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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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In this work, we use a test based on the differential ages of galaxies for distinguishing the dark energy models. As proposed by Jimenez and Loeb, relative ages of galaxies can be used to put constraints on various cosmological parameters. In the same vein, we reconstruct $H_0dt/dz$ and its derivative ($H_0d^2t/dz^2$) using a model independent technique called non-parametric smoothing. Basically, $dt/dz$ is the change in the age of the object as a function of redshift which is directly link with the Hubble parameter. Hence for reconstruction of this quantity, we use the most recent $H(z)$ data. Further, we calculate $H_0dt/dz$ and its derivative for several models like Phantom, Einstein de Sitter (EdS), $Lambda$CDM, Chevallier-Polarski-Linder (CPL) parametrization, Jassal-Bagla-Padmanabhan (JBP) parametrization and Feng-Shen-Li-Li (FSLL) parametrization. We check the consistency of these models with the results of reconstruction obtained in model independent way from the data. It is observed that $H_0dt/dz$ as a tool is not able to distinguish between the $Lambda$CDM, CPL, JBP and FSLL parametrizations but as expected EdS and Phantom models show noticeable deviation from the reconstructed results. Further, the derivative of $H_0dt/dz$ for various dark energy models is more sensitive at low redshift. It is observed that the FSLL model is not consistent with the reconstructed results at redshifts less than $0.5$, however, the $Lambda$CDM model is in concordance with the 3$sigma$ region of the reconstruction.
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.
The accelerated expansion of the Universe is one of the main discoveries of the past decades, indicating the presence of an unknown component: the dark energy. Evidence of its presence is being gathered by a succession of observational experiments with increasing precision in its measurements. However, the most accepted model for explaining the dynamic of our Universe, the so-called Lambda cold dark matter, face several problems related to the nature of such energy component. This has lead to a growing exploration of alternative models attempting to solve those drawbacks. In this review, we briefly summarize the characteristics of a (non-exhaustive) list of dark energy models as well as some of the most used cosmological samples. Next, we discuss how to constrain each models parameters using observational data. Finally, we summarize the status of dark energy modeling.
A large number of cosmological parameters have been suggested for obtaining information on the nature of dark energy. In this work, we study the efficacy of these different parameters in discriminating theoretical models of dark energy, using both currently available supernova (SNe) data, and simulations of future observations. We find that the current data does not put strong constraints on the nature of dark energy, irrespective of the cosmological parameter used. For future data, we find that the although deceleration parameter can accurately reconstruct some dark energy models, it is unable to discriminate between different models of dark energy, therefore limiting its usefulness. Physical parameters such as the equation of state of dark energy, or the dark energy density do a good job of both reconstruction and discrimination if the matter density is known to high accuracy. However, uncertainty in matter density reduces the efficacy of these parameters. A recently proposed parameter, Om(z), constructed from the first derivative of the SNe data, works very well in discriminating different theoretical models of dark energy, and has the added advantage of not being dependent on the value of matter density. Thus we find that a cosmological parameter constructed from the first derivative of the data, for which the theoretical models of dark energy are sufficiently distant from each other, and which is independent of the matter density, performs the best in reconstructing dark energy from SNe data.
The differential age data of astrophysical objects that have evolved passivelly during the history of the universe (e.g. red galaxies) allows to test theoretical cosmological models through the predicted Hubble function expressed in terms of the redshift $z$, $H(z)$. We use the observational data for $H(z)$ to test unified scenarios for dark matter and dark energy. Specifically, we focus our analysis on the Generalized Chaplygin Gas (GCG) and the viscous fluid (VF) models. For the GCG model, it is shown that the unified scenario for dark energy and dark matter requires some priors. For the VF model we obtain estimations for the free parameters that may be compared with further analysis mainly at perturbative level.
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