92 research outputs found
The dependence of assembly bias on the cosmic web
For low-mass haloes, the physical origins of halo assembly bias have been
linked to the slowdown of accretion due to tidal forces, which are expected to
be more dominant in some cosmic-web environments as compared to others. In this
work, we use publicly available data from the application of the Discrete
Persistent Structures Extractor (DisPerSE) to the IllustrisTNG
magnetohydrodynamical simulation to investigate the dependence of the related
galaxy assembly bias effect on the cosmic web. We first show that, at fixed
halo mass, the galaxy population displays significant low-mass secondary bias
when split by distance to DisPerSE critical points representing nodes (), filaments (), and saddles (), with objects
closer to these features being more tightly clustered. The secondary bias
produced by some of these parameters exceeds the assembly bias signal
considerably at some mass ranges, particularly for . We also
demonstrate that the assembly bias signal is reduced significantly when
clustering is conditioned to galaxies being close or far from these critical
points. The maximum attenuation is measured for galaxies close to saddle
points, where less than 35 of the signal remains. Conversely, objects near
voids preserve a fairly pristine effect (almost 85 of the signal). Our
analysis confirms the important role played by the tidal field in shaping
assembly bias, but they are also consistent with the signal being the result of
different physical mechanisms. Our work introduces some new aspects of
secondary bias where the predictions from hydrodynamical simulations can be
directly tested with observational data.Comment: 12 pages, 7 figures. Submitted to MNRAS, comments welcom
The dependence of halo bias on age, concentration and spin
Halo bias is the main link between the matter distribution and dark matter
halos. In its simplest form, halo bias is determined by halo mass, but there
are known additional dependencies on other halo properties which are of
consequence for accurate modeling of galaxy clustering. Here we present the
most precise measurement of these secondary-bias dependencies on halo age,
concentration, and spin, for a wide range of halo masses spanning from
10 to 10 M. At the high-mass end, we find
no strong evidence of assembly bias for masses above M
M. Secondary bias exists, however, for halo concentration
and spin, up to cluster-size halos, in agreement with previous findings. For
halo spin, we report, for the first time, two different regimes: above
M10 M, halos with larger values of spin
have larger bias, at fixed mass, with the effect reaching almost a factor 2.
This trend reverses below this characteristic mass. In addition to these
results, we test, for the first time, the performance of a multi-tracer method
for the determination of the relative bias between different subsets of halos.
We show that this method increases significantly the signal-to-noise of the
secondary-bias measurement as compared to a traditional approach. This analysis
serves as the basis for follow-up applications of our multi-tracer method to
real data.Comment: 11 pages, 6 figures, submitted to MNRA
The galaxy size - halo mass scaling relations and clustering properties of central and satellite galaxies
In this work, we combine size and stellar mass measurements from the Sloan
Digital Sky Server (SDSS) with the group finder algorithm of Rodriguez \&
Merch\'an in order to determine the stellar and halo mass -- size relations of
central and satellite galaxies separately. We show that, while central and
satellite galaxies display similar stellar mass -- size relations, their halo
mass -- size relations differ significantly. As expected, more massive haloes
tend to host larger central galaxies. However, the size of satellite galaxies
depends only slightly on halo virial mass. We show that these results are
compatible with a remarkably simple model in which the size of central and
satellite galaxies scales as the cubic root of their host halo mass, with the
normalization for satellites being 30 \% smaller than that for central
galaxies, which can be attributed to tidal stripping. We further check that our
measurements are in excellent agreement with predictions from the IllustrisTNG
hydrodynamical simulation. In the second part of this paper, we analyse how the
clustering properties of central and satellite galaxies depend on their size.
We demonstrate that, independently of the stellar mass threshold adopted,
smaller galaxies are more tightly clustered than larger galaxies when either
the entire sample or only satellites are considered. The opposite trend is
observed on large scales when the size split is performed for the central
galaxies alone. Our results place significant constraints for halo-galaxy
connection models that link galaxy size with the properties of their hosting
haloes.Comment: 15 pages, 12 figures. Accepted for publication in MNRA
High-fidelity reproduction of central galaxy joint distributions with Neural Networks
The relationship between galaxies and haloes is central to the description of
galaxy formation, and a fundamental step towards extracting precise
cosmological information from galaxy maps. However, this connection involves
several complex processes that are interconnected. Machine Learning methods are
flexible tools that can learn complex correlations between a large number of
features, but are traditionally designed as deterministic estimators. In this
work, we use the IllustrisTNG300-1 simulation and apply neural networks in a
binning classification scheme to predict probability distributions of central
galaxy properties, namely stellar mass, colour, specific star formation rate,
and radius, using as input features the halo mass, concentration, spin, age,
and the overdensity on a scale of 3 Mpc. The model captures the
intrinsic scatter in the relation between halo and galaxy properties, and can
thus be used to quantify the uncertainties related to the stochasticity of the
galaxy properties with respect to the halo properties. In particular, with our
proposed method, one can define and accurately reproduce the properties of the
different galaxy populations in great detail. We demonstrate the power of this
tool by directly comparing traditional single-point estimators and the
predicted joint probability distributions, and also by computing the power
spectrum of a large number of tracers defined on the basis of the predicted
colour-stellar mass diagram. We show that the neural networks reproduce
clustering statistics of the individual galaxy populations with excellent
precision and accuracy.Comment: 12 pages, 7 figure
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