ECoPANN: A Framework for Estimating Cosmological Parameters using
  Artificial Neural Networks
  In this work, we present a new method to estimate cosmological parameters
accurately based on the artificial neural network (ANN), and a code called
ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve
parameter inference. We test the ANN method by estimating the basic parameters
of the concordance cosmological model using the simulated temperature power
spectrum of the cosmic microwave background (CMB). The results show that the
ANN performs excellently on best-fit values and errors of parameters, as well
as correlations between parameters when compared with that of the Markov Chain
Monte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capable
of estimating parameters for multiple experiments that have different
precisions, which can greatly reduce the consumption of time and computing
resources for parameter inference. Furthermore, we extend the ANN to a
multibranch network to achieve a joint constraint on parameters. We test the
multibranch network using the simulated temperature and polarization power
spectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations, and
almost obtain the same results as the MCMC method. Therefore, we propose that
the ANN can provide an alternative way to accurately and quickly estimate
cosmological parameters, and ECoPANN can be applied to the research of
cosmology and even other broader scientific fields.
