相关论文: From Hubble to Snap Parameters: A Gaussian Process…
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…
Gaussian process ($GP$) regression is a widely used non-parametric modeling tool, but its cubic complexity in the training size limits its use on massive data sets. A practical remedy is to predict using only the nearest neighbours of each…
We introduce an adaptable kinematic modelling tool called ROHSA-SNAPD, "Spatially Non-parametric Approach to PSF Deconvolution using ROHSA". ROHSA-SNAPD utilises kinematic regularisation to forward model the intrinsic emission-line flux and…
Strongly lensed quasar systems with time delay measurements provide "time delay distances", which are a combination of three angular diameter distances and serve as powerful tools to determine the Hubble constant $H_0$. However, current…
In this work, we discuss model-independent reconstruction of the expansion history of the late Universe. We use Gaussian Process Regression (GPR) to reconstruct the evolution of various cosmological parameters such as Hubble parameter…
Gaussian processes (GP) provide an elegant and model-independent method for extracting cosmological information from the observational data. In this work, we employ GP to perform a joint analysis by using the geometrical cosmological probes…
The difference from 4 to 6 $\sigma$ in the Hubble constant ($H_0$) between the values observed with the local (Cepheids and Supernovae Ia, SNe Ia) and the high-z probes (CMB obtained by the Planck data) still challenges the astrophysics and…
Recent studies suggest spectroscopic differences explain a fraction of the variation in Type Ia supernova (SN Ia) luminosities after light-curve/color standardization. In this work, (i) we empirically characterize the variations of…
The cosmic deceleration parameter $q$ has been reconstructed in a non-parametric way using various combinations of recent observational datasets. The Pantheon compilation of the Supernova (SN) distance modulus data, the Cosmic Chronometer…
Gaussian processes offers a convenient way to perform nonparametric reconstructions of observational data assuming only a kernel which describes the covariance between neighbouring points in a data set. We approach the ambiguity in the…
In this study, we introduce a novel analytical Gaussian Process (GP) cosmography methodology, leveraging the differentiable properties of GPs to derive key cosmological quantities analytically. Our approach combines cosmic chronometer (CC)…
Cosmographic approach, a Taylor expansion of the Hubble function, has been used as a model-independent method to investigate the evolution of the universe in the presence of cosmological data. Apart from possible technical problems like the…
We present a determination of the Hubble constant ($H_0$) using the latest observational data from multiple cosmological probes, providing an independent geometric calibration of the SN Ia distance scale. By combining baryon acoustic…
Gaussian processes provide a method for extracting cosmological information from observations without assuming a cosmological model. We carry out cosmography -- mapping the time evolution of the cosmic expansion -- in a model-independent…
In this paper, linear first order expansion of deceleration parameter $q(z)=q_0+q_1(1-a)$ ($M_1$), constant jerk $j=j_0$ ($M_2$) and third order expansion of luminosity distance ($M_3$) are confronted with cosmic observations: SCP 307 SN…
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,…
A natural extension of the standard cosmological model are models that include curvature as a free parameter. In this work we study in detail the observational constraints on the non-flat $\Lambda CDM$ model using the two main geometric…
A model-independent reconstruction of the cosmic expansion rate is essential to a robust analysis of cosmological observations. Our goal is to demonstrate that current data are able to provide reasonable constraints on the behavior of the…
Inferring high-fidelity constraints on the spatial curvature parameter, $\Omega_{\rm K}$, under as few assumptions as possible, is of fundamental importance in cosmology. We propose a method to non-parametrically infer $\Omega_{\rm K}$ from…
Using a fully Bayesian approach, Gaussian Process regression is extended to include marginalisation over the kernel choice and kernel hyperparameters. In addition, Bayesian model comparison via the evidence enables direct kernel comparison.…