论文标题
使用小波散射探索从回报时期探索宇宙21-CM信号
Exploring the cosmic 21-cm signal from the Epoch of Reionisation using the Wavelet Scattering Transform
论文作者
论文摘要
在回报和宇宙黎明时期检测宇宙21-CM信号将揭示对第一个星系的特性的见解，并提高宇宙学参数估计。直到最近，来自21-CM信号的天体物理参数推断的主要重点（PS）。然而，宇宙21-CM信号是高度非高斯的，呈现PS亚次数以表征宇宙信号。在这项工作中，我们引入了一种新技术，以分析21 cm信号的图像中的非高斯信息，称为小波散射变换（WST）。这种方法密切反映了卷积神经网络的额外优势，即不需要对神经网络进行调整或培训。取而代之的是，它将2D空间信息压缩到一组系数中，从而更容易解释，同时还提供了对宇宙21-CM信号中包含的非高斯信息的强大统计描述。首先，我们通过与21-CM PS的已知行为进行比较，探索WST在模拟21 cm图像中的应用，以获得有价值的物理见解。然后，我们通过使用从平方公里阵列中的1000 HR模拟观察到现实的1000小时模拟观察中提取天体物理参数约束来定量探索应用于21 cm信号的WST。我们发现：（i）仅适用于2D图像的WST可以胜过3D球的平均21厘米PS，（ii）前景污染模式的切除可以使约束功率与WST和（iii）21厘米图像之间的较高的成果降低约1.5-2的因子可以进一步提高约束功能。
Detecting the cosmic 21-cm signal during the Epoch of Reionisation and Cosmic Dawn will reveal insights into the properties of the first galaxies and advance cosmological parameter estimation. Until recently, the primary focus for astrophysical parameter inference from the 21-cm signal centred on the power spectrum (PS). However, the cosmic 21-cm signal is highly non-Gaussian rendering the PS sub-optimal for characterising the cosmic signal. In this work, we introduce a new technique to analyse the non-Gaussian information in images of the 21-cm signal called the Wavelet Scattering Transform (WST). This approach closely mirrors that of convolutional neural networks with the added advantage of not requiring tuning or training of a neural network. Instead, it compresses the 2D spatial information into a set of coefficients making it easier to interpret while also providing a robust statistical description of the non-Gaussian information contained in the cosmic 21-cm signal. First, we explore the application of the WST to mock 21-cm images to gain valuable physical insights by comparing to the known behaviour from the 21-cm PS. Then we quantitatively explore the WST applied to the 21-cm signal by extracting astrophysical parameter constraints using Fisher Matrices from a realistic 1000 hr mock observation with the Square Kilometre Array. We find that: (i) the WST applied only to 2D images can outperform the 3D spherically averaged 21-cm PS, (ii) the excision of foreground contaminated modes can degrade the constraining power by a factor of ~1.5-2 with the WST and (iii) higher cadences between the 21-cm images can further improve the constraining power.
