相关论文: Exploring the cosmological consequences of JLA sup…

We explore the systematic uncertainties of using Type Ia supernovae (SNe Ia) as cosmological probes, using the Supernova Legacy Survey Three Year data (SNLS3). We focus on studying the possible evolution of the stretch-luminosity parameter…

In this work, by applying the redshift tomography method to Joint Light-curve Analysis (JLA) supernova sample, we explore the possible redshift-dependence of stretch-luminosity parameter $\alpha$ and color-luminosity parameter $\beta$. The…

We examine how dark energy constraints from current observational data depend on the analysis methods used: the analysis of Type Ia supernovae (SNe Ia), and that of galaxy clustering data. We generalize the flux-averaging analysis method of…

In this work, we explore the cosmological consequences of the latest Type Ia supernova (SN Ia) data-set, Pantheon, by adopting the $wCDM$ model. The Pantheon data-set is the largest SN Ia samples till now, which contains 1048 supernovae on…

Because of flux conservation, flux-averaging {\it justifies} the use of the distance-redshift relation for a smooth universe in the analysis of type Ia supernova (SN Ia) data. We have combined the SN Ia data from the High-$z$ SN Search and…

We investigated implications of recently released `Joint Light-curve Analysis' (JLA) supernova Ia (SNe Ia) data for dark energy models with time varying equation of state of dark energy, usually expressed as $w(z)$ in terms of variation…

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…

In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density…

We examine three SNe Type Ia datasets: Union2.1, JLA and Panstarrs to check their consistency using cosmology blind statistical analyses as well as cosmological parameter fitting. We find that Panstarrs dataset is the most stable of the…

Recent works have shown that small shifts in redshift -- gravitational redshift or systematic errors -- could potentially cause a significant bias in the estimation of cosmological parameters. I aim to verify whether a theoretical…

We report a comprehensive statistical analysis of the observational data of the cosmic evolution of supernova (SN) rate density, to derive constraints on cosmic star formation history and the nature of type Ia supernova (SN Ia) progenitor.…

We present a new compilation of Type Ia supernovae (SNe Ia), a new dataset of low-redshift nearby-Hubble-flow SNe and new analysis procedures to work with these heterogeneous compilations. This ``Union'' compilation of 414 SN Ia, which…

The Foundation Supernova Survey aims to provide a large, high-fidelity, homogeneous, and precisely-calibrated low-redshift Type Ia supernova (SN Ia) sample for cosmology. The calibration of the current low-redshift SN sample is the largest…

Bayesian graphical models are an efficient tool for modelling complex data and derive self-consistent expressions of the posterior distribution of model parameters. We apply Bayesian graphs to perform statistical analyses of Type Ia…

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…

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of…

Constraints on dark energy from current observational data are sensitive to how distances are measured from Type Ia supernova (SN Ia) data. We find that flux-averaging of SNe Ia can be used to test the presence of unknown systematic…

We reconstruct the dark energy density $\rho_X(z)$ as a free function from current type Ia supernova (SN Ia) data (Tonry et al. 2003; Barris et al. 2003; Knop et al. 2003), together with the Cosmic Microwave Background (CMB) shift parameter…

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…

The success of Type Ia supernova (SN Ia) distance standardisation for cosmology relies on a single global linear relationship between their peak luminosity and colour, the $\beta$ parameter. However, there are several pieces of evidence and…