论文标题
使用21厘米层析成像的深度学习与平方公里阵列来限制电离历史
Constraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
论文作者
论文摘要
即将进行的21厘米调查使用SKA1-LOW望远镜进行调查，将使早期宇宙中宇宙学尺度上的中性氢分布进行成像。这些调查有望生成庞大的成像数据集，这些数据集将比功率谱部编码更多的信息。这提供了限制电离历史的另一种独特方法，这可能会破坏功率谱分析中的堕落。使用卷积神经网络（CNN），我们从21厘米地图中创建了一个快速的中性分数，这是由我们的大型半数字模拟产生的。我们的估计器能够在几个红移中有效地恢复中性分数（$ x _ {\ rm hi} $），高精度为99 \％，并通过确定系数$ r^{2} $量化。从SKA设计中添加仪器效应会稍微增加损失功能，但是尽管如此，我们仍然能够以98 \％的相似高精度恢复中性分数，仅少1％。尽管观察到对红移的依赖性弱，但随着中性分数的降低，准确性迅速增加。这是由于以下事实：在宇宙高度中立的情况下，仪器噪声朝着高红移增加。我们的结果表明，直接使用21厘米 - 摩擦术以独立模型的方式来限制电离历史的希望，例如通过{\ it planck}从宇宙微波背景（CMB）观察到的光学深度测量值进行补充。
Upcoming 21cm surveys with the SKA1-LOW telescope will enable imaging of the neutral hydrogen distribution on cosmological scales in the early Universe. These surveys are expected to generate huge imaging datasets that will encode more information than the power spectrum. This provides an alternative unique way to constrain the reionization history, which might break the degeneracy in the power spectral analysis. Using Convolutional Neural Networks (CNN), we create a fast estimator of the neutral fraction from the 21cm maps that are produced by our large semi-numerical simulation. Our estimator is able to efficiently recover the neutral fraction ($x_{\rm HI}$) at several redshifts with a high accuracy of 99\% as quantified by the coefficient of determination $R^{2}$. Adding the instrumental effects from the SKA design slightly increases the loss function, but nevertheless we are still able to recover the neutral fraction with a similar high accuracy of 98\%, which is only 1 per cent less. While a weak dependence on redshift is observed, the accuracy increases rapidly with decreasing neutral fraction. This is due to the fact that the instrumental noise increases towards high redshift where the Universe is highly neutral. Our results show the promise of directly using 21cm-tomography to constrain the reionization history in a model independent way, complementing similar efforts, such as those of the optical depth measurements from the Cosmic Microwave Background (CMB) observations by {\it Planck}.
