277 research outputs found
Galaxy size trends as a consequence of cosmology
We show that recently documented trends in galaxy sizes with mass and
redshift can be understood in terms of the influence of underlying cosmic
evolution; a holistic view which is complimentary to interpretations involving
the accumulation of discreet evolutionary processes acting on individual
objects. Using standard cosmology theory, supported with results from the
Millennium simulations, we derive expected size trends for collapsed cosmic
structures, emphasising the important distinction between these trends and the
assembly paths of individual regions. We then argue that the observed variation
in the stellar mass content of these structures can be understood to first
order in terms of natural limitations of cooling and feedback. But whilst these
relative masses vary by orders of magnitude, galaxy and host radii have been
found to correlate linearly. We explain how these two aspects will lead to
galaxy sizes that closely follow observed trends and their evolution, comparing
directly with the COSMOS and SDSS surveys. Thus we conclude that the observed
minimum radius for galaxies, the evolving trend in size as a function of mass
for intermediate systems, and the observed increase in the sizes of massive
galaxies, may all be considered an emergent consequence of the cosmic
expansion.Comment: 14 pages, 13 figures. Accepted by MNRA
Soft clustering analysis of galaxy morphologies: A worked example with SDSS
Context: The huge and still rapidly growing amount of galaxies in modern sky
surveys raises the need of an automated and objective classification method.
Unsupervised learning algorithms are of particular interest, since they
discover classes automatically. Aims: We briefly discuss the pitfalls of
oversimplified classification methods and outline an alternative approach
called "clustering analysis". Methods: We categorise different classification
methods according to their capabilities. Based on this categorisation, we
present a probabilistic classification algorithm that automatically detects the
optimal classes preferred by the data. We explore the reliability of this
algorithm in systematic tests. Using a small sample of bright galaxies from the
SDSS, we demonstrate the performance of this algorithm in practice. We are able
to disentangle the problems of classification and parametrisation of galaxy
morphologies in this case. Results: We give physical arguments that a
probabilistic classification scheme is necessary. The algorithm we present
produces reasonable morphological classes and object-to-class assignments
without any prior assumptions. Conclusions: There are sophisticated automated
classification algorithms that meet all necessary requirements, but a lot of
work is still needed on the interpretation of the results.Comment: 18 pages, 19 figures, 2 tables, submitted to A
Comparing PyMorph and SDSS photometry. II. The differences are more than semantics and are not dominated by intracluster light
The Sloan Digital Sky Survey pipeline photometry underestimates the
brightnesses of the most luminous galaxies. This is mainly because (i) the SDSS
overestimates the sky background and (ii) single or two-component Sersic-based
models better fit the surface brightness profile of galaxies, especially at
high luminosities, than does the de Vaucouleurs model used by the SDSS
pipeline. We use the PyMorph photometric reductions to isolate effect (ii) and
show that it is the same in the full sample as in small group environments, and
for satellites in the most massive clusters as well. None of these are expected
to be significantly affected by intracluster light (ICL). We only see an
additional effect for centrals in the most massive halos, but we argue that
even this is not dominated by ICL. Hence, for the vast majority of galaxies,
the differences between PyMorph and SDSS pipeline photometry cannot be ascribed
to the semantics of whether or not one includes the ICL when describing the
stellar mass of massive galaxies. Rather, they likely reflect differences in
star formation or assembly histories. Failure to account for the SDSS
underestimate has significantly biased most previous estimates of the SDSS
luminosity and stellar mass functions, and therefore Halo Model estimates of
the z ~ 0.1 relation between the mass of a halo and that of the galaxy at its
center. We also show that when one studies correlations, at fixed group mass,
with a quantity which was not used to define the groups, then selection effects
appear. We show why such effects arise, and should not be mistaken for physical
effects.Comment: 15 pages, 17 figures, accepted for publication in MNRAS. The PyMorph
luminosities and stellar masses are available at
https://www.physics.upenn.edu/~ameert/SDSS_PhotDec
The high mass end of the stellar mass function: Dependence on stellar population models and agreement between fits to the light profile
We quantify the systematic effects on the stellar mass function which arise
from assumptions about the stellar population, as well as how one fits the
light profiles of the most luminous galaxies at z ~ 0.1. When comparing results
from the literature, we are careful to separate out these effects. Our analysis
shows that while systematics in the estimated comoving number density which
arise from different treatments of the stellar population remain of order < 0.5
dex, systematics in photometry are now about 0.1 dex, despite recent claims in
the literature. Compared to these more recent analyses, previous work based on
Sloan Digital Sky Survey (SDSS) pipeline photometry leads to underestimates of
rho_*(> M_*) by factors of 3-10 in the mass range 10^11 - 10^11.6 M_Sun, but up
to a factor of 100 at higher stellar masses. This impacts studies which match
massive galaxies to dark matter halos. Although systematics which arise from
different treatments of the stellar population remain of order < 0.5 dex, our
finding that systematics in photometry now amount to only about 0.1 dex in the
stellar mass density is a significant improvement with respect to a decade ago.
