arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1012.1591astro-ph.CO
收藏

重建暗能量:宇宙学参数的比较

Reconstructing Dark Energy : A Comparison of Cosmological Parameters

Alexander V. Pan, Ujjaini Alam

摘要:

为了获得关于暗能量本质的信息,人们提出了大量的宇宙学参数。在这项工作中,我们研究了这些不同的参数在区分暗能量的理论模型,使用目前可用的超新星(SNe)的数据,和未来的观测模拟的功效。我们发现,目前的数据并没有把强大的约束暗能量的性质,无论使用的宇宙学参数。对于未来的数据,我们发现,虽然减速参数可以准确地重建一些暗能量模型,它是无法区分不同的暗能量模型,因此限制了它的有用性。物理参数,如状态方程的暗能量,或暗能量密度做好重建和歧视,如果物质密度是已知的高精度。然而,物质密度的不确定性降低了这些参数的功效。最近提出的一个参数,Om(z),从SNe数据的一阶导数构造,在区分暗能量的不同理论模型方面非常有效,并且具有不依赖于物质密度值的额外优势。因此,我们发现,一个宇宙学参数构造的数据的一阶导数,其中的暗能量的理论模型是足够远离彼此,这是独立的物质密度,在重建暗能量从SNe数据表现最好。

英文摘要:

A large number of cosmological parameters have been suggested for obtaining information on the nature of dark energy. In this work, we study the efficacy of these different parameters in discriminating theoretical models of dark energy, using both currently available supernova (SNe) data, and simulations of future observations. We find that the current data does not put strong constraints on the nature of dark energy, irrespective of the cosmological parameter used. For future data, we find that the although deceleration parameter can accurately reconstruct some dark energy models, it is unable to discriminate between different models of dark energy, therefore limiting its usefulness. Physical parameters such as the equation of state of dark energy, or the dark energy density do a good job of both reconstruction and discrimination if the matter density is known to high accuracy. However, uncertainty in matter density reduces the efficacy of these parameters. A recently proposed parameter, Om(z), constructed from the first derivative of the SNe data, works very well in discriminating different theoretical models of dark energy, and has the added advantage of not being dependent on the value of matter density. Thus we find that a cosmological parameter constructed from the first derivative of the data, for which the theoretical models of dark energy are sufficiently distant from each other, and which is independent of the matter density, performs the best in reconstructing dark energy from SNe data.

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。

↑