ECoPANN：一个用人工神经网络估算宇宙学参数的框架
ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks
摘要：
在这项工作中，我们提出了一种新的方法来精确估计宇宙学参数的人工神经网络（ANN）的基础上，和一个名为ECoPANN（估计宇宙学参数与ANN）的代码开发，以实现参数推断。利用模拟得到的宇宙微波背景（CMB）温度功率谱，对协调宇宙学模型的基本参数进行了估计，并对人工神经网络方法进行了检验。结果表明，人工神经网络的最佳拟合值和参数的误差，以及参数之间的相关性相比，马尔可夫链蒙特卡罗（MCMC）方法表现优异。此外，对于一个训练有素的神经网络模型，它能够估计多个实验，具有不同的精度，这可以大大减少时间和计算资源的参数推断的消耗。此外，我们扩展的人工神经网络的多分支网络，以实现联合约束的参数。我们用CMB、Ia型超新星和重子声学振荡的模拟温度和偏振功率谱对多分支网络进行了检验，得到了与MCMC方法几乎相同的结果。因此，我们认为人工神经网络可以为准确、快速地估计宇宙学参数提供一种替代方法，而ECoPANN可以应用于宇宙学乃至其他更广泛的科学领域的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
