Corpus ID: 218763212

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

@article{Wang2020LikelihoodfreeCC,
  title={Likelihood-free Cosmological Constraints with Artificial Neural Networks: An Application on Hubble Parameters and SN Ia},
  author={Yu-Chen Wang and Yuan-bo Xie and Tongjie Zhang and Huichao Huang and Ting-ting Zhang and Kun Liu Department of Physics and Beijing Normal University and Beijing and China. and Department of Physics Astronomy and College of Command and Control Engineering and Pla Army Engineering University and Nanjing},
  journal={arXiv: Cosmology and Nongalactic Astrophysics},
  year={2020}
}
  • Yu-Chen Wang, Yuan-bo Xie, +11 authors Nanjing
  • Published 2020
  • Physics
  • arXiv: Cosmology and Nongalactic Astrophysics
  • The uncertainty of cosmological data generated from complex processes, such as 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. This necessitates the use of likelihood-free inference, which bypasses the direct calculation of likelihood. In this paper, we propose a new procedure to perform likelihood-free cosmological inference using two artificial neural… CONTINUE READING

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