Speaker
Description
As the stage-IV surveys are beginning, we need to make good preparations for data analysis. The "tomographic Alcock-Pacyznski method" is a unique analysis method that can explore smaller clustering scales. When applied to the SDSS survey, it successfully improved the probing precision of dark energy, the Hubble constant, and neutrino masses parameters by 30-100%. In this talk, I will present our ongoing research aimed at improving the analysis strategy: (1) Adopting statistics beyond standard two-point correlation to enhance the statistical power; (2) Employing data dimension reduction algorithms to compress statistics and alleviate the difficulties of covariance estimation; (3) Examining the cosmology dependencies of the RSDs, and studying the systematics induced by the redshift errors in the CSST survey. Our goal is to ensure the successful application of this method in the stage-IV surveys, achieving better scientific outcomes in exploring the nature of dark energy and dark matter, resolving the Hubble constant tension problem, probing neutrino masses, and so on.
