Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations
Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations
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暗能量调查第 3 年结果：使用离散实现排名对红移分布不确定性进行边缘化
DOI：
10.1093/mnras/stac147
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发表时间：
2022
影响因子：
4.8
通讯作者：
Camacho, H
中科院分区：
文献类型：
--
作者：
Cordero, Juan P;Harrison, Ian;Rollins, Richard P;Bernstein, G M;Bridle, S L;Alarcon, A;Alves, O;Amon, A;Andrade-Oliveira, F;Camacho, H
通过将源星系分类到层析红移子样本中，可以最大限度地利用弱透镜巡天的宇宙学信息。这些红移分布的任何不确定性都必须正确地传播到宇宙学结果中。我们presentthyperrank，边缘化的红移分布的不确定性的一种新方法，使用离散样本从所有可能的红移分布的空间，改进简单的参数化模型。在hyperrank中，建议的红移分布集根据少量（1到4个之间）的汇总值进行排名，然后与用于推断的蒙特卡罗链中的其他滋扰参数和宇宙学参数一起进行沿着采样。这种方法可以被认为是一种通用的方法，用于边缘化的离散实现的数据向量的变化与滋扰参数，因此可以单独从感兴趣的主要参数进行采样，允许提高计算效率。我们专注于弱透镜宇宙剪切分析的情况下，并证明我们的方法使用模拟暗能量调查（DES）。我们表明，该方法可以正确和有效地边缘化在广泛的模型的红移分布的不确定性。最后，我们比较hyperrank到常见的均值漂移方法边缘化的红移的不确定性，验证这个简单的模型是足够的DES年3宇宙学的结果在同伴论文中使用。
Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We presenthyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. Inhyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we comparehyperrankto the common mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.