Related papers: Constraining dark energy models using Jackknife an…
The main aim of this paper is to perform a model comparison for some reconstructions of the key properties that describe the dark energy of the Universe i.e. energy density and the equation of state (EoS). We carry out this process by using…
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks. We provide a tight…
We perform a detailed confrontation of various oscillating dark-energy parame-trizations with the latest sets of observational data. In particular, we use data from Joint Light Curve analysis (JLA) sample from Supernoave Type Ia, Baryon…
Statistical resampling methods have become feasible for parametric estimation, hypothesis testing, and model validation now that the computer is a ubiquitous tool for statisticians. This essay focuses on the resampling technique for…
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…
We study supernova (SN) classification using the machine learning method of the Recurrent Neural Network (RNN) in the Chinese Space Station Survey Telescope Ultra-Deep Field (CSST-UDF) photometric survey, and explore the improvement of the…
Observational growth rate data had been derived from observations of redshift distortions in galaxy redshift surveys. Here we use the growth rate data to place constraints on the dark energy model parameters. By performing a joint analysis…
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…
We present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are…
We use the Type Ia Supernova gold sample data of Riess {\it et al} in order to constrain three models of dark energy. We study the Cardassian model, the Dvali-Turner gravity modified model and the generalized Chaplygin gas model of dark…
A key science goal of upcoming dark energy surveys is to seek time evolution of the dark energy. This problem is one of {\em model selection}, where the aim is to differentiate between cosmological models with different numbers of…
Type Ia supernova (SN Ia), galaxy clustering, and cosmic microwave background anisotropy (CMB) data provide complementary constraints on the nature of the dark energy in the universe. We find that the three-year Wilkinson Microwave…
We present constraints on the dark energy equation-of-state parameter, w=P/(rho c^2), using 60 Type Ia supernovae (SNe Ia) from the ESSENCE supernova survey. We derive a set of constraints on the nature of the dark energy assuming a flat…
In this paper, we combine the the latest observational data, including the WMAP five-year data (WMAP5), the baryon acoustic oscillations (BAO) and type Ia supernovae (SN) "union" compilation, and use the Markov Chain Monte Carlo method to…
So far large and different data sets revealed the accelerated expansion rate of the Universe, which is usually explained in terms of dark energy. The nature of dark energy is not yet known, and several models have been introduced: a non…
Using the Markov chain Monte Carlo (MCMC) method we perform a global analysis constraining the dynamics of dark energy in light of the supernova (Riess "Gold" samples), galaxy clustering (SDSS 3D power spectra and SDSS lyman-\alpha forest…
In this paper, we constrain dark energy models using a compendium of observations at low redshifts. We consider the dark energy as a barotropic fluid, with the equation of state a constant as well the case where dark energy equation of…
For galaxy clustering to provide robust constraints on cosmological parameters and galaxy formation models, it is essential to make reliable estimates of the errors on clustering measurements. We present a new technique, based on a spatial…
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…
We explore the prospects for using future supernova observations to probe the dark energy. We focus on quintessence, an evolving scalar field that has been suggested as a candidate for the dark energy. After simulating the observations that…