The Reconstruction of Dark Energy with the Ridge Regression Approach

被引：3
作者：
Huang, Long [1 ,2 ]
Yang, Xiaofeng [1 ,3 ,4 ]
Liu, Xiang [1 ,3 ,4 ]
机构：
[1] Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 10039, Peoples R China
[3] Chinese Acad Sci, Key Lab Radio Astron, Nanjing 210008, Peoples R China
[4] Key Lab Radio Astrophys Xinjiang Prov, Urumqi 830011, Peoples R China
来源：
ASTROPHYSICAL JOURNAL | 2021年 / 913卷 / 01期
关键词：
HUBBLE-SPACE-TELESCOPE; DIGITAL SKY SURVEY; IA SUPERNOVAE; COSMOLOGICAL CONSTRAINTS; DISCOVERIES;
D O I：
10.3847/1538-4357/abf64a
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
Whether or not dark energy evolves with time may be determined by the nonparametric method. In order to avoid instability of the derivative for the functional data, we linearize the luminosity-distance integral formula in near-flat space by adopting Lagrange interpolation for the numerical integral, and proposing a method of combining principal component analysis (PCA) and biased estimation on the basis of ridge regression analysis to reconstruct the regression parameters. We also present a principal component selection criterion to better distinguish between ?CDM and w(z) not equal -1 models by reconstruction. We define the type I error as the situation where w (true) = -1 but w (recon) not equal -1, and the type II error as the situation where w (true) not equal -1 but w (recon) = -1; we use the various w(z) functions to test the method. The preliminary test results demonstrate that the PCA-biased method can be used to determine the most probable behavior of w(z). Finally, we apply this method to recent supernova measurements, reconstructing the continuous history of w(z) out to redshift z = 1.5.
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页数：6
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