Speaker
Description
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 (κ) 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. AKRA has proven successful in recovering high-precision κ maps from masked shear catalogs, both in flat-sky and full-sky scenarios. More recently, we have applied AKRA to the first-year data release of the Hyper Suprime-Cam (HSC) survey, achieving the first set of κ maps that consistently incorporate masks and heterogeneous noise. We are now extending the method to larger datasets, including DES, to build a wider range of κ-map products for scientific applications such as non-Gaussian statistics. With upcoming surveys like Euclid and CSST, AKRA will provide a promising tool for extracting cosmological information directly from the κ field.
