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arXiv 2204.02544astro-ph.CO
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使用小波散射变换探索来自再电离时代的宇宙21厘米信号

Exploring the cosmic 21-cm signal from the Epoch of Reionisation using the Wavelet Scattering Transform

Bradley Greig, Yuan-Sen Ting, Alexander A. Kaurov

摘要:

在Reionisation和Cosmic Dawn时期探测宇宙21厘米信号将揭示对第一个星系性质的见解,并推进宇宙学参数估计。直到最近,从21厘米信号推断天体物理参数的主要焦点集中在功率谱上。然而,宇宙21-cm信号是高度非高斯的,使得PS对于表征宇宙信号是次优的。在这项工作中,我们介绍了一种新的技术来分析图像中的非高斯信息的21厘米的信号称为小波散射变换(WST)。这种方法与卷积神经网络的方法非常相似,其额外的优点是不需要调整或训练神经网络。相反,它将2D空间信息压缩成一组系数,使其更容易解释,同时还提供了对宇宙21 cm信号中包含的非高斯信息的鲁棒统计描述。首先,我们探讨应用WST模拟21厘米的图像,以获得有价值的物理见解,通过比较从21厘米PS的已知行为。然后,我们定量探索WST应用到21厘米的信号提取天体物理参数的约束,使用Fisher矩阵从一个现实的1000小时模拟观测与平方公里阵列。我们发现:(i)仅应用于2D图像的WST可以优于3D球形平均21 cm PS,(ii)前景污染模式的切除可以使WST的约束能力降低约1.5-2倍,以及(iii)21 cm图像之间的更高节奏可以进一步提高约束能力。

英文摘要:

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.

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