A revised density split statistic model for general filters

Abstract

Context. Studying the statistical properties of the large-scale structure in the Universe with weak gravitational lensing is a prime goal of several current and forthcoming galaxy surveys. The power that weak lensing has to constrain cosmological parameters can be enhanced by considering statistics beyond second-order shear correlation functions or power spectra. One such higher-order probe that has proven successful in observational data is density split statistics (DSS), in which one analyses the mean shear profiles around points that are classified according to their foreground galaxy density. Aims. In this paper, we generalise the most accurate DSS model to allow for a broad class of angular filter functions used for the classification of the different local density regions. This approach is motivated by earlier findings showing that an optimised filter can provide tighter constraints on model parameters compared to the standard top-hat case. Methods. As in the previous DSS model we built on large deviation theory approaches and approximations thereof to model the matter density probability distribution function, and on perturbative calculations of higher-order moments of the density field. The novel addition relies on the generalisation of these previously employed calculations to allow for general filter functions and is validated on several sets of numerical simulations. Results. It is shown that the revised model fits the simulation measurements well for many filter choices, with a residual systematic offset that is small compared to the statistical accuracy of current weak lensing surveys. However, by use of a simple calibration method and a Markov chain Monte Carlo analysis, we studied the expected sensitivity of the DSS to cosmological parameters and find unbiased results and constraints comparable to the commonly used two-point cosmic shear measures. Hence, our DSS model can be used in competitive analyses of current cosmic shear data, while it may need refinements for forthcoming lensing surveys.

KiDS-1000 cosmology: Constraints from density split statistics

“…In this work, we present an unblinded density split statistic analysis of the fourth data release of the Kilo-Degree survey (KiDS-1000). The analytical model used to infer cosmological and astrophysical parameters was first developed in Friedrich et al (2018) and then modified in Burger et al (2022), and we further validated it on realistic simulated data. The lenses used to construct the foreground density map are taken from the KiDSbright sample described in Bi21, while for our sources, we used the fourth and fifth tomographic redshift bins of the KiDS-1000 data described in H21.…”

Starlet higher order statistics for galaxy clustering and weak lensing

“…To perform the galaxy clustering analysis, we employed the KiDS-1000-like lens catalogues of luminous red galaxies (LRGs) provided by the cosmo-SLICS simulations. This simulated foreground LRG sample reproduces the characteristics described in Vakili et al (2020) and is split into four tomographic bins (see Burger et al 2022, for a recent study employing the same catalogues). Each catalogue contains the position and redshift of each galaxy.…”

Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

“…Further complications arise by the need to account for baryonic feedback effects, as well as systematics effects such as photometric redshift uncertainties, shear multiplicative bias and galaxy intrinsic alignments (IA). This helps explain why existing real-data constraints using higher-order shear information are based not on the full 3-point correlation function, but on other statistics including aperture moments [17][18][19][20][21], lensing peaks [22][23][24], density-split statistics [25][26][27][28][29] and persistent homology of cosmic shear [30,31]. The shear 3PCF was recently measured using DES Year 3 (Y3) data [20], although only in patches over the survey and not over the whole footprint as that would be too computationally demanding.…”