Cosmological constraints with weak lensing peak counts and second-order statistics in a large-field survey
Austin Peel, Chieh-An Lin, Francois Lanusse, Adrienne Leonard,, Jean-Luc Starck, Martin Kilbinger

TL;DR
This study evaluates the effectiveness of weak lensing peak counts and second-order statistics in constraining cosmological parameters using simulated large-field survey data, highlighting their comparable power and potential for combined analysis.
Contribution
It demonstrates the application of the Camelus model to large surveys for cosmological parameter estimation and compares peak counts with two-point correlation functions.
Findings
Peak statistics provide tight but biased constraints that require calibration.
Peak counts and 2PCF have comparable constraining power.
Combining both probes can improve cosmological constraints.
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…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Statistical Methods and Inference
