宇宙速度场拉格朗日重建中的观测偏差
Observational biases in Lagrangian reconstructions of cosmic velocity fields
摘要：
大尺度奇异速度场的拉格朗日重建会受到观测偏差的强烈影响。我们开发了一个彻底的分析，这些系统的影响，依靠特别选择的模拟目录。为了本文的目的，我们使用MAK重建方法，尽管任何其他拉格朗日重建方法应该对相同的问题敏感。我们广泛地研究了由于光度的不完全性，以及质量和光度之间的关系不确定的质量到光分配的不确定性。红移失真校正的影响进行了分析的上下文中的MAK和我们检查的重要性，边缘和有限体积效应的重建速度。利用三个平均密度不同的模拟星表，我们还研究了宇宙方差的影响。特别是，其中一个提出了相同的全球功能中发现的观测目录，扩展到80 Mpc/h的规模。我们给食谱，检查使用上述模拟目录，以处理这些特殊的观测效果后，将它们引入模拟目录，以便定量地模仿最密集的采样目前可用的星系目录附近的宇宙。一旦已经考虑到偏差，重建速度中的典型结果误差通常为总体速度分散的大约四分之一，并且没有显著的偏差。最后，我们对重建误差进行建模，提出了一种改进的贝叶斯方法，通过将重建的速度与距离空间中的测量速度进行比较，以无偏的方式测量Omega_m，即使它们可能受到大误差的困扰。我们表明，在观测数据的背景下，一个几乎无偏估计Omega_m可以建立使用MAK重建。
英文摘要：
Lagrangian reconstruction of large-scale peculiar velocity fields can be strongly affected by observational biases. We develop a thorough analysis of these systematic effects by relying on specially selected mock catalogues. For the purpose of this paper, we use the MAK reconstruction method, although any other Lagrangian reconstruction method should be sensitive to the same problems. We extensively study the uncertainty in the mass-to-light assignment due to luminosity incompleteness, and the poorly-determined relation between mass and luminosity. The impact of redshift distortion corrections is analyzed in the context of MAK and we check the importance of edge and finite-volume effects on the reconstructed velocities. Using three mock catalogues with different average densities, we also study the effect of cosmic variance. In particular, one of them presents the same global features as found in observational catalogues that extend to 80 Mpc/h scales. We give recipes, checked using the aforementioned mock catalogues, to handle these particular observational effects, after having introduced them into the mock catalogues so as to quantitatively mimic the most densely sampled currently available galaxy catalogue of the nearby universe. Once biases have been taken care of, the typical resulting error in reconstructed velocities is typically about a quarter of the overall velocity dispersion, and without significant bias. We finally model our reconstruction errors to propose an improved Bayesian approach to measure Omega_m in an unbiased way by comparing the reconstructed velocities to the measured ones in distance space, even though they may be plagued by large errors. We show that, in the context of observational data, a nearly unbiased estimator of Omega_m may be built using MAK reconstruction.
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