DE-ICAZA-LIZAOLA, MIGUEL,ANGEL,CUITLAHUAC (2021) Studying the dark universe with galaxies. Doctoral thesis, Durham University.
|PDF - Accepted Version|
This work presents two different but connected projects that study the dark components of the universe. We show the first full shape analysis of the eBOSS Luminous Red Galaxy (LRG) sample, which has an effective redshift of and used the data from the 14th data release of SDSS (DR14).
Amongst other parameters, we constrain the growth rate of the universe to have the value . Our results are in full agreement with the current -Cold Dark Matter cosmological model under the Planck cosmology. This study was followed up with a later data release that has found comparable results (DR16 Gil-Marín et al., 2020).
The second project uses sparse regression methods (SRM) to model the stellar masses of galaxies inside the EAGLE hydrodynamical simulation as a function of the properties of their host dark matter halos, without using prior knowledge of the underlying physics. Our model is designed to be an accurate and simple equation of the host halo properties, which makes it modifiable if one is interested in fitting to a set of statistics. An advantage of SRMs is that they are designed to remove unnecessary terms, our method discarded all parameters related to the angular momentum of the host halo, suggesting that they are not required to explain the stellar mass halo mass relation to the accuracy considered. Using an appropriate formulation of input parameters, our methodology can model satellite and central galaxies at the same time using a simpler model than when they are treated separately. Our models accurately reproduce the stellar mass function and the correlation function of EAGLE galaxies, which makes them an encouraging approach for the construction of realistic mock galaxy catalogs to interpret results from galaxy surveys.
|Item Type:||Thesis (Doctoral)|
|Award:||Doctor of Philosophy|
|Faculty and Department:||Faculty of Science > Physics, Department of|
|Copyright:||Copyright of this thesis is held by the author|
|Deposited On:||26 Oct 2021 10:29|