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 mult...

中文翻译：

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ECoPANN：使用人工神经网络估算宇宙学参数的框架

在这项工作中，我们提出了一种基于人工神经网络（ANN）准确估算宇宙学参数的新方法，并开发了一个名为ECoPANN（用ANN估算宇宙学参数）的代码来实现参数推断。我们通过使用宇宙微波背景（CMB）的模拟温度功率谱估计协和宇宙学模型的基本参数来测试ANN方法。结果表明，与马尔可夫链蒙特卡洛（MCMC）方法相比，人工神经网络在最佳拟合值和参数误差以及参数之间的相关性方面表现出色。此外，对于训练有素的ANN模型，它可以估算具有不同精度的多个实验的参数，这样可以大大减少时间消耗和参数推断的计算资源。此外，我们将ANN扩展到多分支网络以实现对参数的联合约束。我们测试了多重...