Status on bidimensional dark energy parameterizations using SNe Ia JLA and BAO datasets

Using current observations forecast type Ia supernovae (SNe Ia) Joint Lightcurve Analysis (JLA) and baryon acoustic oscillations (BAO), in this paper we investigate six bidimensional dark energy parameterisations in order to explore which has more constraining power. Our results indicate that for parameterisations that contain $z^2$-terms, the tension ($\sigma$-distance) between these datasets seems to be reduced and their behaviour are $<$1$\sigma$ compatible with $\Lambda$CDM. Also, the results obtained by performing their Bayesian evidence show a striking evidence in favour of the $\Lambda$CDM model, but only one parameterisation can be distinguish by around $1\%$ from the other models when the combination of datasets are considered.Comment: 14 pages, 2 figure

The Possibility of a Non-Lagrangian Theory of Gravity

General Relativity resembles a very elegant crystal glass: If we touch its principles, that is, its Lagrangian, there is a risk of breaking everything. Or, if we will, it is like a short blanket: Curing some problems creates new problems. This paper is devoted to bring to light the reasons why we pursue the possibility of a non-Lagrangian theory of gravity under the hypothesis of an extension of the original general relativity with an ansatz inspired in the fundamental principles of classical and quantum physics.Comment: 6 pages, 1 figure. Version accepted in Universe MDP

Nonparametric reconstruction of the Om diagnostic to test LCDM

Cosmic acceleration is usually related with the unknown dark energy, which equation of state, w(z), is constrained and numerically confronted with independent astrophysical data. In order to make a diagnostic of w(z), the introduction of a null test of dark energy can be done using a diagnostic function of redshift, Om. In this work we present a nonparametric reconstruction of this diagnostic using the so-called Loess-Simex factory to test the concordance model with the advantage that this approach offers an alternative way to relax the use of priors and find a possible 'w' that reliably describe the data with no previous knowledge of a cosmological model. Our results demonstrate that the method applied to the dynamical Om diagnostic finds a preference for a dark energy model with equation of state w =-2/3, which correspond to a static domain wall network.Comment: 10 pages, 5 figures, 2 table

Bayesian Deep Learning for Dark Energy

In this chapter, we discuss basic ideas on how to structure and study the Bayesian methods for standard models of dark energy and how to implement them in the architecture of deep learning processes