相关论文: Estimating Cosmological Parameters and Reconstruct…

This paper introduces a new approach to reconstruct cosmological functions using artificial neural networks based on observational measurements with minimal theoretical and statistical assumptions. By using neural networks, we can generate…

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…

The Hubble parameter, $H(z)$, plays a crucial role in understanding the expansion history of the universe and constraining the Hubble constant, $\mathrm{H}_0$. The Cosmic Chronometers (CC) method provides an independent approach to…

Accurately measuring the Hubble parameter is vital for understanding the expansion history and properties of the universe. In this paper, we propose a new method that supplements the covariance between redshift pairs to improve the…

In this paper, we study the cosmological constraints from the measurements of Hubble parameters---$H(z)$ data. Here, we consider two kinds of $H(z)$ data: the direct $H_0$ probe from the Hubble Space Telescope (HST) observations of Cepheid…

In this work, we reconstruct the Hubble diagram using various data sets, including correlated ones, in Artificial Neural Networks (ANN). Using ReFANN, that was built for data sets with independent uncertainties, we expand it to include…

In this work, we explore the possibility of using artificial neural networks to impose constraints on teleparallel gravity and its $f(T)$ extensions. We use the available Hubble parameter observations from cosmic chronometers and baryon…

In this article, we employ a machine learning (ML) approach for the estimations of four fundamental parameters, namely, the Hubble constant ($H_0$), matter ($\Omega_{0m}$), curvature ($\Omega_{0k}$) and vacuum ($\Omega_{0\Lambda}$)…

The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian…

We discuss the cosmological degeneracy between the Hubble parameter H(z), the age of the universe and cosmological parameters describing simple variations from the minimal LCDM model. We show that independent determinations of the Hubble…

In this letter, we propose an improved cosmological model independent method of determining the value of the Hubble constant $H_0$. The method uses unanchored luminosity distances $H_0d_L(z)$ from SN Ia Pantheon data combined with angular…

Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate…

Reconstructing the evolution history of the dark energy equation of state parameter $w(z)$ directly from observational data is highly valuable in cosmology, since it contains substantial clues in understanding the nature of the accelerated…

We use simulated Hubble parameter data in the redshift range 0 \leq z \leq 2 to explore the role and power of observational H(z) data in constraining cosmological parameters of the {\Lambda}CDM model. The error model of the simulated data…

We present an approach for automatic extraction of measured values from the astrophysical literature, using the Hubble constant for our pilot study. Our rules-based model -- a classical technique in natural language processing -- has…

In this work, we propose a novel approach for cosmological parameter estimation and Hubble parameter reconstruction using Long Short-Term Memory (LSTM) networks and Efficient-Kolmogorov-Arnold Networks (Ef-KAN). LSTM networks are employed…

Recent cosmological observations have achieved high-precision measurements of the Universe's expansion history, prompting the use of nonparametric methods such as Gaussian processes (GP) regression. We apply GP regression for reconstructing…

Direct measurements of Hubble parameters $H(z)$ are very useful for cosmological model parameters inference. Based on them, Sahni, Shafieloo and Starobinski introduced a two-point diagnostic $Omh^2(z_i, z_j)$ as an interesting tool for…

In this paper, we calibrate the luminosity relation of gamma-ray bursts (GRBs) from an Artificial Neural Network (ANN) framework for reconstructing the Hubble parameter \unboldmath{$H(z)$} from the latest observational Hubble data (OHD)…

Over the past decades, cosmology has become largely based on experimental data, the most important sources of which are studies of the cosmic microwave background (CMB). CMB is present in the Universe since the very first moments of its…