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arXiv 2301.04586astro-ph.COgr-qc
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AI辅助从暗物质晕的红移空间空间分布重建宇宙速度场

AI-assisted reconstruction of cosmic velocity field from redshift-space spatial distribution of halos

Ziyong Wu, Liang Xiao, Xu Xiao, Jie Wang, Xi Kang, Yang Wang, Xin Wang, Le Zhang, Xiao-Dong Li

AI总结:

本研究利用UNet深度学习网络从暗物质晕的红移空间分布重建peculiar速度场,在k≤1.1 h/Mpc下以约80%精度恢复各分量功率谱,为校正红移空间畸变提供新方法。

摘要:

暗物质晕的 peculiar 速度对于研究宇宙学和星系演化中的许多问题至关重要。在本研究中,我们利用最先进的深度学习技术——基于 UNet 的神经网络,提出从暗物质晕的红移空间分布重建 peculiar 速度场。通过逐点比较和检查各种统计性质,我们证明重建的速度场与真实值高度一致。当 $k\lesssim 1.1$ $h/\rm Mpc$(模拟的奈奎斯特频率)时,速度场的各个分量(包括速度大小、散度和涡度)的功率谱能够以约80%的精度成功恢复。这种方法非常有前景,提供了一种利用测量的晕三维空间信息来校正红移空间畸变的替代方法。此外,对于晕动量场的重建,UNet 也取得了类似的良好结果。因此,它在宇宙学的各个方面应用非常广泛,例如校正红移误差、改善宇宙网结构、动力学 Sunyaev-Zel'dovich 效应、BAO 重建等的测量。

英文摘要:

The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar velocity field from the redshift-space distribution of dark matter halos. Through a point-to-point comparison and examination of various statistical properties, we demonstrate that, the reconstructed velocity field is in good agreement with the ground truth. The power spectra of various velocity field components, including velocity magnitude, divergence and vorticity, can be successfully recovered when $k\lesssim 1.1$ $h/\rm Mpc$ (the Nyquist frequency of the simulations) at about 80% accuracy. This approach is very promising and presents an alternative method to correct the redshift-space distortions using the measured 3D spatial information of halos. Additionally, for the reconstruction of the momentum field of halos, UNet achieves similar good results. Hence the applications in various aspects of cosmology are very broad, such as correcting redshift errors and improving measurements in the structure of the cosmic web, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, etc.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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