Higher-order convergence statistics for three-dimensional weak gravitational lensing

Dipak Munshi, Alan Heavens, Peter Coles

Research output: Contribution to journalArticlepeer-review

Abstract

Weak gravitational lensing on a cosmological scale can provide strong constraints on both the nature of dark matter and the dark energy equation of state. Most of the current weak lensing studies are restricted to (two-dimensional) projections, but tomographic studies with photometric redshifts have been started, and future surveys offer the possibility of probing the evolution of structure with redshift. In future we will be able to probe the growth of structure in 3D and put tighter constraints on cosmological models than can be achieved by the use of galaxy redshift surveys alone. Earlier studies in this direction focused mainly on evolution of the 3D power spectrum, but extension to higher-order statistics can lift degeneracies as well as providing information on primordial non-Gaussianity. We present analytical results for specific higher-order descriptors, the bispectrum and trispectrum, as well as collapsed multipoint statistics derived from them, i.e. cumulant correlators. We also compute quantities we call the skew-spectrum and kurt-spectra, which are the Fourier transforms of the well-known cumulant correlators. We compute the redshift dependence of these objects and study their performance in the presence of realistic noise and photometric redshift errors.

Original languageEnglish
Pages (from-to)2161-2185
Number of pages25
JournalMonthly Notices of the Royal Astronomical Society
Volume411
Issue number4
DOIs
Publication statusPublished - Mar 2011

Keywords

  • gravitational lensing: weak
  • methods: analytical
  • methods: numerical
  • methods: statistical
  • large-scale structure of Universe
  • LARGE-SCALE STRUCTURE
  • COSMOLOGICAL PERTURBATION-THEORY
  • SMALL ANGULAR SCALES
  • COSMIC SHEAR
  • DARK-MATTER
  • NONLINEAR SCALES
  • NON-GAUSSIANITY
  • POWER SPECTRUM
  • ANALYTICAL PREDICTIONS
  • MODEL PREDICTIONS

Cite this