Related papers: Cosmic expansion history from SNe Ia data via info…
Cosmological analyses using the latest set of type Ia SNe data weakly favor an evolving dark energy (EDE) model without strongly disfavoring the standard LCDM paradigm. Nonparametric reconstructions of the expansion history may reveal…
In modern cosmology, the rapid growth of high-precision observational data, along with significant theoretical advances, has intensified the challenge of identifying a robust, model-independent framework to probe the expansion history of…
We apply two methods, namely the Gaussian processes and the non-parametric smoothing procedure, to reconstruct the Hubble parameter $H(z)$ as a function of redshift from 15 measurements of the expansion rate obtained from age estimates of…
The cosmic expansion history, mapped by the Hubble parameter as a function of redshift, offers the most direct probe of the dark energy equation of state. One way to determine the Hubble parameter at different redshifts is essentially…
We have improved upon the method of smoothing supernovae data to reconstruct the expansion history of the universe, h(z), using two latest datasets, Gold and SNLS. The reconstruction process does not employ any parameterization and is…
We perform a model independent reconstruction of the cosmic expansion rate based on type Ia supernova data. Using the Union 2.1 data set, we show that the Hubble parameter behaviour allowed by the data without making any hypothesis about…
We propose a non-parametric method of smoothing supernova data over redshift using a Gaussian kernel in order to reconstruct important cosmological quantities including H(z) and w(z) in a model independent manner. This method is shown to be…
With a model independent method the expansion history $H(z)$, the deceleration parameter $q(z)$ of the universe and the equation of state $w(z)$ for the dark energy are reconstructed directly from the 192 Sne Ia data points, which contain…
The existing degeneracy between different dark energy and modified gravity cosmologies at the background level may be broken by analysing quantities at the perturbative level. In this work, we apply a non-parametric smoothing (NPS) method…
Perturbative quantities, such as the growth rate ($f$) and index ($\gamma$), are powerful tools to distinguish different dark energy models or modified gravity theories even if they produce the same cosmic expansion history. In this work,…
We present an exact, non-perturbative and non-singular ansatz for the universe's expansion history through a novel analytic scale factor, $a(t)=e^{H(t)} { (1-e^{-k(t)t}) }^{b(t)}$, which reproduces the observed sequence of cosmic epochs and…
The standard cosmographic approach consists in performing a series expansion of a cosmological observable around $z=0$ and then using the data to constrain the cosmographic (or kinematic) parameters at present time. Such a procedure works…
The current paper is addressing the possibility of the Dark Energy scalar field potential reconstruction from the SNe Ia data and the problems arising during the process. We describe the method and test its limits, stability of the…
In this work, we propose a new nonparametric approach for reconstructing a function from observational data using an Artificial Neural Network (ANN), which has no assumptions about the data and is a completely data-driven approach. We test…
Deriving the expansion history of the Universe is a major goal of modern cosmology. To date, the most accurate measurements have been obtained with Type Ia Supernovae and Baryon Acoustic Oscillations, providing evidence for the existence of…
We reconstruct the expansion history of the Universe using type Ia supernovae (SN Ia) in a manner independent of any cosmological model assumptions. To do so, we implement a non-parametric iterative smoothing method on the Joint Light-curve…
Based on the largest homogeneously reduced set of Type Ia supernova luminosity data currently available -- the Union2 sample -- we reconstruct the expansion history of the Universe in a model-independent approach. Our method tests the…
We use the machine learning techniques, for the first time, to study the background evolution of the universe in light of 30 cosmic chronometers. From 7 machine learning algorithms, using the principle of mean squared error minimization on…
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a…
We present that by combining Crossing Statistic and Smoothing method one can reconstruct the expansion history of the universe with a very high precision without considering any prior on the cosmological quantities such as the equation of…