arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1802.02581astro-ph.COastro-ph.GA
收藏

利用关联函数的自洽红移估计

Self-consistent redshift estimation using correlation functions without a spectroscopic reference sample

Ben Hoyle, Markus Michael Rau

摘要:

本文提出了一种利用关联函数在完全数据驱动和自洽的情况下估计星系红移分布和星系-暗物质偏置参数的新方法。与其他机器学习、模板或相关红移方法不同,这种方法不需要具有已知红移的参考样本。通过测量不同星系子样本的投影交叉相关和自相关,通过在色星等空间中选择简单的单元,我们能够估计星系-暗物质偏差模型参数,以及每个子样本的红移分布的形状。该方法充分边缘化了一个灵活的参数化的红移分布和星系的子样本的星系-暗物质偏置参数,从而提供了一个通用的贝叶斯框架,将红移的不确定性纳入宇宙学分析中的数据驱动,一致的,和可重复的方式。这个结果是改善了一个数量级,包括与宇宙微波背景辐射和星系星系透镜的互相关。我们展示了如何将这种方法应用于真正的星系。通过使用理想化的数据向量,其中所有的星系-暗物质模型参数和红移分布是已知的,这种方法被证明是恢复无偏估计的重要数量,如偏移$Δ_z$之间的平均值的真实和估计的红移分布和68\%和95\%和99.5\%的宽度的红移分布的精度所需的当前和未来的调查。

英文摘要:

We present a new method to estimate redshift distributions and galaxy-dark matter bias parameters using correlation functions in a fully data driven and self-consistent manner. Unlike other machine learning, template, or correlation redshift methods, this approach does not require a reference sample with known redshifts. By measuring the projected cross- and auto- correlations of different galaxy sub-samples, e.g., as chosen by simple cells in color-magnitude space, we are able to estimate the galaxy-dark matter bias model parameters, and the shape of the redshift distributions of each sub-sample. This method fully marginalises over a flexible parameterisation of the redshift distribution and galaxy-dark matter bias parameters of sub-samples of galaxies, and thus provides a general Bayesian framework to incorporate redshift uncertainty into the cosmological analysis in a data-driven, consistent, and reproducible manner. This result is improved by an order of magnitude by including cross-correlations with the CMB and with galaxy-galaxy lensing. We showcase how this method could be applied to real galaxies. By using idealised data vectors, in which all galaxy-dark matter model parameters and redshift distributions are known, this method is demonstrated to recover unbiased estimates on important quantities, such as the offset $Δ_z$ between the mean of the true and estimated redshift distribution and the 68\% and 95\% and 99.5\% widths of the redshift distribution to an accuracy required by current and future surveys.

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。

↑