Speaker
Description
Weak gravitational lensing is an invaluable tool for understanding fundamental cosmological physics.
In this study, we introduce the Accurate Kappa Reconstruction Algorithm for masked shear catalog (AKRA), a novel approach that overcomes the limitations in traditional methods and significantly improves the accuracy of the reconstructions. Using simulated experiments, we demonstrate that the AKRA method outperforms traditional methods across a range of masks, achieving high-quality reconstructions in both well-sampled and partially masked regions. Moreover, our results show that the AKRA method substantially reduces errors in power spectrum estimation and cross correlation coefficient compared to the widely-used Kaiser Squire (KS) method.
We also investigate the impact of mask shape and pixel ratio on reconstruction quality and find that increasing the masked pixel ratio does not compromise the reconstruction quality when the ratio is below 50%.
This study emphasizes the importance of accurate and reliable weak gravitational lensing convergence map reconstruction techniques and provides valuable insights for upcoming surveys.
