The first AKRA mass map reconstruction from HSC Y1 data

TL;DR

This paper introduces AKRA, a new unbiased, prior-free mass-mapping algorithm, and applies it to the first real HSC Y1 data, validating its effectiveness with mock catalogs.

Contribution

The paper presents the first real-data application of AKRA, demonstrating its unbiased reconstruction of convergence maps from HSC Y1 shear data.

Findings

AKRA produces unbiased lensing power spectrum and higher-order statistics.

Validation with mock catalogs confirms AKRA's robustness across various survey masks.

Reconstructed $ppa$ maps are ready for scientific analysis.

Abstract

Weak lensing mass-mapping from shear catalogs faces systematic challenges from survey masks and spatially varying noise. To overcome these issues and reconstruct unbiased convergence $\kappa$ maps, we have constructed the AKRA (Accurate Kappa Reconstruction Algorithm), a prior-free and maximum-likelihood based analytical method. It has been validated for mock shear catalogs with a variety of survey masks. In this work, we present the first real-data application of the AKRA on the Subaru Hyper Suprime-Cam Year 1 (HSC Y1) data. We first validate AKRA using mock shear catalogs from the \texttt{Kun} simulation suite, with masks corresponding to the six HSC Y1 regions (\texttt{GAMA09H}, \texttt{GAMA15H}, \texttt{HECTOMAP}, \texttt{VVDS}, \texttt{WIDE12H}, and \texttt{XMMLSS}). The investigated statistics, including the lensing power spectrum, $\langle \kappa^2\rangle$, $\langle \kappa^3\rangle$, and the one-point probability distribution function of $\kappa$, are all unbiased. We then apply AKRA to the HSC Y1 shear catalog and provide reconstructed $\kappa$ maps ready for subsequent scientific analyses.