论文标题
通过主要成分分析的宇宙学模型的推断
Inference of cosmological models with principal component analysis
论文作者
论文摘要
宇宙学参数的确定目前是宇宙学的主要目标。改进的数据集的可用性需要开发新颖的统计工具来解释宇宙学模型的推论。在本文中，我们结合了主要成分分析（PCA）和马尔可夫链蒙特卡洛（MCMC）方法，以推断宇宙学模型的参数。我们使用NO掉头采样器（螺母）在模型参数空间中运行MCMC链。在确定PCA可观察到的可观察到的后，我们用可观察的PCA重构可观察到的可能性分析的观察性和误差部分，并找到最优选的模型参数集。作为我们方法论的证明，我们假设多项式扩展是状态的暗能量方程的参数化，并将其插入重建算法中作为我们的模型。在使用模拟数据测试我们的方法之后，我们将其应用于观察到的数据集，哈勃参数数据，超新星类型IA数据和Baryon声学振荡数据。此方法有效地从数据（包括稀疏数据集）中限制了宇宙学参数。
Determination of cosmological parameters is a major goal in cosmology at present. The availability of improved data sets necessitates the development of novel statistical tools to interpret the inference from a cosmological model. In this paper, we combine the Principal Component Analysis (PCA) and Markov Chain Monte Carlo (MCMC) method to infer the parameters of cosmological models. We use the No U-Turn Sampler (NUTS) to run the MCMC chains in the model parameter space. After determining the observable by PCA, we replace the observational and error parts of the likelihood analysis with the PCA reconstructed observable and find the most preferred model parameter set. As a demonstration of our methodology, we assume a polynomial expansion as the parametrization of the dark energy equation of state and plug it in the reconstruction algorithm as our model. After testing our methodology with simulated data, we apply the same to the observed data sets, the Hubble parameter data, Supernova Type Ia data, and the Baryon Acoustic oscillation data. This method effectively constrains cosmological parameters from data, including sparse data sets.
