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Maryam Vazirnia

Publications and source records attributed to Maryam Vazirnia.

2 recordsLinked to original sources

Reconstruction of dark energy density by non-parametric approaches

The evolution of dark energy density is a crucial quantity in understanding the nature of dark energy. Often, the quantity is described by the so-called equation of state, that is the ratio of dark energy pressure to its density. In this scenario, the dark energy density is always positive throughout cosmic history and a negative value is not allowed. Assuming a homogeneous and isotropic universe, we reconstruct the dark energy density directly from observational data and investigate its evolution through cosmic history. We consider the latest SNIa, BAO and cosmic chronometer data and reconstruct the dark energy density in both flat and non-flat universes up to redshift $z\sim 3$. The results are well in agreement with the $Λ$CDM up to redshift $z\sim 1.5$, whereas all data and methods, in our analysis, provide a negative dark energy density at high redshifts.

astro-ph.CO↗

Non-parametric modeling of the cosmological data, base on the $χ^2$ distribution

In the $Λ$CDM model, cosmological observations from the late and recent universe reveal a puzzling $\sim 4.5σ$ tension in the current rate of universe expansion. In addition to the various scenarios suggested to resolve the tension, non-parametric modeling may provide useful insights. In this paper, we look at three well-known non-parametric methods, the smoothing method, the genetic algorithm, and the Gaussian process. Considering these three methods, we employ the recent Hubble parameters data to reconstruct the rate of universe expansion and supernovae Pantheon sample to reconstruct the luminosity distance. In contrast to the similar studies in the literature, the chi-squared distribution has been used to construct a reliable criterion to select a reconstruction. Finally, we compute the current rate of universe expansion ($H_0$) for each method, provide some discussions regarding the performance of each approach, and compare the results.

astro-ph.CO↗