论文标题
深森林：Lyman-Alpha森林的神经网络重建
Deep Forest: Neural Network reconstruction of the Lyman-alpha forest
论文作者
论文摘要
我们探讨了从Lyman-Alpha森林中可观察到的可观察到的助焊剂中推断出物理量的使用。我们使用红移Z = 3个输出的宇宙流体动力模拟和模拟数据集训练神经网络。我们评估了受过训练的网络能够从嘈杂且经常饱和的传输通量数据中重建莱曼 - 阿尔法森林吸收的光学深度。神经网络的表现优于一种涉及对数反转和样条插值的替代重建方法，在光学深度根均方根误差中约为2倍。尽管在高光学深度区域中的增益最大，但我们发现对输入数据信号的改善对噪声的改善没有显着依赖性。这里研究的Lyman-Alpha森林光学深度是一个简单的，一个维度的例子，但是使用深度学习和模拟来解决宇宙学中的反问题，可以扩展到其他物理量和更高的维度数据。
We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock datasets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Lyman-alpha forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Lyman-alpha forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data.
