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The cosmological information of shear peaks: beyond the abundance

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 Added by Laura Marian
 Publication date 2013
  fields Physics
and research's language is English
 Authors Laura Marian




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We study the cosmological information of weak lensing (WL) peaks, focusing on two other statistics besides their abundance: the stacked tangential-shear profiles and the peak-peak correlation function. We use a large ensemble of simulated WL maps with survey specifications relevant to future missions like Euclid and LSST, to explore the three peak probes. We find that the correlation function of peaks with high signal-to-noise (S/N) measured from fields of size 144 sq. deg. has a maximum of ~0.3 at an angular scale ~10 arcmin. For peaks with smaller S/N, the amplitude of the correlation function decreases, and its maximum occurs on smaller angular scales. We compare the peak observables measured with and without shape noise and find that for S/N~3 only ~5% of the peaks are due to large-scale structures, the rest being generated by shape noise. The covariance matrix of the probes is examined: the correlation function is only weakly covariant on scales < 30 arcmin, and slightly more on larger scales; the shear profiles are very correlated for theta > 2 arcmin, with a correlation coefficient as high as 0.7. Using the Fisher-matrix formalism, we compute the cosmological constraints for {Om_m, sig_8, w, n_s} considering each probe separately, as well as in combination. We find that the correlation function of peaks and shear profiles yield marginalized errors which are larger by a factor of 2-4 for {Om_m, sig_8} than the errors yielded by the peak abundance alone, while the errors for {w, n_s} are similar. By combining the three probes, the marginalized constraints are tightened by a factor of ~2 compared to the peak abundance alone, the least contributor to the error reduction being the correlation function. This work therefore recommends that future WL surveys use shear peaks beyond their abundance in order to constrain the cosmological model.



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