arXiv subjects

Yuan-Bo Xie

Publications and source records attributed to Yuan-Bo Xie.

4 recordsLinked to original sources

arXiv2026

Probing the Hubble Tension with an Infinite-Future Condition on the Hubble Parameter

We study the impact of imposing an infinite-future condition on Gaussian-process (GP) reconstructions of $H(z)$ from 37 cosmic-chronometer measurements. Implementing the asymptotic limit $H(-1)=0$ expected in constant-$w$CDM with $w>-1$ as a pseudo-point at $z=-1$ with tunable uncertainty $σ_{-1}$ lowers the GP-inferred Hubble constant from $H_0=68.71\pm6.08$ to $H_0\simeq(64.67$--$65.86)\pm(4.45$--$4.85)~{\rm km\,s^{-1}\,Mpc^{-1}}$. The resulting $H_0$ remains within $\sim0.32$--$0.61\,σ$ of the \textit{Planck} $Λ$CDM value, while the separation from representative local distance-ladder measurements increases to $\sim1.45$--$1.83\,σ$. A scan over $σ_{-1}$ shows that the shift is governed by the effective pseudo-point weight, interpolating between the hard-condition and data-dominated limits. Finally, constant-$w$CDM Markov Chain Monte Carlo (MCMC) fits to the same $H(z)$ data with $H_0$ fixed to the GP-inferred values show that $Ω_m$ is only weakly affected, whereas lower $H_0$ shifts $w$ toward less negative values; allowing curvature broadens constraints and remains consistent with $Ω_k=0$.

astro-ph.CO↗

arXiv2025

Constraints on Baryon Density from the Effective Optical Depth of High-Redshift Quasars

We present constraints on the baryonic matter density parameter, $Ω_b$, within the framework of the $Λ$CDM model. Our analysis utilizes observational data on the effective optical depth from high-redshift quasars. To parameterize the photoionization rate $Γ_{-12}$, we employ a Bézier polynomial. Additionally, we approximate the Hubble parameter at high redshifts as $H(z)\approx 100hΩ_m^{1/2} (1+z)^{3/2}$ km s$^{-1}$ Mpc$^{-1}$. Confidence regions are obtained with $h=0.701\pm0.013$ and $Ω_m = 0.315$, optimized by the Planck mission. The best-fit values are $Ω_b =0.043^{+0.005}_{-0.006}$ and $Ω_b = 0.045^{+0.004}_{-0.006}$, corresponding to an old data set and a new data set, respectively. And we test the non-parametric form of $Γ_{-12}$, obtaining $Ω_b = 0.048^{+0.001}_{-0.003}$. These results are consistent with the findings of Planck at the 1 $σ$ confidence level. Our findings underscore the effectiveness of quasar datasets in constraining $Ω_b$, eliminating the need for independent photoionization rate data. This approach provides detailed cosmic information about baryon density and the photoionization history of the intergalactic medium.

astro-ph.CO↗

arXiv2024

A Non-parametric Reconstruction of the Hubble Parameter $H(z)$ Based on Radial Basis Function Neural Networks

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 reconstruction of the Hubble parameter using the OHD dataset. Our approach utilizes a cosmological model-independent radial basis function neural network (RBFNN) to describe the Hubble parameter as a function of redshift effectively. Our experiments show that this method results in a reconstructed Hubble parameter of $H_0 = 67.1\pm9.7~\mathrm{km~s^{-1}~Mpc^{-1}}$ , which is more noise-resistant and fits better with the $Λ$CDM model at high redshifts. Providing the covariance between redshift pairs in subsequent observations will significantly improve the reliability and accuracy of Hubble parametric data reconstruction. Future applications of this method could help overcome the limitations of previous methods and lead to new advances in our understanding of the universe.

astro-ph.CO↗

arXiv2021

Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

The errors of cosmological data generated from complex processes, such as the observational Hubble parameter data (OHD) and the Type Ia supernova (SN Ia) data, cannot be accurately modeled by simple analytical probability distributions, e.g. Gaussian distribution. To constrain cosmological parameters from these data, likelihood-free inference is usually used to bypass the direct calculation of the likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural networks (ANN), the Masked Autoregressive Flow (MAF) and the denoising autoencoder (DAE). Our procedure is the first to use DAE to extract features from data, in order to simplify the structure of MAF needed to estimate the posterior. Tested on simulated Hubble parameter data with a simple Gaussian likelihood, the procedure shows the capability of extracting features from data and estimating posterior distributions without the need of tractable likelihood. We demonstrate that it can accurately approximate the real posterior, achieve performance comparable to the traditional MCMC method, and the MAF gets better training results for small number of simulation when the DAE is added. We also discuss the application of the proposed procedure to OHD and Pantheon SN Ia data, and use them to constrain cosmological parameters from the non-flat $Λ$CDM model. For SNe Ia, we use fitted light curve parameters to find constraints on $H_0,Ω_m,Ω_Λ$ similar to relevant work, using less empirical distributions. In addition, this work is also the first to use Gaussian process in the procedure of OHD simulation.

astro-ph.CO↗