AKRA 2.0: Accurate Kappa Reconstruction Algorithm for masked shear catalog

Abstract

Cosmic shear surveys serve as a powerful tool for mapping the underlying matter density field, including non-visible dark matter. A key challenge in cosmic shear surveys is the accurate reconstruction of lensing convergence ($\kappa$) maps from shear catalogs impacted by survey boundaries and masks, which seminal Kaiser-Squires (KS) method are not designed to handle. To overcome these limitations, we previously proposed the Accurate Kappa Reconstruction Algorithm (AKRA), a prior-free maximum likelihood map-making method. Initially designed for flat sky scenarios with periodic boundary conditions, AKRA has proven successful in recovering high-precision $\kappa$ maps from masked shear catalogs. In this work, we upgrade AKRA to AKRA 2.0 by integrating the tools designed for spherical geometry. This upgrade employs spin-weighted spherical harmonic transforms to reconstruct the convergence field over the full sky. To optimize computational efficiency, we implement a scale-splitting strategy that segregates the analysis into two parts: large-scale analysis on the sphere (referred to as AKRA-sphere) and small-scale analysis on the flat sky (referred to as AKRA-flat); the results from both analyses are then combined to produce final reconstructed $\kappa$ map. We tested AKRA 2.0 using simulated shear catalogs with various masks, demonstrating that the reconstructed $\kappa$ map by AKRA 2.0 maintains high accuracy. For the reconstructed $\kappa$ map in unmasked regions, the reconstructed convergence power spectrum $C_{\kappa }^{\mathrm{rec}}$ and the correlation coefficient with the true $\kappa$ map $r_{\ell }$ achieve accuracies of $(1-C_{\ell }^{\mathrm{rec}}/C_{\ell }^{\mathrm{true}})\lesssim 1\%$ and $(1-r_{\ell })\lesssim 1\%$, respectively. Our algorithm is capable of straightforwardly handling further issues such as inhomogeneous shape measurement noise, which we will address in subsequent analysis.

34 pages, 17 figures. The code is publicly available at https://github.com/shiyuan-1/akra_series

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Cited by in corpus (1)

• Advancing weak lensing mass mapping with a mask-aware HEALPix transformer