3 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
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),…
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…
Redshift drift effect through the observation of HI 21cm signal with SKA
Jiangang Kang, Tong-Jie Zhang, Peng He +1
This study presents the findings of using the Square Kilometre Array (SKA) telescope to measure redshift drift via the HI 21cm signal, employing semi-annual observational interval…
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…
Reconstruction of the dark energy scalar field potential by Gaussian process
Jing Niu, Kang Jiao, Peng He +1
Dark energy is believed to be responsible for the acceleration of the universe. In this paper, we reconstruct the dark energy scalar field potential using the Hubble paramet…