We explore the role of galactic feedback on the low-redshift Ly
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) and
- NSF-PAR ID:
- 10400647
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal Letters
- Volume:
- 945
- Issue:
- 1
- ISSN:
- 2041-8205
- Format(s):
- Medium: X Size: Article No. L17
- Size(s):
- ["Article No. L17"]
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract α (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 Lyα forest 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 Lyα forest, but varying the stellar feedback model does produce subtle changes. Our results imply that the low-z Lyα forest 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. -
more » « less
ABSTRACT We examine the properties of damped Lyman-α absorbers (DLAs) emerging from a single set of cosmological initial conditions in two state-of-the-art cosmological hydrodynamic simulations: simba and technicolor dawn. The former includes star formation and black hole feedback treatments that yield a good match with low-redshift galaxy properties, while the latter uses multifrequency radiative transfer to model an inhomogeneous ultraviolet background (UVB) self-consistently and is calibrated to match the Thomson scattering optical depth, UVB amplitude, and Ly α forest mean transmission at z > 5. Both simulations are in reasonable agreement with the measured stellar mass and star formation rate functions at z ≥ 3, and both reproduce the observed neutral hydrogen cosmological mass density, $\Omega _{\rm H\, \small{I}}(z)$. However, the DLA abundance and metallicity distribution are sensitive to the galactic outflows’ feedback and the UVB amplitude. Adopting a strong UVB and/or slow outflows underproduces the observed DLA abundance, but yields broad agreement with the observed DLA metallicity distribution. By contrast, faster outflows eject metals to larger distances, yielding more metal-rich DLAs whose observational selection may be more sensitive to dust bias. The DLA metallicity distribution in models adopting an H2-regulated star formation recipe includes a tail extending to [M/H] ≪ −3, lower than any DLA observed to date, owing to curtailed star formation in low-metallicity galaxies. Our results show that DLA observations play an important role in constraining key physical ingredients in galaxy formation models, complementing traditional ensemble statistics such as the stellar mass and star formation rate functions.
-
null (Ed.)ABSTRACT We examine the 1.4 GHz radio luminosities of galaxies arising from star formation and active galactic nuclei (AGNs) within the state-of-the-art cosmological hydrodynamic simulation Simba. Simba grows black holes via gravitational torque limited accretion from cold gas and Bondi accretion from hot gas, and employs AGN feedback including jets at low Eddington ratios. We define a population of radio loud AGNs (RLAGNs) based on the presence of ongoing jet feedback. Within RLAGN, we define high and low excitation radio galaxies (HERGs and LERGs) based on their dominant mode of black hole accretion: torque limited accretion representing feeding from a cold disc, or Bondi representing advection-dominated accretion from a hot medium. Simba predicts good agreement with the observed radio luminosity function (RLF) and its evolution, overall as well as separately for HERGs and LERGs. Quiescent galaxies with AGN-dominated radio flux dominate the RLF at $\gtrsim 10^{22-23}$ W Hz−1, while star formation dominates at lower radio powers. Overall, RLAGNs have higher black hole accretion rates and lower star formation rates than non-RLAGN at a given stellar mass or velocity dispersion, but have similar black hole masses. Simba predicts an LERG number density of 8.53 Mpc−3, ∼10× higher than for HERGs, broadly as observed. While LERGs dominate among most massive galaxies with the largest black holes and HERGs dominate at high specific star formation rates, they otherwise largely populate similar-sized dark matter haloes and have similar host galaxy properties. Simba thus predicts that deeper radio surveys will reveal an increasing overlap between the host galaxy demographics of HERGs and LERGs.more » « less
-
Abstract We investigate the metal species associated with the Ly α forest in eBOSS quasar spectra. Metal absorption lines are revealed in stacked spectra from cross-correlating the selected Ly α absorbers in the forest and the flux fluctuation field. Up to 13 metal species are identified as being associated with relatively strong Ly α absorbers (those with flux fluctuations − 1.0 < δ Ly α < − 0.6 and with a neutral hydrogen column density of ∼ 10 15−16 cm −2 ) over the absorber redshift range of 2 < z abs < 4. The column densities of these species decrease toward higher redshift and for weaker Ly α absorbers. From modeling the column densities of various species, we find that the column density pattern suggests contributions from multiple gas components, both in the circumgalactic medium (CGM) and the intergalactic medium (IGM). While the low-ionization species (e.g., C ii , Si ii , and Mg ii ) can be explained by high-density, cool gas ( T ∼ 10 4 K) from the CGM, the high-ionization species may reside in low-density or high-temperature gas in the IGM. The measurements provide inputs for modeling the metal contamination in the Ly α forest baryon acoustic oscillation measurements. Comparisons with metal absorptions in high-resolution quasar spectra and hydrodynamic galaxy formation simulations can further elucidate the physical conditions of these Ly α absorbers.more » « less
-
more » « less
Abstract We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies. CAMELS-ASTRID employs the galaxy formation model following the ASTRID simulation and contains 2124 hydrodynamic simulation runs that vary three cosmological parameters (Ω
m ,σ 8, Ωb ) and four parameters controlling stellar and active galactic nucleus (AGN) feedback. Compared to the existing TNG and SIMBA simulation suites in CAMELS, the fiducial model of ASTRID features the mildest AGN feedback and predicts the least baryonic effect on the matter power spectrum. The training set of ASTRID covers a broader variation in the galaxy populations and the baryonic impact on the matter power spectrum compared to its TNG and SIMBA counterparts, which can make machine-learning models trained on the ASTRID suite exhibit better extrapolation performance when tested on other hydrodynamic simulation sets. We also introduce extension simulation sets in CAMELS that widely explore 28 parameters in the TNG and SIMBA models, demonstrating the enormity of the overall galaxy formation model parameter space and the complex nonlinear interplay between cosmology and astrophysical processes. With the new simulation suites, we show that building robust machine-learning models favors training and testing on the largest possible diversity of galaxy formation models. We also demonstrate that it is possible to train accurate neural networks to infer cosmological parameters using the high-dimensional TNG-SB28 simulation set.
