Cosmological forecasts with the clustering of weak lensing peaks
Christopher T. Davies (LMU, USM), Marius Cautun (Leiden), Benjamin, Giblin (Edinburgh, ifA), Baojiu Li (Durham, ICC), Joachim Harnois-D\'eraps, (LJMU, Newcastle), Yan-Chuan Cai (Edinburgh, ifA)

TL;DR
This paper demonstrates that analyzing the clustering of weak lensing peaks, especially when combining different peak heights, significantly enhances cosmological parameter constraints for upcoming surveys like LSST.
Contribution
It introduces a novel analysis of weak lensing peak clustering, showing its complementarity to peak abundance and shear correlation functions for improved cosmological constraints.
Findings
Clustering of low amplitude peaks complements high peaks.
Combining peak height information tightens constraints on cosmological parameters.
Peak clustering offers better constraints on $S_8$ and $w_0$ than shear correlation alone.
Abstract
Maximising the information that can be extracted from weak lensing measurements is a key goal for upcoming surveys such as LSST and Euclid. This is typically achieved through statistics that are complementary to the cosmic shear two-point correlation function, the most well established of which is the weak lensing peak abundance. In this work, we study the clustering of weak lensing peaks, and present parameter constraint forecasts for an LSST-like survey. We use the cosmoslics CDM simulations to measure the peak two-point correlation function for a range of cosmological parameters, and use the simulation data to train a Gaussian process regression emulator which is applied to generate likelihood contours and provide parameter constraint forecasts from mock observations. We investigate the dependence of the peak two-point correlation function on the peak height, and find that the…
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