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arXiv 1709.08407gr-qchep-th
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$f(R)$宇宙学的Padé多项式有理逼近

Rational approximations of $f(R)$ cosmography through Padé polynomials

Salvatore Capozziello, Rocco D' Agostino, Orlando Luongo

摘要:

我们考虑采用多项式重建技术的高红移$f(R)$宇宙学。代替考虑泰勒治疗,这是非预测性的,只要$z>1$,我们考虑到Padé有理逼近,其中包括在执行扩展收敛在高红移域。特别地,我们的策略是首先重建$f(z)$函数,假设Ricci标量相对于红移$z$是可逆的。有了这样得到的$f(z)$函数,我们将它们反转,并且我们容易地得到相应的$f(R)$项。我们尽量减少误差传播,假设没有错误的红移数据。我们遵循的治疗自然导致评估曲率压力,密度和状态方程,表征宇宙演化的红移远高于标准的宇宙学方法。因此,我们将这些结果与通过围绕$z\simeq 0$的泰勒级数构建$f(R)$宇宙学得到的小红移约束相匹配。这就产生了一个小的红移,使多项式近似的定义高达$z\simeq 10$的校准程序。最后但并非最不重要的是,我们显示出与标准宇宙学模型的差异,走向扩展的$Λ$ CDM范式,表明一个有效的暗能量长期演变的时间。最后,我们描述了我们的有效暗能量项的演变数据挖掘的基本技术手段。

英文摘要:

We consider high-redshift $f(R)$ cosmography adopting the technique of polynomial reconstruction. In lieu of considering Taylor treatments, which turn out to be non-predictive as soon as $z>1$, we take into account the Padé rational approximations which consist in performing expansions converging at high redshift domains. Particularly, our strategy is to reconstruct $f(z)$ functions first, assuming the Ricci scalar to be invertible with respect to the redshift $z$. Having the thus-obtained $f(z)$ functions, we invert them and we easily obtain the corresponding $f(R)$ terms. We minimize error propagation, assuming no errors upon redshift data. The treatment we follow naturally leads to evaluating curvature pressure, density and equation of state, characterizing the universe evolution at redshift much higher than standard cosmographic approaches. We therefore match these outcomes with small redshift constraints got by framing the $f(R)$ cosmology through Taylor series around $z\simeq 0$. This gives rise to a calibration procedure with small redshift that enables the definitions of polynomial approximations up to $z\simeq 10$. Last but not least, we show discrepancies with the standard cosmological model which go towards an extension of the $Λ$CDM paradigm, indicating an effective dark energy term evolving in time. We finally describe the evolution of our effective dark energy term by means of basic techniques of data mining.

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