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Register a new userComparison of the linear bias models in the light of the Dark Energy Survey
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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.
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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 those peaks. On large scales, where density fluctuations are mild, this bias simply amounts to a constant offset in the clustering amplitude of the tracer, known as linear bias. In this work we focus on the relative bias between galaxies and galaxy clusters that are located inside and in the vicinity of cosmic voids, extended regions of relatively low density in the large-scale structure of the Universe. With the help of hydro-dynamical simulations we verify that the relation between galaxy and cluster overdensity around voids remains linear. Hence, the void-centric density profiles of different tracers can be linked by a single multiplicative constant. This amounts to the same value as the relative linear bias between tracers for the largest voids in the sample. For voids of small sizes, which typically arise in higher density regions, this constant has a higher value, possibly showing an environmental dependence similar to that observed for the linear bias itself. We confirm our findings by analysing mocks and data obtained during the first year of observations by the Dark Energy Survey. As a side product, we present the first catalogue of three-dimensional voids extracted from a photometric survey with a controlled photo-z uncertainty. Our results will be relevant in forthcoming analyses that attempt to use voids as cosmological probes.
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 competitive method to determine linear and higher-order galaxy bias and assess clustering systematics. We measure the galaxy bias by comparing the first four moments of the galaxy density distribution with those of the dark matter distribution. We use data from the MICE simulation to evaluate the performance of this method, and subsequently perform measurements on the public Science Verification (SV) data from the Dark Energy Survey (DES). We find that the linear bias obtained with CiC is consistent with measurements of the bias performed using galaxy-galaxy clustering, galaxy-galaxy lensing, CMB lensing, and shear+clustering measurements. Furthermore, we compute the projected (2D) non-linear bias using the expansion $delta_{g} = sum_{k=0}^{3} (b_{k}/k!) delta^{k}$, finding a non-zero value for $b_2$ at the $3sigma$ level. We also check a non-local bias model and show that the linear bias measurements are robust to the addition of new parameters. We compare our 2D results to the 3D prediction and find compatibility in the large scale regime ($>30$ Mpc $h^{-1}$)
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 provided by the various galaxy surveys in order to constrain the growth index ($gamma$) of the linear matter perturbations. Considering a constant growth index we can put tight constraints, up to $sim 10%$ accuracy, on $gamma$. Specifically, using the priors of the Dark Energy Survey and implementing a joint likelihood procedure between theoretical expectations and data we find that the best fit value is in between $gamma=0.64pm 0.075$ and $0.65pm 0.063$. On the other hand utilizing the Planck priors we obtain $gamma=0.680pm 0.089$ and $0.690pm 0.071$. This shows a small but non-zero deviation from General Relativity ($gamma_{rm GR}approx 6/11$), nevertheless the confidence level is in the range $sim 1.3-2sigma$. Moreover, we find that the estimated mass of the dark-matter halo in which LRGs survive lies in the interval $sim 6.2 times 10^{12} h^{-1} M_{odot}$ and $1.2 times 10^{13} h^{-1} M_{odot}$, for the different bias models. Finally, allowing $gamma$ to evolve with redshift [Taylor expansion: $gamma(z)=gamma_{0}+gamma_{1}z/(1+z)$] we find that the $(gamma_{0},gamma_{1})$ parameter solution space accommodates the GR prediction at $sim 1.7-2.9sigma$ levels.
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 metastable dark energy models considered in this article, are excellent candidates to alleviate the $H_0$ tension. In particular, for the present models, Planck 2018 alone can alleviate the $H_0$ tension within 68% CL. Along with the final cosmic microwave background data from the Planck 2018 legacy release, we also include external cosmological datasets in order to asses the robustness of our findings.
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 variational approximation to the true posterior of the latent parameters in the models, yielding consistent model comparison results with those derived by the standard Bayesian method, which suffers from a computationally expensive integral over the parameters in the product of the likelihood and the prior. The parallel computational nature of the network together with the stochastic gradient descent optimization technique leads to an efficient way to compare the physical models given a set of observations. The converged network also provides interpolation for a dataset, which is useful for data reconstruction.
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