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arXiv 1306.4885astro-ph.CO
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探索暗能量本质的模型无关方法比较分析

Comparative analysis of model-independent methods for exploring the nature of dark energy

Savvas Nesseris, Juan Garcia-Bellido

摘要:

我们使用四种不同的模拟SnIa数据,对文献中提出的研究暗能量性质的各种独立方法进行了比较分析。特别是,我们探讨了一个通用的主成分分析方法,遗传算法,一系列的近似,如Padé幂律近似,和各种扩展的正交多项式,以及宇宙学,并比较它们与通常适合的模型与一个恒定的暗能量状态方程w。我们发现,根据模拟数据,一些方法在区分底层模型方面比其他方法更有效,尽管没有普遍更好的方法。

英文摘要:

We make a comparative analysis of the various independent methods proposed in the literature for studying the nature of dark energy, using four different mocks of SnIa data. In particular, we explore a generic principal components analysis approach, the genetic algorithms, a series of approximations like Padé power law approximants, and various expansions in orthogonal polynomials, as well as cosmography, and compare them with the usual fit to a model with a constant dark energy equation of state w. We find that, depending on the mock data, some methods are more efficient than others at distinguishing the underlying model, although there is no universally better method.

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。

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