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arXiv 2312.03469astro-ph.COastro-ph.GA
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利用神经网络估计单个星系的视线速度 I. 大尺度红移空间畸变建模

Estimation of line-of-sight velocities of individual galaxies using neural networks I. Modelling redshift-space distortions at large scales

Hongxiang Chen, Jie Wang, Tianxiang Mao, Juntao Ma, Yuxi Meng, Baojiu Li, Yan-Chuan Cai, Mark Neyrinck, Bridget Falck, Alexander S. Szalay

AI总结:

本文提出利用人工神经网络基于红移空间星系分布的环境特征预测单个星系视线速度,从而直接消除RSD效应,在大尺度上高精度恢复实空间相关函数和功率谱,并适用于不同宇宙学模型和星系样本。

摘要:

我们提出了一种基于人工神经网络(ANN)的方案,用于从观测到的红移空间星系分布中估计单个星系的视线速度。我们根据星系周围壳层中的星系计数和质心来估计该星系处的本动速度。通过利用环境特征(例如红移空间中每个星系周围每个壳层内的总质量和质量中心)训练网络,我们的ANN模型能够准确预测每个单个星系的视线速度。当利用该速度消除红移空间畸变(RSD)效应时,与真实值相比,实空间两点相关函数(TPCF)在s > 8 h^{-1}Mpc尺度上的恢复精度优于1%,在所有尺度上为4%。实空间功率谱在k < 0.5 Mpc^{-1}h范围内可在3%以内恢复,对所有k模的恢复误差小于5%。TPCF或功率谱的四极矩在s = 10 h^{-1}Mpc以下或所有k模上几乎为零,表明RSD效应引起的空间各向异性得到了有效校正。我们证明,在大尺度上,无需使用新数据进行额外训练,我们的网络即可适应不同的星系形成模型、不同的宇宙学模型以及高红移和高偏袒的模拟星系样本,在大于15 h^{-1}Mpc的尺度上实现小于10%的误差。由于该网络对大尺度密度敏感,它无法消除大星系团中的“上帝之指”效应,但在恢复其他区域的实空间星系位置方面表现非常出色。我们的方案提供了一种新颖的方法来预测单个星系的本动速度,在未来的大型星系巡天中直接消除RSD效应,并准确重建三维宇宙速度场。

英文摘要:

We present a scheme based on artificial neural networks (ANN) to estimate the line-of-sight velocities of individual galaxies from an observed redshift-space galaxy distribution. We find an estimate of the peculiar velocity at a galaxy based on galaxy counts and barycenters in shells around it. By training the network with environmental characteristics, such as the total mass and mass center within each shell surrounding every galaxy in redshift space, our ANN model can accurately predict the line-of-sight velocity of each individual galaxy. When this velocity is used to eliminate the RSD effect, the two-point correlation function (TPCF) in real space can be recovered with an accuracy better than 1% at $s$ > 8 $h^{-1}\mathrm{Mpc}$, and 4% on all scales compared to ground truth. The real-space power spectrum can be recovered within 3% on $k$< 0.5 $\mathrm{Mpc}^{-1}h$, and less than 5% for all $k$ modes. The quadrupole moment of the TPCF or power spectrum is almost zero down to $s$ = 10 $h^{-1}\mathrm{Mpc}$ or all $k$ modes, indicating an effective correction of the spatial anisotropy caused by the RSD effect. We demonstrate that on large scales, without additional training with new data, our network is adaptable to different galaxy formation models, different cosmological models, and mock galaxy samples at high redshifts and high biases, achieving less than 10% error for scales greater than 15 $h^{-1}\mathrm{Mpc}$. As it is sensitive to large-scale densities, it does not manage to remove Fingers of God in large clusters, but works remarkably well at recovering real-space galaxy positions elsewhere. Our scheme provides a novel way to predict the peculiar velocity of individual galaxies, to eliminate the RSD effect directly in future large galaxy surveys, and to reconstruct the 3-D cosmic velocity field accurately.

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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