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arXiv 2009.10673astro-ph.COphysics.data-an
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Deep Forest:Lyman-alpha森林的神经网络重建

Deep Forest: Neural Network reconstruction of the Lyman-alpha forest

Lawrence Huang, Rupert A. C. Croft, Hitesh Arora

摘要:

我们探索使用深度学习来从莱曼-阿尔法森林中可观测的透射通量推断物理量。我们训练一个神经网络使用红移z=3输出宇宙流体动力学模拟和模拟数据集构建从他们。我们评估如何训练网络能够重建莱曼α森林吸收的光学深度从嘈杂的,往往饱和的传输通量数据。神经网络优于包括对数反演和样条插值的替代重建方法,在光学深度均方根误差方面约为2倍。我们发现没有显着的依赖性,在输入数据的信噪比的改善,虽然增益是最大的高光学深度区域。这里研究的莱曼-阿尔法森林光学深度是一个简单的一维例子,但是使用深度学习和模拟来解决宇宙学中的逆问题可以扩展到其他物理量和更高维的数据。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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