论文标题
反宇宙学：使用机器学习测试磁线多项式的有效性
Inverse Cosmography: testing the effectiveness of cosmographic polynomials using machine learning
论文作者
论文摘要
宇宙学被称为解决反向散射问题的解决方案，这是合理的，因为它使我们能够通过在当前时间内进行宇宙学可观察物的扩展来计算数据采样器的宇宙学界限。然而，这种方法不能正确地是一种反散射解决方案，因为该方法仅通过用其cosomographic参数（多项式串联依赖性）替换其替换状态参数方程（EOS）参数方程（依赖模型）。因此，我们可以构建一种新的宇宙学方法，可以在其中拟合宇宙动力学参数，然后使用它们通过其通用杂志参数来分析运动学？无论如何，在文献中所有宇宙学提案所论述的无截断问题的情况下。在这项工作中，我们仅根据$ f_i（z）$的形式及其导数的形式提出通用的EOS，在此功能可以是任何多项式（模仿黑暗能量的术语），允许特定宇宙学密度的动力学。我们使用标准的宇宙学模型和多项式提案（例如Padé和Chebyshev近似值）测试通用EOS。有趣的是，在$ f（z）$ cdm的模型中，已经考虑了padé（2,2）的近似值，显示了$ z = 1 $的过渡。另外，我们发现这是不能保证的，因为具有这些特征的模型具有变性和截断问题，这些问题在此红移极限下实验了差异。为了探索我们的结果，我们还提供了一种新的Supernovae样品，该样本通过一个名为Recurrent-bayesian（RNN+BNN）网络训练，可以解决问题，因为它在较低的红移处过度拟合并增加了该区域的数据点的密度，可以在2- $σ$σ$σ$σ$σ$σ$σ$σ$σ$σ的情况下。
Cosmography has been referred to as a solution to the inverse scattering problem, which is reasonable since it allows us to calculate cosmological bounds from data samplers by performing an expansion of the cosmological observables around present time. Nevertheless, this approach is not properly an inverse scattering solution since the method only circumvents the problem to fit the equation of state (EoS) parameters (model-dependent) by replacing it with their fit of its cosmographic parameters (with a polynomial series-dependence). Therefore, can we construct a new cosmography approach where the cosmodynamical parameters can be fitted and then employ them to analyse the kinematics via its generic cosmographic parameters? By all means, without experimenting the problem of truncation of the series that all cosmography proposals in the literature argue. In this work we present a generic EoS depending solely on the form of $f_i(z)$ and its derivative is found, where this function can be any polynomial (mimicking a dark energy-like term) that allow the dynamics of a specific cosmological density. We test our generic EoS with standard cosmological models and with polynomials proposals as Padé and Chebyshev approximants. Interesting enough, a Padé (2,2) approximant has been considered inside a $f(z)$CDM-like model showing a transition in $z=1$. Also, we found that this is not assured since models with these characteristics have degeneracy and truncation problems that experiment a divergence at this redshift limit. To explore our results, also we present a new supernovae sample trained via a deep learning tool called Recurrent-Bayesian (RNN+BNN) network that can solve problems as overfitting at lower redshifts and increase the density of data points in this region, which can help to discern between cosmographies at 2-$σ$ of precision.
