AI 中文总结
该研究针对21厘米强度映射的前景抑制问题，提出基于张量的O-SVD框架，在SKA模拟数据与天籁阵列实测数据上验证其性能，实现比传统SVD更优的高保真信号恢复效果。
AI 中文摘要
我们提出了一种原生基于张量的框架，用于21厘米强度映射（IM）中的前景抑制，该框架采用定向奇异值分解（O-SVD）算法。21厘米IM是探测宇宙大尺度结构的强大工具，但其效能受天体物理前景的严重限制，这些前景的亮度比宇宙学信号高出数个数量级。传统抑制策略常需将多维数据立方体展平为二维矩阵，此过程会将不同空间像素视为独立样本，可能破坏固有的空间-光谱相关性。通过将多频天图和角功率谱视为三阶张量，O-SVD方法直接在多线性流形上进行分解，保留潜在物理拓扑结构，并利用天体物理前景在不同维度上的独特相干特性。我们通过将O-SVD框架应用于平方公里阵列科学数据挑战3a（SDC3a）的高保真模拟数据，以及天籁圆柱探路者阵列的真实观测数据，验证了其性能与通用性。结果表明，O-SVD是一种稳健且通用的前景 subtraction方法，可实现高保真信号恢复，且相比传统基于矩阵的奇异值分解（SVD）方法表现更优。
英文摘要
We introduce a native tensor-based framework for foreground mitigation in 21\,cm intensity mapping (IM), utilizing the Oriented Singular Value Decomposition (O-SVD) algorithm. While 21\,cm IM is a powerful probe of the large-scale structure of the Universe, its efficacy is severely limited by astrophysical foregrounds that are orders of magnitude brighter than the cosmological signal. Traditional mitigation strategies often necessitate flattening multidimensional data cubes into two-dimensional matrices, a process that potentially compromises the intrinsic spatial-spectral correlations by treating distinct spatial pixels as independent samples. By treating multi-frequency sky maps and angular power spectra as third-order tensors, the O-SVD method performs decomposition directly on the multilinear manifold, preserving the underlying physical topology and leveraging the distinct coherence properties of astrophysical foregrounds across different dimensions. We demonstrate the performance and versatility of the O-SVD framework through its application to high-fidelity simulations from the SKA Science Data Challenge 3a (SDC3a) and real-world observational data from the Tianlai Cylinder Pathfinder Array. Our results indicate that O-SVD provides a robust and universal approach for foreground subtraction, achieving high-fidelity signal recovery while offering superior performance compared to conventional matrix-based Singular Value Decomposition (SVD) methods.
Comments16 pages
