activity
20192025
most citedLikelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SNe Ia

29 citations · 38 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.CO2025

Exploring the Cosmological Model Degeneracy with a new evaluate factor G

Yuan-bo Xie, Yun-dong Wu, Wei Hong +1

In the context of fitting cosmological models, parameter degeneracy remains a central issue. This paper critically examines traditional methods for constraining parameters and focu…

astro-ph.CO2025

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

Wen-Fei Liu, Yuan-Bo Xie, Zhi-E Liu +4

We present constraints on the baryonic matter density parameter, , within the framework of the CDM model. Our analysis utilizes observational data on the effective optical…

astro-ph.CO2023★ 9 cited

A Non-parametric Reconstruction of the Hubble Parameter Based on Radial Basis Function Neural Networks

Jian-Chen Zhang, Yu Hu, Kang Jiao +5

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 t…

astro-ph.CO2020★ 29 cited

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

Yu-Chen Wang, Yuan-Bo Xie, Tong-Jie Zhang +3

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…

astro-ph.CO2019

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

Yuan-Bo Xie, Cheqiu Lyu, Zhiyuan Mo +2

We study the impact of imposing an infinite-future condition on Gaussian-process (GP) reconstructions of from 37 cosmic-chronometer measurements. Implementing the asymptotic…