Detecting the integrated Sachs-Wolfe effect with stacked voids

The stacking of cosmic microwave background (CMB) patches has been recently used to detect the integrated Sachs-Wolfe effect (iSW). When focusing on the locations of superstructures identified in the Sloan Digital Sky Survey (SDSS), Granett et al. (2008a, Gr08) found a signal with strong significance and an amplitude reportedly higher than expected within the LambdaCDM paradigm. We revisit the analysis using our own robust protocol, and extend the study to the two most recent and largest catalogues of voids publicly available. We quantify and subtract the level of foreground contamination in the stacked images and determine the contribution on the largest angular scales from the first multipoles of the CMB. We obtain the radial temperature and photometry profiles from the stacked images. Using a Monte Carlo approach, we computed the statistical significance of the profiles for each catalogue and identified the angular scale at which the signal-to-noise ratio (S/N) is maximum. We essentially confirm the signal detection reported by Gr08, but for the other two catalogues, a rescaling of the voids to the same size on the stacked image is needed to find any significant signal (with a maximum at ~2.4 sigmas). This procedure reveals that the photometry peaks at unexpectedly large angles in the case of the Gr08 voids, in contrast to voids from other catalogues. Conversely, the photometry profiles derived from the stacked voids of these other catalogues contain small central hot spots of uncertain origin. We also stress the importance of a posteriori selection effects that might arise when intending to increase the S/N, and we discuss the possible impact of void overlap and alignment effects. We argue that the interpretation in terms of an iSW effect of any detected signal via the stacking method is far from obvious.Comment: 14 pages, 18 figures, 2 tables. Submitted, accepted and published in A&A ; Minor changes to match the published version of the pape

Bias in Matter Power Spectra ?

We review the constraints given by the linear matter power spectra data on cosmological and bias parameters, comparing the data from the PSCz survey (Hamilton et al., 2000) and from the matter power spectrum infered by the study of Lyman alpha spectra at z=2.72 (Croft et al., 2000). We consider flat--$\Lambda$ cosmologies, allowing $\Lambda$, $H_0$ and $n$ to vary, and we also let the two ratio factors $r_{pscz}$ and $r_{lyman}$ ($r^2_i = \frac{P_{i}(k)}{P_{CMB}(k)}$) vary independently. Using a simple $\chi^2$ minimisation technique, we find confidence intervals on our parameters for each dataset and for a combined analysis. Letting the 5 parameters vary freely gives almost no constraints on cosmology, but requirement of a universal ratio for both datasets implies unacceptably low values of $H_0$ and $\Lambda$. Adding some reasonable priors on the cosmological parameters demonstrates that the power derived by the PSCz survey is higher by a factor $\sim 1.75$ compared to the power from the Lyman $\alpha$ forest survey.Comment: Accepted in A&

An Approximation to the Likelihood Function for Band-Power Estimates of CMB Anisotropies

Band-power estimates of cosmic microwave background fluctuations are now routinely used to place constraints on cosmological parameters. For this to be done in a rigorous fashion, the full likelihood function of band-power estimates must be employed. Even for Gaussian theories, this likelihood function is not itself Gaussian, for the simple reason that band-powers measure the {\em variance} of the random sky fluctuations. In the context of Gaussian sky fluctuations, we use an ideal situation to motivate a general form for the full likelihood function from a given experiment. This form contains only two free parameters, which can be determined if the 68% and 95% confidence intervals of the true likelihood function are known. The ansatz works remarkably well when compared to the complete likelihood function for a number of experiments. For application of this kind of approach, we suggest that in the future both 68% and 95% (and perhaps also the 99.7%) confidence intervals be given when reporting experimental results.Comment: Published versio