4 papers
Comparative Analysis of EMCEE, Gaussian Process, and Masked Autoregressive Flow in Constraining the Hubble Constant Using Cosmic Chronometers Dataset
Jing Niu, Jie-Feng Chen, Peng He +2
The Hubble constant () is essential for understanding the universe's evolution. Different methods, such as Affine Invariant Markov chain Monte Carlo Ensemble sampler (EMCEE),…
Redshift-binned constraints on the Hubble constant under CDM, CPL, and Padé cosmography
Zhi-Yuan Mo, Kang Jiao, Tong-Jie Zhang
Motivated by recent claims of a possible redshift dependence in late-Universe determinations of the Hubble constant (H_0), we test the robustness of this behavior using multiple co…
Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks
Jing Niu, Peng He, Tong-Jie Zhang
The Hubble parameter, , plays a crucial role in understanding the expansion history of the universe and constraining the Hubble constant, . The Cosmic Chronomet…
Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z)
Jie-feng Chen, Tong-Jie Zhang, Peng He +2
In this work, we reconstruct the H(z) based on observational Hubble data with Artificial Neural Network, then estimate the cosmological parameters and the Hubble constant. The trai…