Speaker
Description
The galaxy clustering at north hemisphere is precisely measured by DESI Yr1 survey, to aim better understanding the nature of cosmic acceleration. The credible interpretation of estimated galaxy spectrum is demanded for statistics at smaller scales in which the accurate prediction of higher order perturbation models are required. We advocate the hybrid approach to supplement the incomplete higher order theoretical computations with the templates computed by N-body simulations. It is presented a fast Gaussian process machine learning tool emulating those templates estimating the impact of scale-dependent parameters. This approach enables us to probe a fully model independent parameters of cosmic distances and growth structures with marginalizing the primordial shape parameters, such as the mass and baryon fraction, and the primordial spectral index. With the given Planck prior of those primordial parameters, we find that $f\sigma_8$ is consistent with the $\Lambda$CDM concordance model but the AP parameters are 1$\sigma$ away from the fiducial values.
