A Full $w$CDM Analysis of KiDS-1000 Weak Lensing Maps using Deep Learning

TL;DR

This paper introduces a comprehensive $w$CDM analysis of KiDS-1000 weak lensing data using graph-convolutional neural networks and simulation-based inference, achieving improved constraints on cosmological parameters.

Contribution

It presents a novel combination of deep learning, extensive simulations, and likelihood-free inference for full-sky weak lensing analysis, incorporating systematics and astrophysical uncertainties.

Findings

Constraints on $S_8$ are around 0.78-0.79, consistent with previous results.

Deep learning analysis improves constraint precision by 16%.

Baryonic feedback broadens the $S_8$ constraints by about 10%.

Abstract

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.