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In this work, we present a new method to estimate cosmological parametersaccurately based on the artificial neural network (ANN), and a code calledECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieveparameter inference. We test the ANN method by estimating the basic parametersof the concordance cosmological model using the simulated temperature powerspectrum of the cosmic microwave background (CMB). The results show that theANN performs excellently on best-fit values and errors of parameters, as wellas correlations between parameters when compared with that of the Markov ChainMonte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capableof estimating parameters for multiple experiments that have differentprecisions, which can greatly reduce the consumption of time and computingresources for parameter inference. Furthermore, we extend the ANN to amultibranch network to achieve a joint constraint on parameters. We test themultibranch network using the simulated temperature and polarization powerspectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations, andalmost obtain the same results as the MCMC method. Therefore, we propose thatthe ANN can provide an alternative way to accurately and quickly estimatecosmological parameters, and ECoPANN can be applied to the research ofcosmology and even other broader scientific fields.

ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks