Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data

The DES Collaboration

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

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 deg2 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 Ωm and σ8, fixing w=-1, Ωb = 0.04, h = 0.7 and ns = 1, to which we have applied the DES SV mask and redshift distribution. In our fiducial analysis we measure σ8m/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 languageEnglish (US)
Pages (from-to)3653-3673
Number of pages21
JournalMonthly Notices of the Royal Astronomical Society
Volume463
Issue number4
DOIs
StatePublished - 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

Fingerprint Dive into the research topics of 'Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data'. Together they form a unique fingerprint.

Cite this