71 research outputs found
Torque-Limited Growth of Massive Black Holes in Galaxies Across Cosmic Time
We combine cosmological hydrodynamic simulations with analytic models to
evaluate the role of galaxy-scale gravitational torques on the evolution of
massive black holes at the centers of star-forming galaxies. We confirm and
extend our earlier results to show that torque-limited growth yields black
holes and host galaxies evolving on average along the Mbh-Mbulge relation from
early times down to z = 0 and that convergence onto the scaling relation occurs
independent of the initial conditions and with no need for mass averaging
through mergers or additional self-regulation processes. Smooth accretion
dominates the long-term evolution, with black hole mergers with mass ratios
>1:5 representing typically a small fraction of the total growth. Winds from
the accretion disk are required to eject significant mass to suppress black
hole growth, but there is no need for coupling this wind to galactic-scale gas
to regulate black holes in a non-linear feedback loop. Torque-limited growth
yields a close-to-linear relation for the star formation rate and the black
hole accretion rate averaged over galaxy evolution time scales. However, the
SFR-AGN connection has significant scatter owing to strong variability of black
hole accretion at all resolved time scales. Eddington ratios can be described
by a broad lognormal distribution with median value evolving roughly as (1 +
z)^1.9, suggesting a main sequence for black hole growth similar to the cosmic
evolution of specific SFRs. Our results offer an attractive scenario consistent
with available observations in which cosmological gas infall and transport of
angular momentum in the galaxy by gravitational instabilities regulate the
long-term co-evolution of black holes and star-forming galaxies.Comment: 26 pages, 15 figures, replaced by published versio
Disappearing galaxies: the orientation dependence of JWST-bright, HST-dark, star-forming galaxy selection
Galaxies that are invisible in deep optical-NIR imaging but detected at
longer wavelengths have been the focus of several recent observational studies,
with speculation that they could constitute a substantial missing population
and even dominate the cosmic star formation rate density at . The
depths now achievable with JWST at the longest wavelengths probed by HST,
coupled with the transformative resolution at longer wavelengths, are already
enabling detailed, spatially-resolved characterisation of sources that were
invisible to HST, often known as `HST-dark' galaxies. However, until now, there
has been little theoretical work to compare against. We present the first
simulation-based study of this population, using highly-resolved galaxies from
the Feedback in Realistic Environments (FIRE) project, with multi-wavelength
images along several lines of sight forward-modelled using radiative transfer.
We naturally recover a population of modelled sources that meet commonly-used
selection criteria ( and
). These simulated HST-dark galaxies lie at high
redshifts (), have high levels of dust attenuation (), and
display compact recent star formation
(). Orientation is very
important: for all but one of the 17 simulated galaxy snapshots with HST-dark
sightlines, there exist other sightlines that do not meet the criteria. This
result has important implications for comparisons between observations and
models that do not resolve the detailed star-dust geometry, such as
semi-analytic models or coarsely-resolved hydrodynamical simulations.
Critically, we demonstrate that HST-dark sources are not an unexpected or
exotic population, but a subset of high-redshift, highly-dust-attenuated
sources viewed along certain lines of sight.Comment: 12 pages, 8 figures. Accepted for publication in Ap
An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks
The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength
surveys covering broad swaths of the sky. With multiple large datasets becoming
available in the near future, we develop a likelihood-free Deep Learning
technique using convolutional neural networks (CNNs) to infer broad-scale
physical properties of a galaxy's CGM and its halo mass for the first time.
Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations)
data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft
X-ray and 21-cm (HI) radio 2D maps to trace hot and cool gas, respectively,
around galaxies, groups, and clusters. Our CNNs offer the unique ability to
train and test on ''multifield'' datasets comprised of both HI and X-ray maps,
providing complementary information about physical CGM properties and improved
inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful
enough to infer individual halos with masses . The multifield improves the inference for all halo masses. Generally,
the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer
CGM properties. Cross-simulation analysis -- training on one galaxy formation
model and testing on another -- highlights the challenges of developing CNNs
trained on a single model to marginalize over astrophysical uncertainties and
perform robust inferences on real data. The next crucial step in improving the
resulting inferences on physical CGM properties hinges on our ability to
interpret these deep-learning models
Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation
We investigate how the recovery of galaxy star formation rates (SFRs) using
energy-balance spectral energy distribution (SED) fitting codes depends on
their recent star formation histories (SFHs). We use the Magphys and Prospector
codes to fit 6,706 synthetic spectral energy distributions (SEDs) of simulated
massive galaxies at from the Feedback in Realistic Environments
(FIRE) project. We identify a previously unknown systematic error in the
Magphys results due to bursty star formation: the SFR estimates of individual
galaxies can differ from the true values by as much as 1 dex, at large
statistical significance (), depending on the details of their recent
SFH. The SFRs inferred using Prospector do not exhibit this trend, likely
because unlike Magphys, Prospector uses non-parametric SFHs. We urge caution
when using Magphys, or other codes assuming parametric SFHs, to study galaxies
where the average SFR may have changed significantly over the last 100
Myr, such as those which have recently quenched their star formation or those
experiencing an ongoing burst. This concern is especially relevant, for
example, when fitting JWST observations of very high-redshift galaxies.Comment: 5 pages, 3 figures, submitted to MNRAS Letter
Beware the recent past: a bias in spectral energy distribution modelling due to bursty star formation
© 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We investigate how the recovery of galaxy star formation rates (SFRs) using energy-balance spectral energy distribution (SED) fitting codes depends on their recent star formation histories (SFHs). We use the Magphys and Prospector codes to fit 6,706 synthetic spectral energy distributions of simulated massive galaxies at ), depending on the details of their recent SFH. SFRs inferred using Prospector with non-parametric SFHs do not exhibit this trend. We show that using parametric SFHs (pSFHs) causes SFR uncertainties to be underestimated by a factor of up to . Although this undoubtedly contributes to the significance of the systematic, it cannot explain the largest biases in the SFRs of the starbursting galaxies, which could be caused by details of the stochastic prior sampling or the burst implementation in the Magphys libraries. We advise against using pSFHs and urge careful consideration of starbursts when SED modelling galaxies where the SFR may have changed significantly over the last ~100 Myr, such as recently quenched galaxies, or those experiencing a burst. This concern is especially relevant, e.g. when fitting JWST observations of very high-redshift galaxies.Peer reviewe
Constraining cosmology with machine learning and galaxy clustering: the CAMELS-SAM suite
As the next generation of large galaxy surveys come online, it is becoming
increasingly important to develop and understand the machine learning tools
that analyze big astronomical data. Neural networks are powerful and capable of
probing deep patterns in data, but must be trained carefully on large and
representative data sets. We developed and generated a new `hump' of the
Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project:
CAMELS-SAM, encompassing one thousand dark-matter only simulations of (100
cMpc) with different cosmological parameters ( and
) and run through the Santa Cruz semi-analytic model for galaxy
formation over a broad range of astrophysical parameters. As a proof-of-concept
for the power of this vast suite of simulated galaxies in a large volume and
broad parameter space, we probe the power of simple clustering summary
statistics to marginalize over astrophysics and constrain cosmology using
neural networks. We use the two-point correlation function, count-in-cells, and
the Void Probability Function, and probe non-linear and linear scales across
R cMpc. Our cosmological constraints cluster around
3-8 error on and , and we explore the effect
of various galaxy selections, galaxy sampling, and choice of clustering
statistics on these constraints. We additionally explore how these clustering
statistics constrain and inform key stellar and galactic feedback parameters in
the Santa Cruz SAM. CAMELS-SAM has been publicly released alongside the rest of
CAMELS, and offers great potential to many applications of machine learning in
astrophysics: https://camels-sam.readthedocs.io.Comment: 40 pages, 22 figures (11 made of subfigures
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