No abstract

Weak lensing is one of the most powerful probes for dark matter and dark energy science, although it faces increasing challenges in controlling systematic uncertainties as the statistical errors become smaller. The Point Spread Function (PSF) needs to be precisely modeled to avoid systematic error on the weak lensing measurements. The weak lensing biases induced by errors in the PSF model second moments, i.e. its size and shape, are well-studied. However, Zhang et al. (2021) showed that errors in the higher moments of the PSF may also be a significant source of systematics for upcoming weak lensing surveys. Therefore, this work comprehensively investigate the modeling quality of PSF moments from the 3rd to 6th order, and propagate the PSFEx higher moments modeling error in the HSC survey dataset to the weak lensing shear-shear correlation functions and their cosmological analyses. The overall multiplicative shear bias associated with errors in PSF higher moments can cause a ∼0.1σ shift on the cosmological parameters for LSST Y10, while the associated additive biases can induce 1σ uncertainties in cosmology parameter inference for LSST Y10, if not accounted. We compare the PSFEx model with PSF in Full FOV (Piff), and find similar performance in modeling the PSF higher moments. We conclude that PSF higher moment errors of the future PSF models should be reduced from those in current methods, otherwise needed to be explicitly modeled in the weak lensing analysis.

The rapidly increasing statistical power of cosmological imaging surveys requires us to reassess the regime of validity for various approximations that accelerate the calculation of relevant theoretical predictions. In this paper, we present the results of the 'N5K non-Limber integration challenge', the goal of which was to quantify the performance of different approaches to calculating the angular power spectrum of galaxy number counts and cosmic shear data without invoking the so-called 'Limber approximation', in the context of the Rubin Observatory Legacy Survey of Space and Time (LSST). We quantify the performance, in terms of accuracy and speed, of three non-Limber implementations: FKEM (CosmoLike), Levin, and matter, themselves based on different integration schemes and approximations. We find that in the challenge's fiducial 3x2pt LSST Year 10 scenario, FKEM (CosmoLike) produces the fastest run time within the required accuracy by a considerable margin, positioning it favourably for use in Bayesian parameter inference. This method, however, requires further development and testing to extend its use to certain analysis scenarios, particularly those involving a scale-dependent growth rate. For this and other reasons discussed herein, alternative approaches such as matter and Levin may be necessary for a full exploration of parameter space. We also find that the usual first-order Limber approximation is insufficiently accurate for LSST Year 10 3x2pt analysis on ℓ = 200 − 1000, whereas invoking the second-order Limber approximation on these scales (with a full non-Limber method at smaller ℓ) does suffice.

Cosmological parameter constraints from recent galaxy imaging surveys are reaching 2 − 3%-level accuracy. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will produce sub-percent level measurements of cosmological parameters, providing a milestone test of the ΛCDM model. To supply guidance to the upcoming LSST analysis, it is important to understand thoroughly the results from different recent galaxy imaging surveys and assess their consistencies. In this work we perform a unified catalog-level reanalysis of three cosmic shear datasets: the first year data from the Dark Energy Survey (DES-Y1), the 1,000 deg 2 dataset from the Kilo-Degree Survey (KiDS-1000), and the first year data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-Y1). We utilize a pipeline developed and rigorously tested by the LSST Dark Energy Science Collaboration to perform the reanalysis and assess the robustness of the results to analysis choices. We find the S 8 constraint to be robust to two different small-scale modeling approaches, and varying choices of cosmological priors. Our unified analysis allows the consistency of the surveys to be rigorously tested and we find the three surveys to be statistically consistent. Due to the partially overlapping footprint, we model the cross-covariance between KiDS-1000 and HSC-Y1 approximately when combining all three datasets, resulting in a 1.6-1.9% constraint on S 8 given different assumptions on the cross-covariance.

No abstract