arXiv subjects

Maryi Alejandra Carvajal Quintero

Publications and source records attributed to Maryi Alejandra Carvajal Quintero.

3 recordsLinked to original sources

arXiv2022

$H_0$ tension or $M$ overestimation?

There is a strong discrepancy between the value of the Hubble parameter $H_0^P$ obtained from large scale observations such as the Planck mission, and the small scale value $H_0^R$, obtained from low redshift supernovae (SNe). The value of the absolute magnitude $M^{Hom}$ used as prior in analyzing observational data is obtained from low-redshift SNe, assuming a homogeneous Universe, but the distance of the anchors used to calibrate the SNe to obtain $M$ would be affected by a local inhomogeneity, making it inconsistent to test the Copernican principle using $M^{Hom}$, since $M$ estimation itself is affected by local inhomogeneities. We perform an analysis of the luminosity distance of low redshift SNe, using different values of $M$, $\{M^P,M^R\}$, corresponding to different values of $H_0$, $\{H_0^P,H_0^R\}$, obtained from the model independent consistency relation between $H_0$ and $M$ which can be derived from the definition of the distance modulus. We find that the value of $M$ can strongly affect the evidence of a local inhomogeneity. We analyze data from the Pantheon catalog, finding no significant statistical evidence of a local inhomogeneity using the parameters $\{M^R,H_0^R\}$, confirming previous studies, while with $\{M^P,H_0^P\}$ we find evidence of a small local void, which causes an overestimation of $M^R$ with respect to $M^P$. An inhomogeneous model with the parameters $\{M^P,H_0^P\}$ fits the data better than a homogeneous model with $\{M^R,H_0^R\}$, resolving the apparent $H_0$ tension. Using $\{M^P,H_0^P\}$, we obtain evidence of a local inhomogeneity with a density contrast $-0.140 \pm 0.042 $, extending up to a redshift of $z_v =0.056 \pm 0.0002$, in good agreement with recent results of galaxy catalogs analysis.

astro-ph.CO↗

arXiv2021

Combining gravitational and electromagnetic waves observations to investigate local structure and the Hubble tension

Recent estimations of the Hubble parameter $H_0$ based on gravitational waves (GW) observations can be used to shed some light on the discrepancy between the value of the Hubble parameter $H_0^P$ obtained from large scale observations such as the Planck mission, and the small scale value $H_0^R$, obtained from low redshift supernovae (SNe). In order to investigate the origin of this discrepancy we perform a combined analysis of the luminosity distance of SNe and GW sources, using different methods, finding that the impact of the GW data is very limited, due to the small number of data points, and their large errors. We analyze separately data from the Pantheon and the Union 2.1 catalogues, finding that a model with $H_0^P$ and a small local void can fit the data as well as a homogeneous model with $H_0^R$, resolving the apparent $H_0$ tension. We find that there is a significant difference between the size and depth of the inhomogeneity obtained using the two datasets, which could be due to the different sky coverage of the two catalogues. For Pantheon we obtain evidence of a local inhomogeneity with a density contrast $δ_v=-0.155 \pm 0.026$, extending up to a redshift of $z_v = 0.056 \pm 0.0002$, while for Union 2.1 we obtain $δ_v=-0.461 \pm 0.032$ and $z_v= 0.081\pm 0.008$. We also perform some analysis using redshift shell averaged data, and obtain approximately the same results, hinting to the fact that the effects of the monopole component of the local inhomogeneity are the dominant ones

gr-qc↗

arXiv2020

A deep learning approach to cosmological dark energy models

We propose a novel deep learning tool in order to study the evolution of dark energy models. The aim is to combine two architectures: the Recurrent Neural Networks (RNN) and the Bayesian Neural Networks (BNN), we named this full network as RNN+BNN. The first one is capable of learning complex sequential information to classify objects like supernovae and use the light-curves directly to learn information from the sequence of observations. Since RNN is not capable to calculate the uncertainties, BNN emerges as a solution for problems in deep learning like, for example, the overfitting. For the trainings we use measurements of the distance modulus $μ(z)$, such as those provided by Pantheon Supernovae Type Ia. In view of our results, the reported approach turns out to be a first promising step on how we can train a new neural network that can compute their own confidence regions for specific cosmological data. It is worth stressing that the new technique allows to reduce the computational load of expensive codes for dark energy models and probe the necessity of modified dark energy models at large redshifts for a supernovae trained sampler.

astro-ph.CO↗