An Updated Tomographic Analysis of the Integrated Sachs-Wolfe Effect and implications for Dark Energy

Abstract

We derive updated constraints on the Integrated Sachs-Wolfe (ISW) effect through cross-correlation of the cosmic microwave background with galaxy surveys. We improve with respect to similar previous analyses in several ways. First, we use the most recent versions of extragalactic object catalogs: SDSS DR12 photometric redshift (photo-$z$) and 2MASS Photo-$z$ datasets, as well as employed earlier for ISW, SDSS QSO photo-$z$ and NVSS samples. Second, we use for the first time the WISE~$\times$~SuperCOSMOS catalog, which allows us to perform an all-sky analysis of the ISW up to $z\sim 0.4$. Third, thanks to the use of photo-$z$s, we separate each dataset into different redshift bins, deriving the cross-correlation in each bin. This last step leads to a significant improvement in sensitivity. We remove cross-correlation between catalogs using masks which mutually exclude common regions of the sky. We use two methods to quantify the significance of the ISW effect. In the first one, we fix the cosmological model, derive linear galaxy biases of the catalogs, and then evaluate the significance of the ISW using a single parameter. In the second approach we perform a global fit of the ISW and of the galaxy biases varying the cosmological model. We find significances of the ISW in the range 4.7-5.0 $\sigma$ thus reaching, for the first time in such an analysis, the threshold of 5 $\sigma$. Without the redshift tomography we find a significance of $\sim$ 4.0 $\sigma$, which shows the importance of the binning method. Finally we use the ISW data to infer constraints on the Dark Energy redshift evolution and equation of state. We find that the redshift range covered by the catalogs is still not optimal to derive strong constraints, although this goal will be likely reached using future datasets such as from Euclid, LSST, and SKA.

arXiv comment16 pages, 6 figures, 8 tables, 2 appendices; v2: minor changes, matches version published in PRD; ISW likelihood code is available within the new release of MontePython (see arXiv:1804.07261 )

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