas profiles in constraining quenching mechanisms. 

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 .