Black hole - galaxy correlations without self-regulation

Recent models of black hole growth in a cosmological context have forwarded a paradigm in which the growth is self-regulated by feedback from the black hole itself. Here we use cosmological zoom simulations of galaxy formation down to z =2 to show that such strong self-regulation is required in the popular spherical Bondi accretion model, but that a plausible alternative model in which black hole growth is limited by galaxy-scale torques does not require self-regulation. Instead, this torque-limited accretion model yields black holes and galaxies evolving on average along the observed scaling relations by relying only on a fixed, 5% mass retention rate onto the black hole from the radius at which the accretion flow is fed. Feedback from the black hole may (and likely does) occur, but does not need to couple to galaxy-scale gas in order to regulate black hole growth. We show that this result is insensitive to variations in the initial black hole mass, stellar feedback, or other implementation details. The torque-limited model allows for high accretion rates at very early epochs (unlike the Bondi case), which if viable can help explain the rapid early growth of black holes, while by z ∼ 2 it yields Eddington factors of ∼1%–10%. This model also yields a less direct correspondence between major merger events and rapid phases of black hole growth. Instead, growth is more closely tied to cosmological disk feeding, which may help explain observational studies showing that, at least at z >~ 1, active galaxies do not preferentially show merger signatures.Web of Scienc

The Radio Galaxy Population in the SIMBA SImulations

We examine the 1.4GHz radio luminosities of galaxies arising from star formation and active galactic nuclei (AGN) 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 AGN (RLAGN) 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 disk, 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 > $\sim 10^{22-23}$ W Hz$^{-1}$, while star formation dominates at lower radio powers. Overall, RLAGN 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 a LERG number density of 8.53 Mpc$^{-3}$, $\sim 10\times$ 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 halos 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.Comment: 17 pages, 9 figures, Accepted for publication in MNRA

Inpainting hydrodynamical maps with deep learning

From 1,000 hydrodynamic simulations of the CAMELS project, each with a different value of the cosmological and astrophysical parameters, we generate 15,000 gas temperature maps. We use a state-of-the-art deep convolutional neural network to recover missing data from those maps. We mimic the missing data by applying regular and irregular binary masks that cover either $15\%$ or $30\%$ of the area of each map. We quantify the reliability of our results using two summary statistics: 1) the distance between the probability density functions (pdf), estimated using the Kolmogorov-Smirnov (KS) test, and 2) the 2D power spectrum. We find an excellent agreement between the model prediction and the unmasked maps when using the power spectrum: better than $1\%$ for $k<20 h/$Mpc for any irregular mask. For regular masks, we observe a systematic offset of $\sim5\%$ when covering $15\%$ of the maps while the results become unreliable when $30\%$ of the data is missing. The observed KS-test p-values favor the null hypothesis that the reconstructed and the ground-truth maps are drawn from the same underlying distribution when irregular masks are used. For regular-shaped masks on the other hand, we find a strong evidence that the two distributions do not match each other. Finally, we use the model, trained on gas temperature maps, to perform inpainting on maps from completely different fields such as gas mass, gas pressure, and electron density and also for gas temperature maps from simulations run with other codes. We find that visually, our model is able to reconstruct the missing pixels from the maps of those fields with great accuracy, although its performance using summary statistics depends strongly on the considered field.Comment: 14 pages, 6 figures, Submitted to AP

The BLAST Survey of the Vela Molecular Cloud: Dynamical Properties of the Dense Cores in Vela-D

