Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey
arXiv:1612.02264 · doi:10.1051/0004-6361/201629928
Abstract
Peak statistics in weak lensing maps access the non-Gaussian information contained in the large-scale distribution of matter in the Universe. They are therefore a promising complement to two-point and higher-order statistics to constrain our cosmological models. To prepare for the high-precision data of next-generation surveys, we assess the constraining power of peak counts in a simulated Euclid-like survey on the cosmological parameters , , and . In particular, we study how the Camelus model--a fast stochastic algorithm for predicting peaks--can be applied to such large surveys. We measure the peak count abundance in a mock shear catalogue of ~5,000 sq. deg. using a multiscale mass map filtering technique. We then constrain the parameters of the mock survey using Camelus combined with approximate Bayesian computation (ABC). We find that peak statistics yield a tight but significantly biased constraint in the - plane, indicating the need to better understand and control the model's systematics. We calibrate the model to remove the bias and compare results to those from the two-point correlation functions (2PCF) measured on the same field. In this case, we find the derived parameter with for peaks, while for 2PCF the value is with . We therefore see comparable constraining power between the two probes, and the offset of their - degeneracy directions suggests that a combined analysis would yield tighter constraints than either measure alone. As expected, cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and 2PCF. (abridged)
arXiv comment12 pages, 10 figures, submitted to A&A
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