论文标题
使用主成分分析重建延迟宇宙学
Reconstruction of late-time cosmology using Principal Component Analysis
论文作者
论文摘要
我们使用主成分分析技术（PCA）重建延迟宇宙学。特别是，我们关注从两个不同的观察数据集，超新星类型IA数据和哈勃参数数据的状态的暗能量方程重建。分析以两种不同的方法进行。第一个是一种派生方法，我们在其中使用PCA重建可观察的数量，然后构建状态参数方程。另一种方法是从数据中直接重建状态方程。 PCA算法和相关系数的计算的组合用作重建的主要工具。我们使用模拟数据以及实际数据进行分析。发现派生的方法在统计学上比直接方法更可取。重建的状态方程表示暗能状态的变化缓慢。
We reconstruct late-time cosmology using the technique of Principal Component Analysis (PCA). In particular, we focus on the reconstruction of the dark energy equation of state from two different observational data-sets, Supernovae type Ia data, and Hubble parameter data. The analysis is carried out in two different approaches. The first one is a derived approach, where we reconstruct the observable quantity using PCA and subsequently construct the equation of state parameter. The other approach is the direct reconstruction of the equation of state from the data. A combination of PCA algorithm and calculation of correlation coefficients are used as prime tools of reconstruction. We carry out the analysis with simulated data as well as with real data. The derived approach is found to be statistically preferable over the direct approach. The reconstructed equation of state indicates a slowly varying equation of state of dark energy.