Our results highlight the importance of using the same stellar population and
photometric models whenever low and high redshift samples are compared.Comment: 18 pages, 17 figures, accepted for publication in MNRAS. The PyMorph
luminosities and stellar masses are available at
https://www.physics.upenn.edu/~ameert/SDSS_PhotDec
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
We present a new non-parametric method to quantify morphologies of galaxies
based on a particular family of learning machines called support vector
machines. The method, that can be seen as a generalization of the classical CAS
classification but with an unlimited number of dimensions and non-linear
boundaries between decision regions, is fully automated and thus particularly
well adapted to large cosmological surveys. The source code is available for
download at http://www.lesia.obspm.fr/~huertas/galsvm.html To test the method,
we use a seeing limited near-infrared ( band, ) sample observed
with WIRCam at CFHT at a median redshift of . The machine is trained
with a simulated sample built from a local visually classified sample from the
SDSS chosen in the high-redshift sample's rest-frame (i band, ) and
artificially redshifted to match the observing conditions. We use a
12-dimensional volume, including 5 morphological parameters and other
caracteristics of galaxies such as luminosity and redshift. We show that a
qualitative separation in two main morphological types (late type and early
type) can be obtained with an error lower than 20% up to the completeness limit
of the sample () which is more than 2 times better that what would
be obtained with a classical C/A classification on the same sample and indeed
comparable to space data. The method is optimized to solve a specific problem,
offering an objective and automated estimate of errors that enables a
straightforward comparison with other surveys.Comment: 11 pages, 7 figures, 3 tables. Submitted to A&A. High resolution
images are available on reques
A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. II. Quantifying morphological k-correction in the COSMOS field at 1<z<2: Ks band vs. I band
We quantify the effects of \emph{morphological k-correction} at by
comparing morphologies measured in the K and I-bands in the COSMOS area.
Ks-band data have indeed the advantage of probing old stellar populations for
, enabling a determination of galaxy morphological types unaffected by
recent star formation. In paper I we presented a new non-parametric method to
quantify morphologies of galaxies on seeing limited images based on support
vector machines. Here we use this method to classify
selected galaxies in the COSMOS area observed with WIRCam at CFHT. The obtained
classification is used to investigate the redshift distributions and number
counts per morphological type up to and to compare to the results
obtained with HST/ACS in the I-band on the same objects from other works. We
associate to every galaxy with and a probability between 0 and
1 of being late-type or early-type. The classification is found to be reliable
up to . The mean probability is . It decreases with redshift
and with size, especially for the early-type population but remains above
. The classification is globally in good agreement with the one
obtained using HST/ACS for . Above , the I-band classification
tends to find less early-type galaxies than the Ks-band one by a factor
1.5 which might be a consequence of morphological k-correction effects.
We argue therefore that studies based on I-band HST/ACS classifications at
could be underestimating the elliptical population. [abridged]Comment: accepted for publication in A&A, updated with referee comments, 12
pages, 10 figure
The role of environment in the morphological transformation of galaxies in 9 intermediate redshift clusters
[abridged] We analyze a sample of 9 massive clusters at 0.4<z<0.6 observed
with MegaCam in 4 photometric bands (g,r,i,z) from the core to a radius of 5
Mpc (~4000 galaxies). Galaxy cluster candidates are selected using photometric
redshifts computed with HyperZ. Morphologies are estimated with galSVM in two
broad morphological types (early-type and late-type). We examine the
morphological composition of the red-sequence and the blue-cloud and study the
relations between galaxies and their environment through the morphology-density
relations (T-Sigma) and the morphology-radius relation (T-R) in a mass limited
sample (log(M/Msol)>9.5). We find that the red sequence is already in place at
z~0.5 and it is mainly composed of very massive (log(M/Msol)>11.3) early-type
galaxies. These massive galaxies seem to be already formed when they enter the
cluster, probably in infalling groups, since the fraction remains constant with
the cluster radius. Their presence in the cluster center could be explained by
a segregation effect reflecting an early assembly history. Any evolution that
takes place in the galaxy cluster population occurs therefore at lower masses
(10.3<log(M/Msol)<11.3). For these galaxies, the evolution, is mainly driven by
galaxy-galaxy interactions in the outskirts as revealed by the T-Sigma
relation. Finally, the majority of less massive galaxies (9.5<log(M/Msol)<10.3)
are late-type galaxies at all locations, suggesting that they have not started
the morphological transformation yet even if this low mass bin might be
affected by incompleteness.Comment: A&A in pres
Euclid preparation : XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models
- âŠ