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Abstract We study the effects of active galactic nuclei (AGN) feedback on the Lyαforest 1D flux power spectrum (P1D). Using theSimbacosmological-hydrodynamic simulations, we examine the impact that adding different AGN feedback modes has on the predicted P1D. We find that, forSimba, the impact of AGN feedback is most dramatic at lower redshifts (z < 1) and that AGN jet feedback plays the most significant role in altering the P1D. The effects of AGN feedback can be seen across a large range of wavenumbers (1.5 × 10⁻³ < k < 10⁻¹s km⁻¹) changing the ionization state of hydrogen in the IGM through heating. AGN feedback can also alter the thermal evolution of the IGM and thermally broaden individual Lyαabsorbers. For theSimbamodel, these effects become observable atz ≲ 1.0. At higher redshifts (z > 2.0), AGN feedback has a 2% effect on the P1D fork < 5 × 10⁻²s km⁻¹and an 8% effect fork > 5 × 10⁻²s km⁻¹. We show that the small-scale effect is reduced when normalizing the simulation to the observed mean flux. On large scales, the effect of AGN feedback appears via a change in the IGM temperature and is thus unlikely to bias cosmological parameters. The strong AGN jets in theSimbasimulation can reproduce thez > 2 Lyαforest. We stress that analyses comparing different AGN feedback models to future higher precision data will be necessary to determine the full extent of this effect. 

Abstract We explore the role of galactic feedback on the low-redshift Lyα(Lyα) forest (z≲ 2) statistics and its potential to alter the thermal state of the intergalactic medium. Using the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) suite, we explore variations of the AGN and stellar feedback models in the IllustrisTNG and Simba subgrid models. We find that both AGN and stellar feedback in Simba play a role in setting the Lyαforest column density distribution function (CDD) and the Doppler width (b-value) distribution. The Simba AGN jet feedback mode is able to efficiently transport energy out to the diffuse IGM, causing changes in the shape and normalization of the CDD and a broadening of theb-value distribution. We find that stellar feedback plays a prominent role in regulating supermassive black hole growth and feedback, highlighting the importance of constraining stellar and AGN feedback simultaneously. In IllustrisTNG, the AGN feedback variations explored in CAMELS do not affect the Lyαforest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-zLyαforest can be sensitive to changes in the ultraviolet background, stellar and black hole feedback, and that AGN jet feedback in particular can have a strong effect on the thermal state of the IGM. 

Abstract Active galactic nuclei (AGNs) feedback models are generally calibrated to reproduce galaxy observables such as the stellar mass function and the bimodality in galaxy colors. We use variations of the AGN feedback implementations in the IllustrisTNG (TNG) andSimbacosmological hydrodynamic simulations to show that the low-redshift Lyαforest can provide constraints on the impact of AGN feedback. We show that TNG overpredicts the number density of absorbers at column densitiesN_HI< 10¹⁴cm⁻²compared to data from the Cosmic Origins Spectrograph (in agreement with previous work), and we demonstrate explicitly that its kinetic feedback mode, which is primarily responsible for galaxy quenching, has a negligible impact on the column density distribution (CDD) of absorbers. In contrast, we show that the fiducialSimbamodel, which includes AGN jet feedback, is the preferred fit to the observed CDD of thez= 0.1 Lyαforest across 5 orders of magnitude in column density. We show that theSimbaresults with jets produce a quantitatively better fit to the observational data than theSimbaresults without jets, even when the ultraviolet background is left as a free parameter. AGN jets inSimbaare high speed, collimated, weakly interacting with the interstellar medium (via brief hydrodynamic decoupling), and heated to the halo virial temperature. Collectively these properties result in stronger long-range impacts on the intergalactic medium when compared to TNG’s kinetic feedback mode, which drives isotropic winds with lower velocities at the galactic radius. Our results suggest that the low-redshift Lyαforest provides plausible evidence for long-range AGN jet feedback. 

ABSTRACT Observations of massive galaxies at low redshift have revealed approximately linear scaling relations between the mass of a supermassive black hole (SMBH) and properties of its host galaxy. How these scaling relations evolve with redshift and whether they extend to lower-mass galaxies, however, remain open questions. Recent galaxy formation simulations predict a delayed, or ‘two-phase,’ growth of SMBHs: slow, highly intermittent BH growth due to repeated gas ejection by stellar feedback in low-mass galaxies, followed by more sustained gas accretion that eventually brings BHs on to the local scaling relations. The predicted two-phase growth implies a steep increase, or ‘kink,’ in BH-galaxy scaling relations at a stellar mass $$\rm {M}_{*}\sim 5\times 10^{10}$$ M⊙. We develop a parametric, semi-analytic model to compare different SMBH growth models against observations of the quasar luminosity function (QLF) at z ∼ 0.5−4. We compare models in which the relation between SMBH mass and galaxy mass is purely linear versus two-phase models. The models are anchored to the observed galaxy stellar mass function, and the BH mass functions at different redshifts are consistently connected by the accretion rates contributing to the QLF. The best fits suggest that two-phase evolution is significantly preferred by the QLF data over a purely linear scaling relation. Moreover, when the model parameters are left free, the two-phase model fits imply a transition mass consistent with that predicted by simulations. Our analysis motivates further observational tests, including measurements of BH masses and active galactic nuclei activity at the low-mass end, which could more directly test two-phase SMBH growth. 

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 .