The goal is to investigate dark energy and the accelerated expansion of the Universe, as well as the clustering properties of dark matter from large-scale structure data.To this end, we want to apply novel analysis techniques, which combine data modelling,data simulation, and data analysis.The first step requires modelling and simulating the distribution of large-scale structuretracers, which will be massively available in futuregalaxy surveys (DESI, EUCLID, JPAS), such as, bright galaxies (BGs), luminous red galaxies (LRGs), emission line galaxies (eLGs), H-alpha galaxies, and quasars.
Our aim is to produce highly accurate, efficient mock galaxy catalogues exploiting automatic statistical learning techniques applied to detailedreference simulations and to the observations themselves. In particular, we plan to applythe BAM code developed within the previous national grant (by Balaguera-Antolínez y Kitaura).
Then, we plan to test our inference techniques to extract cosmological parameters onthose realistic synthetic galaxy catalogues by relying on the bias expressions found in the first step. In particular, we plan to develop a joint baryon acoustic and redshift space distortions Bayesian analysis machinery based on the COSMIC BIRTH code developed by the PI within the previous national grant. We aim at unifying the BAM and COSMIC BIRTH codes to jointly account for the galaxy and Lyman alpha forest bias within a Bayesian framework.
Finally, we plan to apply our analysis pipeline on observational data of the DESI and JPAS collaboration and extract cosmological parameters.