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arXiv 1502.05952astro-ph.CO
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根据SNeIa,CMB,BAO和OHD观测数据集的相互作用标量张量宇宙学

Interacting scalar tensor cosmology in light of SNeIa, CMB, BAO and OHD observational data sets

Sayed Wrya Rabiei, Haidar Sheikhahmadi, Khaled Saaidi, Ali Aghamohammadi

摘要:

在这项工作中,一个相互作用的变色龙一样的标量场的情况下,考虑SNeIa,CMB,BAO和OHD数据集进行了研究。事实上,该研究是通过引入有效暗能量状态方程的一个近似来实现的,它模拟了变色龙模型的行为。在此基础上,分析了该机制中的一些宇宙学参数,包括哈勃参数、减速参数和符合参数。本文认识到,在估计理论模型的自由参数时,考虑系统误差时,最好考虑上述观测数据集的整体。事实上,如果考虑SNeIa、CMB和BAO,而不考虑OHD,可能会导致不同的结果。此外,为了在具有约束$χ_{\rm{m}}^2\leq 1$的计数器之间获得更好的重叠,可以重新加权$χ_{\rm{T}}^2$函数。根据二维置信水平$68.3\%$、$90\%$和$95.4\%$,绘制边缘化似然$\mathcal{L} (Ω_{\rm{m0}} ,ω_1, β)$的相对概率函数。同时,在上述置信水平下,求出使边缘似然最大化的自由参数值。此外,在此基础上,还得到了有效暗能量状态方程的自由参数$χ^2$的最小值。

英文摘要:

During this work, an interacting chameleon like scalar field scenario, by considering SNeIa, CMB, BAO and OHD data sets is investigated. In fact, the investigation is realized by introducing an ansatz for the effective dark energy equation of state, which mimics the behaviour of chameleon like models. Based on this assumption, some cosmological parameters including Hubble, deceleration and coincidence parameters in such mechanism are analysed. It is realized that, to estimate the free parameters of a theoretical model, by regarding the systematic errors it better the whole of the above observational data sets to be considered. In fact, if one considers SNeIa, CMB and BAO but disregards OHD it maybe leads to different results. Also to get a better overlap between the counters with the constraint $χ_{\rm{m}}^2\leq 1$, the $χ_{\rm{T}}^2$ function could be re-weighted. The relative probability functions are plotted for marginalized likelihood $\mathcal{L} (Ω_{\rm{m0}} ,ω_1, β)$ according to two dimensional confidence levels $68.3\%$, $90\%$ and $95.4\%$. Meanwhile, the value of free parameters which maximize the marginalized likelihoods using above confidence levels are obtained. In addition, based on these calculations the minimum value of $χ^2$ based on free parameters of an ansatz for the effective dark energy equation of state are achieved.

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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