The Vela-D region, according to the nomenclature given by Murphy & May (1991), of the star forming complex known as the Vela Molecular Ridge (VMR), has been recently analyzed in details by Olmi et al. (2009), who studied the physical properties of 141 pre- and proto-stellar cold dust cores, detected by the ``Balloon-borne Large-Aperture Submillimeter Telescope'' (BLAST) during a much larger (55 sq. degree) Galactic Plane survey encompassing the whole VMR. This survey's primary goal was to identify the coldest, dense dust cores possibly associated with the earliest phases of star formation. In this work, the dynamical state of the Vela-D cores is analyzed. Comparison to dynamical masses of a sub-sample of the Vela-D cores estimated from the 13CO survey of Elia et al. (2007), is complicated by the fact that the 13CO linewidths are likely to trace the lower density intercore material, in addition to the dense gas associated with the compact cores observed by BLAST. In fact, the total internal pressure of these cores, if estimated using the 13CO linewidths, appears to be higher than the cloud ambient pressure. If this were the case, then self-gravity and surface pressure would be insufficient to bind these cores and an additional source of external confinement (e.g., magnetic field pressure) would be required. However, if one attempts to scale down the 13CO linewidths, according to the observations of high-density tracers in a small sample of sources, then most proto-stellar cores would result effectively gravitationally bound.Comment: This paper has 12 pages and 6 figures. Accepted for publication by the Astrophysical Journal on July 19, 201

Co-evolution of massive black holes and their host galaxies at high redshift: discrepancies from six cosmological simulations and the key role of JWST

The James Webb Space Telescope will have the power to characterize high-redshift quasars at z>6 with an unprecedented depth and spatial resolution. While the brightest quasars at such redshift (i.e., with bolometric luminosity L_bol> 10^46 erg/s) provide us with key information on the most extreme objects in the Universe, measuring the black hole (BH) mass and Eddington ratios of fainter quasars with L_bol= 10^45-10^46 erg/s opens a path to understand the build-up of more normal BHs at z>6. In this paper, we show that the Illustris, TNG100, TNG300, Horizon-AGN, EAGLE, and SIMBA large-scale cosmological simulations do not agree on whether BHs at z>4 are overmassive or undermassive at fixed galaxy stellar mass with respect to the M_BH-M_star scaling relation at z=0 (BH mass offsets). Our conclusions are unchanged when using the local scaling relation produced by each simulation or empirical relations. We find that the BH mass offsets of the simulated faint quasar population at z>4, unlike those of bright quasars, represent the BH mass offsets of the entire BH population, for all the simulations. Thus, a population of faint quasars with L_bol= 10^45-10^46 erg/s observed by JWST can provide key constraints on the assembly of BHs at high redshift. Moreover, this will help constraining the high-redshift regime of cosmological simulations, including BH seeding, early growth, and co-evolution with the host galaxies. Our results also motivate the need for simulations of larger cosmological volumes down to z=6, with the same diversity of sub-grid physics, in order to gain statistics on the most extreme objects at high redshift.Comment: published in MNRAS, 19 pages, 8 figures, key figures: Fig. 3, Fig.5, and Fig.

Cosmology with one galaxy? -- The ASTRID model and robustness

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter $\Omega_{\rm m}$, at fixed $\Omega_{\rm b}$, 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 made use of a distinct subgrid physics: Astrid. We find that also in this case, neural networks are able to infer the value of $\Omega_{\rm m}$ with a $\sim10\%$ precision from the properties of individual galaxies while accounting for astrophysics uncertainties as modeled in CAMELS. This tight relationship is present at all considered redshifts, $z\leq3$, 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.Comment: 16 pages, 12 figure

Predicting the impact of feedback on matter clustering with machine learning in CAMELS

Extracting information from the total matter power spectrum with the precision needed for upcoming cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. We investigate the impact of baryonic physics on matter clustering at $z=0$ using a library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of $(25\,h^{-1}{\rm Mpc})^3$ volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength and sub-grid model implementation methods. We show that baryonic physics affects matter clustering on scales $k \gtrsim 0.4\,h\,\mathrm{Mpc}^{-1}$ and the magnitude of this effect is dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos ($M_{\rm 200c} > 10^{13.5}$\,\Msun) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range $10^{10} \leq M_\mathrm{halo}/$\Msun$< 10^{15}$ can predict the effect of galaxy formation on the matter power spectrum on scales $k = 1.0$--20.0\,$h\,\mathrm{Mpc}^{-1}$