58 research outputs found
Cosmological Constraints with Clustering-Based Redshifts
We demonstrate that observations lacking reliable redshift information, such
as photometric and radio continuum surveys, can produce robust measurements of
cosmological parameters when empowered by clustering-based redshift estimation.
This method infers the redshift distribution based on the spatial clustering of
sources, using cross-correlation with a reference dataset with known redshifts.
Applying this method to the existing SDSS photometric galaxies, and projecting
to future radio continuum surveys, we show that sources can be efficiently
divided into several redshift bins, increasing their ability to constrain
cosmological parameters. We forecast constraints on the dark-energy
equation-of-state and on local non-gaussianity parameters. We explore several
pertinent issues, including the tradeoff between including more sources versus
minimizing the overlap between bins, the shot-noise limitations on binning, and
the predicted performance of the method at high redshifts. Remarkably, we find
that, once this technique is implemented, constraints on dynamical dark energy
from the SDSS imaging catalog can be competitive with, or better than, those
from the spectroscopic BOSS survey and even future planned experiments.
Further, constraints on primordial non-Gaussianity from future large-sky
radio-continuum surveys can outperform those from the Planck CMB experiment,
and rival those from future spectroscopic galaxy surveys. The application of
this method thus holds tremendous promise for cosmology.Comment: 7 pages, 3 figures, 2 tables; to be submitted to MNRA
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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