论文标题
部分可观测时空混沌系统的无模型预测
LyMAS reloaded: improving the predictions of the large-scale Lyman-α forest statistics from dark matter density and velocity fields
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
We present LyMAS2, an improved version of the "Lyman-α Mass Association Scheme" aiming at predicting the large-scale 3d clustering statistics of the Lyman-α forest (Ly-α) from moderate resolution simulations of the dark matter (DM) distribution, with prior calibrations from high resolution hydrodynamical simulations of smaller volumes. In this study, calibrations are derived from the Horizon-AGN suite simulations, (100 Mpc/h)^3 comoving volume, using Wiener filtering, combining information from dark matter density and velocity fields (i.e. velocity dispersion, vorticity, line of sight 1d-divergence and 3d-divergence). All new predictions have been done at z=2.5 in redshift-space, while considering the spectral resolution of the SDSS-III BOSS Survey and different dark matter smoothing (0.3, 0.5 and 1.0 Mpc/h comoving). We have tried different combinations of dark matter fields and found that LyMAS2, applied to the Horizon-noAGN dark matter fields, significantly improves the predictions of the Ly-α 3d clustering statistics, especially when the DM overdensity is associated with the velocity dispersion or the vorticity fields. Compared to the hydrodynamical simulation trends, the 2-point correlation functions of pseudo-spectra generated with LyMAS2 can be recovered with relative differences of ~5% even for high angles, the flux 1d power spectrum (along the light of sight) with ~2% and the flux 1d probability distribution function exactly. Finally, we have produced several large mock BOSS spectra (1.0 and 1.5 Gpc/h) expected to lead to much more reliable and accurate theoretical predictions.