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The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the $Lambda$CDM cosmology, is confronted to that of the Dark Energy Survey (DES) Luminous Red Galaxies (LRGs). Applying a $chi^2$ minimization procedure between models and data we find that all the considered linear bias models reproduce well the LRG bias data. The differences among the bias models are absorbed in the predicted mass of the dark-matter halo in which LRGs live and which ranges between $sim 6 times 10^{12} h^{-1} M_{odot}$ and $1.4 times 10^{13} h^{-1} M_{odot}$, for the different bias models. Similar results, reaching however a maximum value of $sim 2times 10^{13} h^{-1} M_{odot}$, are found by confronting the SDSS (2SLAQ) Large Red Galaxies clustering with theoretical clustering models, which also include the evolution of bias. This later analysis also provides a value of $Omega_{m}=0.30pm 0.01$, which is in excellent agreement with recent joint analyses of different cosmological probes and the reanalysis of the Planck data.
Luminous tracers of large-scale structure are not entirely representative of the distribution of mass in our Universe. As they arise from the highest peaks in the matter density field, the spatial distribution of luminous objects is biased towards th
Non-linear bias measurements require a great level of control of potential systematic effects in galaxy redshift surveys. Our goal is to demonstrate the viability of using Counts-in-Cells (CiC), a statistical measure of the galaxy distribution, as a
We study how the cosmological constraints from growth data are improved by including the measurements of bias from Dark Energy Survey (DES). In particular, we utilize the biasing properties of the DES Luminous Red Galaxies (LRGs) and the growth data
We investigate the recently introduced metastable dark energy (DE) models after the final Planck 2018 legacy release. The essence of the present work is to analyze their evolution at the level of perturbations. Our analyses show that both the metasta
This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an analytical variat