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Weak gravitational lensing is a powerful probe, which is used to constrain the standard cosmological model and its extensions. With the enhanced statistical precision of current and upcoming surveys, high-accuracy predictions for weak lensing statistics are needed to limit the impact of theoretical uncertainties on cosmological parameter constraints. For this purpose, we present a comparison of the theoretical predictions for the non-linear matter and weak lensing power spectra, based on the widely used fitting functions (mead and rev-halofit), emulators (EuclidEmulator, EuclidEmulator2, BaccoEmulator, and CosmicEmulator), and N-body simulations (PKDGRAV3). We consider the forecasted constraints on the ЛCDM and wCDM models from weak lensing for stage III and stage IV surveys. We study the relative bias on the constraints and their dependence on the assumed prescriptions. Assuming a ЛCDM cosmology, we find that the relative agreement on the S8 parameter is between 0.2 and 0.3σ for a stage III-like survey between the above predictors. For a stage IV-like survey the agreement becomes 1.4-3.0σ. In the wCDM scenario, we find broader S8 constraints, and agreements of 0.18-0.26σ and 0.7-1.7σ for stage III and stage IV surveys, respectively. The accuracies of the above predictors therefore appear adequate for stage III surveys, whereas the fitting functions would need improvements for future stage IV surveys. Furthermore, we find that, of the fitting functions, mead provides the best agreement with the emulators. We discuss the implication of these findings for the preparation of future weak lensing surveys, and the relative impact of theoretical uncertainties to other systematics., Monthly Notices of the Royal Astronomical Society, 522 (3), ISSN:0035-8711, ISSN:1365-2966

We present a full forward-modeled $w$CDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the $\texttt{CosmoGrid}$, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large data set size and survey area, we perform a spherical analysis while limiting our map resolution to $\texttt{HEALPix}$ $n_\mathrm{side}=512$. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the non-linear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood free inference method called Gaussian Process Approximate Bayesian Computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of $S_8 \equiv \sigma_8\sqrt{\Omega_M/0.3} = 0.78^{+0.06}_{-0.06}$ for our power spectrum analysis and $S_8 = 0.79^{+0.05}_{-0.05}$ for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of on-going and future weak lensing surveys., Comment: 22 pages, 13 figures