一种重建21厘米层析数据的生成建模方法
A Generative Modeling Approach to Reconstructing 21-cm Tomographic Data
AI总结：
本文提出基于随机插值的深度生成模型，利用21厘米信号的非高斯性质重建被楔形滤波去除的数据，有效恢复三维光锥空间信息，并展望其在宇宙黎明与再电离研究中的应用。
摘要：
宇宙21厘米信号的分析受到天体物理前景的阻碍，这些前景远比信号本身强。这些前景通常局限于傅里叶空间中的一个楔形区域，往往需要移除绝大多数模式，从而各向异性地降低数据质量。为了应对这一挑战，我们引入了一种基于随机插值的新型深度生成模型，以重建因楔形滤波而丢失的21厘米数据。我们的方法利用21厘米信号的非高斯性质，有效地将经过楔形滤波的三维光锥映射到楔形恢复光锥的条件分布样本。我们展示了该方法在考虑不同宇宙学初始条件和天体物理学的情况下，能够有效恢复空间信息。此外，我们讨论了该方法未来可应用于21厘米信号分析的若干途径，可能为增进我们对宇宙黎明和再电离时期宇宙的理解提供新的机会。
英文摘要：
Analyses of the cosmic 21-cm signal are hampered by astrophysical foregrounds that are far stronger than the signal itself. These foregrounds, typically confined to a wedge-shaped region in Fourier space, often necessitate the removal of a vast majority of modes, thereby degrading the quality of the data anisotropically. To address this challenge, we introduce a novel deep generative model based on stochastic interpolants to reconstruct the 21-cm data lost to wedge filtering. Our method leverages the non-Gaussian nature of the 21-cm signal to effectively map wedge-filtered 3D lightcones to samples from the conditional distribution of wedge-recovered lightcones. We demonstrate how our method is able to restore spatial information effectively, considering both varying cosmological initial conditions and astrophysics. Furthermore, we discuss a number of future avenues where this approach could be applied in analyses of the 21-cm signal, potentially offering new opportunities to improve our understanding of the Universe during the epochs of cosmic dawn and reionization.
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