### Abstract

Shear peak statistics has gained a lot of attention recently as a practical alternative to the two-point statistics for constraining cosmological parameters. We perform a shear peak statistics analysis of the Dark Energy Survey (DES) Science Verification (SV) data, using weak gravitational lensing measurements from a 139 deg^{2} field. We measure the abundance of peaks identified in aperture mass maps, as a function of their signal-to-noise ratio, in the signal-to-noise range 0 < S/N < 4. To predict the peak counts as a function of cosmological parameters, we use a suite of N-body simulations spanning 158 models with varying Ω_{m} and σ_{8}, fixing w=-1, Ω_{b} = 0.04, h = 0.7 and n_{s} = 1, to which we have applied the DES SV mask and redshift distribution. In our fiducial analysis we measure σ_{8}(Ω_{m}/0.3)^{0.6} = 0.77 ± 0.07, after marginalizing over the shear multiplicative bias and the error on the mean redshift of the galaxy sample. We introduce models of intrinsic alignments, blending and source contamination by cluster members. These models indicate that peaks with S/N > 4 would require significant corrections, which is why we do not include them in our analysis. We compare our results to the cosmological constraints from the two-point analysis on the SV field and find them to be in good agreement in both the central value and its uncertainty. We discuss prospects for future peak statistics analysis with upcoming DES data.

Original language | English (US) |
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Pages (from-to) | 3653-3673 |

Number of pages | 21 |

Journal | Monthly Notices of the Royal Astronomical Society |

Volume | 463 |

Issue number | 4 |

DOIs | |

State | Published - 2016 |

### All Science Journal Classification (ASJC) codes

- Astronomy and Astrophysics
- Space and Planetary Science

### Keywords

- Cosmological parameter
- Cosmology: observations
- Dark matter
- Gravitational lensing: weak
- Methods: data analysis
- Methods: statistical

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## Cite this

*Monthly Notices of the Royal Astronomical Society*,

*463*(4), 3653-3673. https://doi.org/10.1093/mnras/stw2070