97 research outputs found
Cosmology with the largest galaxy cluster surveys: Going beyond Fisher matrix forecasts
We make the first detailed MCMC likelihood study of cosmological constraints
that are expected from some of the largest, ongoing and proposed, cluster
surveys in different wave-bands and compare the estimates to the prevalent
Fisher matrix forecasts. Mock catalogs of cluster counts expected from the
surveys -- eROSITA, WFXT, RCS2, DES and Planck, along with a mock dataset of
follow-up mass calibrations are analyzed for this purpose. A fair agreement
between MCMC and Fisher results is found only in the case of minimal models.
However, for many cases, the marginalized constraints obtained from Fisher and
MCMC methods can differ by factors of 30-100%. The discrepancy can be
alarmingly large for a time dependent dark energy equation of state, w(a); the
Fisher methods are seen to under-estimate the constraints by as much as a
factor of 4--5. Typically, Fisher estimates become more and more inappropriate
as we move away from LCDM, to a constant-w dark energy to varying-w dark energy
cosmologies. Fisher analysis, also, predicts incorrect parameter degeneracies.
From the point of mass-calibration uncertainties, a high value of unknown
scatter about the mean mass-observable relation, and its redshift dependence,
is seen to have large degeneracies with the cosmological parameters sigma_8 and
w(a) and can degrade the cosmological constraints considerably. We find that
the addition of mass-calibrated cluster datasets can improve dark energy and
sigma_8 constraints by factors of 2--3 from what can be obtained compared to
CMB+SNe+BAO only. Since, details of future cluster surveys are still being
planned, we emphasize that optimal survey design must be done using MCMC
analysis rather than Fisher forecasting. [abridged]Comment: 26 pages, 13 figures, 7 tables, accepted for publication in JCA
On Weighted Multivariate Sign Functions
Multivariate sign functions are often used for robust estimation and
inference. We propose using data dependent weights in association with such
functions. The proposed weighted sign functions retain desirable robustness
properties, while significantly improving efficiency in estimation and
inference compared to unweighted multivariate sign-based methods. Using
weighted signs, we demonstrate methods of robust location estimation and robust
principal component analysis. We extend the scope of using robust multivariate
methods to include robust sufficient dimension reduction and functional outlier
detection. Several numerical studies and real data applications demonstrate the
efficacy of the proposed methodology.Comment: Keywords: Multivariate sign, Principal component analysis, Data
depth, Sufficient dimension reductio
The hybrid SZ power spectrum: Combining cluster counts and SZ fluctuations to probe gas physics
Sunyaev-Zeldovich (SZ) effect from a cosmological distribution of clusters
carry information on the underlying cosmology as well as the cluster gas
physics. In order to study either cosmology or clusters one needs to break the
degeneracies between the two. We present a toy model showing how complementary
informations from SZ power spectrum and the SZ flux counts, both obtained from
upcoming SZ cluster surveys, can be used to mitigate the strong cosmological
influence (especially that of sigma_8) on the SZ fluctuations. Once the strong
dependence of the cluster SZ power spectrum on sigma_8 is diluted, the cluster
power spectrum can be used as a tool in studying cluster gas structure and
evolution. The method relies on the ability to write the Poisson contribution
to the SZ power spectrum in terms the observed SZ flux counts. We test the toy
model by applying the idea to simulations of SZ surveys.Comment: 12 pages. 11 plots. MNRAS submitte
Precision cosmology with a combination of wide and deep Sunyaev-Zeldovich cluster surveys
We show the advantages of a wedding cake design for Sunyaev-Zel'dovich
cluster surveys. We show that by dividing up a cluster survey into a wide and a
deep survey, one can essentially recover the cosmological information that
would be diluted in a single survey of the same duration due to the
uncertainties in our understanding of cluster physics. The parameter degeneracy
directions of the deep and wide surveys are slightly different, and combining
them breaks these degeneracies effectively. A variable depth survey with a few
thousand clusters is as effective at constraining cosmological parameters as a
single depth survey with a much larger cluster sample.Comment: 4 figures, 1 table; revised versio
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