20 research outputs found
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 > W Hz, 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, 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 or
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 for
Mpc for any irregular mask. For regular masks, we observe a systematic
offset of when covering of the maps while the results become
unreliable when 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 , at fixed ,
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 with a
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, , 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 using
a library of power spectra from the Cosmology and Astrophysics with MachinE
Learning Simulations (CAMELS) project, containing thousands of 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 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 (\,\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 \Msun can predict the
effect of galaxy formation on the matter power spectrum on scales --20.0\,