通过隐式互相关统计量的贝叶斯宇宙学推断
Bayesian cosmological inference through implicit cross-correlation statistics
摘要：
对下一代星系数据的分析需要准确处理系统效应，如观测星系与底层物质密度场之间的偏差。然而，所提出的模型的现象是数值昂贵或太不准确，以实现无偏的宇宙学参数的推断，即使在轻度非线性尺度的数据。作为一种替代构建准确的星系偏差模型，需要了解星系形成，我们建议构建似然分布的贝叶斯正向建模方法是不敏感的线性，尺度依赖的偏见，并提供对模型误指定的鲁棒性。我们使用最大熵参数来构造仅考虑数据和推断量之间的相关性的似然分布。通过设计，这些相关性对线性星系偏置关系不敏感，提供了所需的鲁棒性。该方法的实施和测试中的马尔可夫链蒙特卡罗方法。该方法是评估使用晕模拟目录的基础上标准的完整，宇宙学，N体模拟。我们得到公正的和严格的约束宇宙学参数只利用线性互相关率为$k\le 0.10$兆秒钟/小时。对质量约为10 $^{12}$ M $_\odot$到约10 $^{13}$ M $_\odot$的晕的测试表明，在获得宇宙学的鲁棒约束时，可以忽略线性的、尺度相关的、偏置函数的所有细节。我们的研究结果提供了一个很有前途的路径向前分析星系调查，而不需要准确地模拟星系偏置的细节，但通过设计强大的似然推理。
英文摘要：
Analyzes of next-generation galaxy data require accurate treatment of systematic effects such as the bias between observed galaxies and the underlying matter density field. However, proposed models of the phenomenon are either numerically expensive or too inaccurate to achieve unbiased inferences of cosmological parameters even at mildly-nonlinear scales of the data. As an alternative to constructing accurate galaxy bias models, requiring understanding galaxy formation, we propose to construct likelihood distributions for Bayesian forward modeling approaches that are insensitive to linear, scale-dependent bias and provide robustness against model misspecification. We use maximum entropy arguments to construct likelihood distributions designed to account only for correlations between data and inferred quantities. By design these correlations are insensitive to linear galaxy biasing relations, providing the desired robustness. The method is implemented and tested within a Markov Chain Monte Carlo approach. The method is assessed using a halo mock catalog based on standard full, cosmological, N-body simulations. We obtain unbiased and tight constraints on cosmological parameters exploiting only linear cross-correlation rates for $k\le 0.10$ Mpc/h. Tests for halos of masses ~10$^{12}$ M$_\odot$ to ~10$^{13}$ M$_\odot$ indicate that it is possible to ignore all details of the linear, scale dependent, bias function while obtaining robust constraints on cosmology. Our results provide a promising path forward to analyzes of galaxy surveys without the requirement of having to accurately model the details of galaxy biasing but by designing robust likelihoods for the inference.
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