从光度测量中对星系红移分布的分层贝叶斯推断
Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys
摘要：
准确地描述星系的红移分布对于分析深空光度测量和检验宇宙学模型是必不可少的。我们提出了一种技术，同时推断红移分布和个人的红移光度星系目录。我们的模型构建了一个分段常数表示（有效地直方图）的星系类型和红移的分布，其参数是有效地推断从嘈杂的光度通量测量。这种方法可以被看作是模板拟合光度红移方法的推广，并依赖于光谱模板库将单个星系的光度通量与它们的红移联系起来。我们说明了这种技术的模拟星系调查数据，并证明它提供正确的后验分布的基本类型和红移分布，以及对个别类型和红移的星系。我们表明，即使与无信息的先验，大光度误差和参数退化，红移和类型分布可以恢复鲁棒性由于该模型的层次性，这是不可能与常见的光度红移估计技术。因此，红移的不确定性可以第一次在宇宙学分析中完全传播，满足当前和未来几代调查的基本要求。
英文摘要：
Accurately characterizing the redshift distributions of galaxies is essential for analysing deep photometric surveys and testing cosmological models. We present a technique to simultaneously infer redshift distributions and individual redshifts from photometric galaxy catalogues. Our model constructs a piecewise constant representation (effectively a histogram) of the distribution of galaxy types and redshifts, the parameters of which are efficiently inferred from noisy photometric flux measurements. This approach can be seen as a generalization of template-fitting photometric redshift methods and relies on a library of spectral templates to relate the photometric fluxes of individual galaxies to their redshifts. We illustrate this technique on simulated galaxy survey data, and demonstrate that it delivers correct posterior distributions on the underlying type and redshift distributions, as well as on the individual types and redshifts of galaxies. We show that even with uninformative priors, large photometric errors and parameter degeneracies, the redshift and type distributions can be recovered robustly thanks to the hierarchical nature of the model, which is not possible with common photometric redshift estimation techniques. As a result, redshift uncertainties can be fully propagated in cosmological analyses for the first time, fulfilling an essential requirement for the current and future generations of surveys.
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