Cosmological hydrodynamical simulations, such as EAGLE and IllustrisTNG, provide valuable insights when coupled with high-quality survey data, enhancing our comprehension of galaxy evolution. In this work, we adopt a variance analysis of subsets of continuum-subtracted Sloan Digital Sky Survey (SDSS) spectra by Sharbaf+2023 and evaluate equivalent

Machine Learning applications such as the hybrid-CNN (Guigluon et al. 2024) allows to homogeneously combine Gaia RVS spectra, photometry (G, BP, RP), parallaxes and the XP coefficients to obtain precise stellar parameters down to S/N=15. In this contribution, I will focus on the scientific applications and new results facilitated by this machine

Metal-poor ultracool dwarfs (ultracool subdwarfs, including very-low-mass stars and brown dwarfs) are tracers of the pristine chemical composition at early phases in the assembly of the Milky Way. They are poorly understood because they are extremely faint and rare. Our aims are to improve the knowledge of ultracool subdwarf candidates, to break