153 research outputs found
Testing the Halo Model Against the SDSS Photometric Survey
We present halo model predictions for the expected angular clustering and
associated errors from the completed Sloan Digital Sky Survey (SDSS)
photometric galaxy sample. These results are used to constrain halo model
parameters under the assumption of a fixed LCDM cosmology using standard Fisher
matrix techniques. Given the ability of the five-color SDSS photometry to
separate galaxies into sub-populations by intrinsic color, we also use
extensions of the standard halo model formalism to calculate the expected
clustering of red and blue galaxy sub-populations as a further test of the
galaxy evolution included in the semi-analytic methods for populating dark
matter halos with galaxies. The extremely small sample variance and Poisson
errors from the completed SDSS survey should result in very impressive
constraints (~1-10%) on the halo model parameters for a simple
magnitude-limited sample and should provide an extremely useful check on the
behavior of current and future N-body simulations and semi-analytic techniques.
We also show that similar constraints are possible using a narrow selection
function, as would be possible using photometric redshifts, without making
linear assumptions regarding the evolution of the underlying power spectra. In
both cases, we explore the effects of uncertainty in the selection function on
the resulting constraints and the degeneracies between various combinations of
parameters.Comment: 16 pages, 17 figure
Modeling Galaxy Clustering by Color
We extend the mass-halo formalism for analytically generating power spectra
to allow for the different clustering behavior observed in galaxy
sub-populations. Although applicable to other separations, we concentrate our
methods on a simple separation by rest-frame color into ``red'' and ``blue''
sub-populations through modifications to the relations and halo
distribution functions for each of the sub-populations. This sort of separation
is within the capabilities of the current generations of simulations as well as
galaxy surveys, suggesting a potentially powerful observational constraint for
current and future simulations. In anticipation of this, we demonstrate the
sensitivity of the resulting power spectra to the choice of model parameters.Comment: 8 pages, 9 figures, submitted to MNRAS; minor typos correcte
Clustering-based Redshift Estimation: Comparison to Spectroscopic Redshifts
We investigate the potential and accuracy of clustering-based redshift
estimation using the method proposed by M\'enard et al. (2013). This technique
enables the inference of redshift distributions from measurements of the
spatial clustering of arbitrary sources, using a set of reference objects for
which redshifts are known. We apply it to a sample of spectroscopic galaxies
from the Sloan Digital Sky Survey and show that, after carefully controlling
the sampling efficiency over the sky, we can estimate redshift distributions
with high accuracy. Probing the full colour space of the SDSS galaxies, we show
that we can recover the corresponding mean redshifts with an accuracy ranging
from z=0.001 to 0.01. We indicate that this mapping can be used to
infer the redshift probability distribution of a single galaxy. We show how the
lack of information on the galaxy bias limits the accuracy of the inference and
show comparisons between clustering redshifts and photometric redshifts for
this dataset. This analysis demonstrates, using real data, that
clustering-based redshift inference provides a powerful data-driven technique
to explore the redshift distribution of arbitrary datasets, without any prior
knowledge on the spectral energy distribution of the sources.Comment: 13 pages. Submitted to MNRAS. Comments welcom
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