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This paper is the first of a series of papers constraining cosmological parameters with weak lensing peak statistics using $sim 450~rm deg^2$ of imaging data from the Kilo Degree Survey (KiDS-450). We measure high signal-to-noise ratio (SNR: $ u$) weak lensing convergence peaks in the range of $3< u<5$, and employ theoretical models to derive expected values. These models are validated using a suite of simulations. We take into account two major systematic effects, the boost factor and the effect of baryons on the mass-concentration relation of dark matter haloes. In addition, we investigate the impacts of other potential astrophysical systematics including the projection effects of large scale structures, intrinsic galaxy alignments, as well as residual measurement uncertainties in the shear and redshift calibration. Assuming a flat $Lambda$CDM model, we find constraints for $S_{rm 8}=sigma_{rm 8}(Omega_{rm m}/0.3)^{0.5}=0.746^{+0.046}_{-0.107}$ according to the degeneracy direction of the cosmic shear analysis and $Sigma_{rm 8}=sigma_{rm 8}(Omega_{rm m}/0.3)^{0.38}=0.696^{+0.048}_{-0.050}$ based on the derived degeneracy direction of our high-SNR peak statistics. The difference between the power index of $S_{rm 8}$ and in $Sigma_{rm 8}$ indicates that combining the two probes has the potential to break the degeneracy in $sigma_{rm 8}$ and $Omega_{rm m}$. Our results are consistent with the cosmic shear tomographic correlation analysis of the same dataset and $sim 2sigma$ lower than the Planck 2016 results.
We study the statistics of peaks in a weak lensing reconstructed mass map of the first 450 square degrees of the Kilo Degree Survey. The map is computed with aperture masses directly applied to the shear field with an NFW-like compensated filter. We
We present cosmological parameter constraints from a tomographic weak gravitational lensing analysis of ~450deg$^2$ of imaging data from the Kilo Degree Survey (KiDS). For a flat $Lambda$CDM cosmology with a prior on $H_0$ that encompasses the most r
Convolutional Neural Networks (CNN) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular they have the potential to yield more precise cosmological constraints from weak lensing mass
In order to extract full cosmological information from next-generation large and high-precision weak lensing (WL) surveys (e.g. Euclid, Roman, LSST), higher-order statistics that probe the small-scale, non-linear regime of large scale structure (LSS)
We derived constraints on cosmological parameters using weak lensing peak statistics measured on the $sim130~{rm deg}^2$ of the Canada-France-Hawaii Telescope Stripe 82 Survey (CS82). This analysis demonstrates the feasibility of using peak statistic