221 citations
- Beijing Normal UniversityCN15 papers
- Dezhou UniversityCN9 papers
- Nanjing University of Aeronautics and AstronauticsCN9 papers
- Chinese Academy of SciencesCN8 papers
- Beijing Computational Science Research CenterCN6 papers
- Southeast UniversityCN6 papers
- Toronto Metropolitan UniversityCA6 papers
- Soochow UniversityCN5 papers
- Zhejiang UniversityCN5 papers
- Nanjing University of Posts and TelecommunicationsCN4 papers
- National University of SingaporeSG4 papers
- Peking UniversityCN4 papers
14 papers · 1 filter
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…
Cosmological neutrino N-body simulations of dark matter halo
Yu Chen, Chang-Zhi Lu, Juan Li +3
The study of massive neutrinos and their interactions is a critical aspect of contemporary cosmology. Recent advances in parallel computation and high-performance computing provide…
Effect of Neutrinos on Angular Momentum of Dark Matter Halo
Yu Chen, Chang-Zhi Lu, Yu Lu +2
Massive neutrinos are expected to affect the large-scale structure formation, including the major component of solid substances, dark matter halos. How halos are influenced by neut…
Novel Cosmological Joint Constraints in Multidimensional Observables Space with Redshift-free Inferences
Wei Hong, Kang Jiao, Yu-Chen Wang +2
Cosmology constraints serve as a crucial criterion in discriminating cosmological models. The traditional combined method to constrain the cosmological parameters designates the co…
Kernel Selection for Gaussian Process in Cosmology: with Approximate Bayesian Computation Rejection and Nested Sampling
Hao Zhang, Yu-Chen Wang, Tong-Jie Zhang +1
Gaussian Process (GP) has gained much attention in cosmology due to its ability to reconstruct cosmological data in a model-independent manner. In this study, we compare two method…
Test of Artificial Neural Networks in Likelihood-free Cosmological Constraints: A Comparison of IMNN and DAE
Jie-Feng Chen, Yu-Chen Wang, Tingting Zhang +1
In the procedure of constraining the cosmological parameters with the observational Hubble data and the type Ia supernova data, the combination of Masked Autoregressive Flow and De…