无需看见波纹：利用深度学习在21厘米强度测绘中恢复大尺度结构
Seeing Wiggles without Seeing Wiggles: BAO Recovery in 21 cm Intensity Mapping with Deep Learning
AI总结：
本研究利用深度学习在21厘米强度测绘中恢复大尺度结构特征，特别是通过非线性模式耦合恢复BAO信息。
摘要：
21厘米强度测绘为研究大尺度结构提供了一种有前途的探测手段。天体物理前兆作为主要的污染源，由于射电干涉观测中固有的色散性，在傅里叶空间的楔形区域中持续存在。前兆规避策略专注于利用相对干净且前景泄漏最小的区域的数据，但代价是失去大尺度信息。然而，非线性结构形成却将傅里叶模式在不同尺度上耦合，使缺失的大尺度模式在剩余数据中留下印记。在本研究中，我们采用深度学习方法测试是否能仅利用短波长模式（超出线性尺度）来恢复21厘米亮度温度场的大尺度特征，特别是宇宙学大尺度结构（BAO）。为了明确评估训练宇宙学对结果的影响，我们仅在去波纹模拟数据上训练网络，提供了一个受控测试，以确定重建是否源于物理非线性模式耦合，而非隐含编码的BAO特征。在理想噪声无的情况下，丢失模式的振幅和相位可以高保真地恢复。在包含仪器噪声的情况下，重建的振幅会偏置，而相位信息仍保持稳健。训练后的网络也显示出对底层宇宙学模型变化的合理鲁棒性。这些结果表明，模式恢复为从未来21厘米强度测绘分析中提取宇宙学信息提供了一种互补的方法。
英文摘要：
The 21 cm intensity mapping provides a promising probe of the large-scale structure. Astrophysical foregrounds, as the main source of contamination to the cosmological 21 cm signal, persist in a wedge-like region of Fourier space due to the inherent chromaticity in radio interferometric observations. The foreground avoidance strategy focuses on utilizing data from relatively clean regions with minimal foreground leakage, at the cost of losing large-scale information. Non-linear structure formation, however, couples Fourier modes across scales, leaving imprints of the missing large-scale modes in the remaining data. In this work, we employ a deep learning approach based on Convolutional Neural Networks (CNNs) to test whether large-scale features of the 21 cm brightness temperature fields, particularly the baryon acoustic oscillations (BAO), can be recovered at the field level using only short-wavelength modes that are beyond the linear scales. To explicitly assess the dependence on the training cosmology, we train the network exclusively on de-wiggled simulations, providing a controlled test of whether the reconstruction arises from physical non-linear mode coupling rather than implicit encoding of BAO features. In the ideal noise-free case, the amplitude and phase of the lost modes can be restored with high fidelity. With instrumental noise included, the reconstructed amplitude becomes biased, while the phase information remains robust. The trained network also exhibits reasonable robustness to variations in the underlying cosmological model. Together, these results suggest that mode restoration offers a complementary approach for extracting cosmological information from future 21 cm intensity mapping analyses.
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