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arXiv 2602.09230astro-ph.CO
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建模罗曼弱引力透镜宇宙学中的红移不确定性

Modeling Redshift Uncertainties in Roman Weak Lensing Cosmology

Jet Propulsion Laboratory, California Institute of Technology(加州理工学院喷气推进实验室);Duke University(杜克大学);University of Arizona(亚利桑那大学);Stony Brook University(石溪大学);University of Chicago(芝加哥大学);University of Pittsburgh(匹兹堡大学)

Diogo H. F. de Souza, Boyan Yin, Tim Eifler, Vivian Miranda, Chun-Hao To, Brett H. Andrews, Katarina Markovič, Eric Huff, Michael A. Troxel, Olivier Doré

AI总结:

针对罗曼望远镜弱引力透镜测量,采用优化主成分分析(PCA)建模红移分布形状不确定性,在CoCoA管线中验证其对宇宙学参数S8和Ωm的偏差缓解效果。

摘要:

利用罗曼空间望远镜的弱引力透镜测量进行宇宙学约束需要一种强大的方法来建模星系红移分布的不确定性。在这项工作中,我们使用优化版本的主成分分析(PCA)来建模红移分布完整形状的不确定性,该方法由\cite{pca_method}提出,并最近在暗能量巡天Y6分析中使用。在这里,我们在罗曼高纬度成像巡天(HLIS)宇宙学项目基础设施团队(PIT)管线,即Cobaya-Cosmolike联合架构(\texttt{CoCoA})中实现了这一新方法。为了验证PCA在缓解宇宙学参数$S_8$和$\Omega_m$偏差方面的效果,我们使用了一组来自\texttt{Cardinal}的、针对多种罗曼配置生成的红移分布。总体而言,当模拟的宇宙剪切数据向量相对于基准数据没有强烈错误校准时,均值偏移和基于PCA的方法在边缘化 nuisance 参数时产生一致的宇宙学约束。对于轻度到强烈的错误校准,包含额外的PC逐渐缓解$S_8$和$\Omega_m$的偏差,并且可以用比九个断层扫描bin均值偏移模型更少的参数实现相当的性能。

英文摘要:

Cosmological constraints using weak gravitational lensing measurements from the Roman Space Telescope will require a powerful method for modelling uncertainties in the galaxy redshift distribution. In this work, we use an optimized version of the principal component analysis (PCA) to model uncertainties in the full shape of the redshift distributions, a method proposed by \cite{pca_method} and recently used in the Dark Energy Survey Y6 analysis. Here, we implement this new approach within the Roman High Latitude Imaging Survey (HLIS) Cosmology Project Infrastructure Team (PIT) pipeline, namely Cobaya-Cosmolike Joint Architecture (\texttt{CoCoA}). To validate the PCA in mitigating biases on cosmological parameters, $S_8$ and $Ω_m$, we use a set of redshift distributions from \texttt{Cardinal} generated for a variety of Roman configurations. Overall, when the simulated cosmic shear data vector is not strongly miscalibrated relative to the fiducial one, both the mean-shift and the PCA-based approaches produce consistent cosmological constraints when marginalizing over nuisance parameters. For mild to strong miscalibration, including additional PCs progressively mitigates biases in $S_8$ and $Ω_m$, and can achieve comparable performance with fewer parameters than the nine tomographic-bin mean-shift model.

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。

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