ELUCID-DESI II. Revealing dark matter mass, tidal, and velocity (MTV) fields using galaxy group phase information
Qingyang Li, Xiaohu Yang, Wensheng Hong, Feng Shi, Youcai Zhang, Jiaqi Wang, Junde Li, Yiyang Guo, Yingxiao Song, Huiyuan Wang, Yan-Chuan Cai, Yizhou Gu, Chengze Liu, Jiaxin Han, Zhongxu Zhai, Yu Yu, Yipeng Jing, Houjun Mo, Yuyu Wang, Hao-Ran Yu, Yingjie Peng, Weiguang Cui, Qi Guo, Liang Gao, Xi Kang, Weipeng Lin, Jie Wang
Abstract
We introduce a novel method for reconstructing the cosmic mass, tidal, and velocity (MTV) fields over the redshift range 0 < z < 0.6 using the phase information of galaxy groups. This approach replaces the explicit theoretical bias correction typically needed to relate galaxy groups to the underlying dark matter density field with a simulation-calibrated statistical mapping, reducing a major source of systematic uncertainty and making the method directly applicable to spectroscopic redshift surveys such as the DESI Bright Galaxy Survey (BGS). We evaluate the performance of our MTV reconstruction pipeline with mock redshift surveys that include a comprehensive set of observational selection effects. The galaxy groups used as tracers are identified with an extended halo-based group finder applied to the DESI mock galaxy catalogue with an apparent magnitude limit of mz < 19.65, yielding a galaxy number comparable to that of the DESI BGS faint sample (mr < 20.175). Our tests show that the reconstructed velocities are accurate and unbiased, with a residual dispersion of 120\ km\,s-1 across the redshift bins. The recovered velocity field allows us to shift galaxy groups to their real-space positions, thereby correcting for the Kaiser effect. By iteratively applying this Kaiser correction to the galaxy groups, we further reconstruct the tidal field and the mass-density distribution. The reconstruction is stable with respect to the grid resolution. Overall, our results demonstrate that this group-based phase-space reconstruction provides a robust pathway to recovering the dark matter MTV fields, with strong prospects for application to DESI BGS data.
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