论文标题
用神经网络在21厘米地图中删除天体物理学
Removing Astrophysics in 21 cm maps with Neural Networks
论文作者
论文摘要
从电离时期和宇宙黎明时期的21 cm信号中测量温度波动是在高红移时研究宇宙的最有希望的方法之一。不幸的是，21 cm信号以非平凡的方式受到宇宙学和天体物理学过程的影响。我们运行了1,000个数值模拟的套件，其主要天体物理参数值不同。从这些模拟中，我们在RedShifts $ 10 \ leq Z \ leq 20 $中生产数万cm地图。我们训练一个卷积神经网络，从21厘米地图中删除天体物理学的影响，以及基础物质领域的输出图。我们表明，我们的模型能够生成2D物质字段，这些字段不仅在视觉上类似于真实的字段，而且其统计属性与几个百分之几中的真实属性一致。我们证明我们的神经网络保留了天体物理信息，可用于限制天体物理参数的价值。最后，我们使用显着性图来尝试了解21 cm地图的哪些特征是网络使用的，以确定天体物理参数的值。
Measuring temperature fluctuations in the 21 cm signal from the Epoch of Reionization and the Cosmic Dawn is one of the most promising ways to study the Universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a non-trivial manner. We run a suite of 1,000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts $10\leq z\leq 20$. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps, and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields that not only resemble the true ones visually, but whose statistical properties agree with the true ones within a few percent down to pretty small scales. We demonstrate that our neural network retains astrophysical information, that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps is the network using in order to determine the value of the astrophysical parameters.
