标题： 在真实空间和红移空间中映射莱曼-α森林的三维大尺度结构 显示英文标题

标题： Mapping Lyman-alpha forest three-dimensional large scale structure in real and redshift space

摘要： 这项工作提出了一种新的物理驱动的监督机器学习方法，Hydro-BAM，从参考流体动力学模拟中学习，以在真实空间和红移空间中再现三维Lyman-$\alpha$森林场，从而节省约7个数量级的计算时间。 我们表明，我们的方法在重构场的一点（PDF）、两点（功率谱）和三点（双谱）统计中准确度达到$k\sim1\,h\,\rm{Mpc}^{-1}$。 与包括红移空间畸变的参考模拟相比，我们的方法在单极子中的偏差为$\lesssim2\%$到$k=0.6\,h\,\rm{Mpc}^{-1}$，在四极子中的偏差为$\lesssim5\%$到$k=0.9\,h\,\rm{Mpc}^{-1}$。 对于边长达到$k=0.8\,h\,\rm{Mpc}^{-1}$的三角形配置，双谱被很好地再现。 相反，常用的波动 Gunn-Peterson 近似在忽略特殊运动的情况下，在双谱中边长为$k=0.2-0.4\,h\,\rm{Mpc}^{-1}$的配置中已显示出显著偏差，在功率谱中也明显不准确（在 5$\%$范围内直到$k=0.7\,h\,\rm{Mpc}^{-1}$）。 我们得出结论，对 Lyman-$\alpha$森林的准确分析需要考虑复杂的重子热力学大尺度结构关系。 我们的分层领域特定机器学习方法可以有效地利用这一点，并准备好生成覆盖 DESI 和 WEAVE 等调查所需大体积的准确 Lyman-$\alpha$森林模拟目录。 显示英文摘要

摘要： This work presents a new physically-motivated supervised machine learning method, Hydro-BAM, to reproduce the three-dimensional Lyman-$\alpha$ forest field in real and in redshift space learning from a reference hydrodynamic simulation, thereby saving about 7 orders of magnitude in computing time. We show that our method is accurate up to $k\sim1\,h\,\rm{Mpc}^{-1}$ in the one- (PDF), two- (power-spectra) and three-point (bi-spectra) statistics of the reconstructed fields. When compared to the reference simulation including redshift space distortions, our method achieves deviations of $\lesssim2\%$ up to $k=0.6\,h\,\rm{Mpc}^{-1}$ in the monopole, $\lesssim5\%$ up to $k=0.9\,h\,\rm{Mpc}^{-1}$ in the quadrupole. The bi-spectrum is well reproduced for triangle configurations with sides up to $k=0.8\,h\,\rm{Mpc}^{-1}$. In contrast, the commonly-adopted Fluctuating Gunn-Peterson approximation shows significant deviations already neglecting peculiar motions at configurations with sides of $k=0.2-0.4\,h\,\rm{Mpc}^{-1}$ in the bi-spectrum, being also significantly less accurate in the power-spectrum (within 5$\%$ up to $k=0.7\,h\,\rm{Mpc}^{-1}$). We conclude that an accurate analysis of the Lyman-$\alpha$ forest requires considering the complex baryonic thermodynamical large-scale structure relations. Our hierarchical domain specific machine learning method can efficiently exploit this and is ready to generate accurate Lyman-$\alpha$ forest mock catalogues covering large volumes required by surveys such as DESI and WEAVE.