http://arxiv.org/abs/2207.00185
In this work, we propose the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $\Lambda$CDM and $w$CDM models using Type-Ia supernovae and power spectra of the cosmic microwave background. We find that the MDN method can achieve the same level of accuracy as the Markov Chain Monte Carlo method, with a slight difference of $\mathcal{O}(10^{-2}\sigma)$. Furthermore, the MDN method can provide accurate parameter estimates with $\mathcal{O}(10^3)$ forward simulation samples, which is useful for complex and resource-consuming cosmological models. This method can process either one data set or multiple data sets to achieve joint constraints on parameters, extendable for any parameter estimation of complicated models in a wider scientific field. Thus, MDN provides an alternative way for likelihood-free inference of parameters.
G. Wang, C. Cheng, Y. Ma, et. al.
Mon, 4 Jul 22
49/62
Comments: 17 pages, 4 tables, 15 figures, accepted by the Astrophysical Journal Supplement Series