Joint likelihood function of cluster counts and n point correlation functions: Improving their power through including halo sample variance
Abstract
Naive estimates of the statistics of largescale structure and weak lensing power spectrum measurements that include only Gaussian errors exaggerate their scientific impact. Nonlinear evolution and finitevolume effects are both significant sources of nonGaussian covariance that reduce the ability of power spectrum measurements to constrain cosmological parameters. Using a halo model formalism, we derive an intuitive understanding of the various contributions to the covariance and show that our analytical treatment agrees with simulations. This approach enables an approximate derivation of a joint likelihood for the cluster number counts, the weak lensing power spectrum and the bispectrum. We show that this likelihood is a good description of the raytracing simulation. Since all of these observables are sensitive to the same finitevolume effects and contain information about the nonlinear evolution, a combined analysis recovers much of the "lost" information. For upcoming weak lensing surveys, we estimate that a joint analysis of power spectrum, number counts and bispectrum will produce an improvement of about 3040% in determinations of the matter density and the scalar amplitude. This improvement is equivalent to doubling the survey area.
 Publication:

Physical Review D
 Pub Date:
 December 2014
 DOI:
 10.1103/PhysRevD.90.123523
 arXiv:
 arXiv:1406.3330
 Bibcode:
 2014PhRvD..90l3523S
 Keywords:

 98.80.k;
 95.36.+x;
 95.75.z;
 98.65.Dx;
 Cosmology;
 Dark energy;
 Observation and data reduction techniques;
 computer modeling and simulation;
 Superclusters;
 largescale structure of the Universe;
 Astrophysics  Cosmology and Nongalactic Astrophysics
 EPrint:
 20 pages, 12 figures, comments welcome