基于Augur的DESC费舍尔预测
Fisher Forecasting for the DESC with $\texttt{Augur}$
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中文总结 AI 辅助
研究人员开发DESC的Augur工具为LSST提供宇宙学推断的费舍尔预测，经测试其与其他方法吻合良好，还为谐波空间3×2pt研究提供建模选择诊断，将持续更新以适配DESC软件生态。
中文摘要 AI 辅助
薇拉·C·鲁宾天文台的空间与时间遗产巡天（LSST）已开始对整个可见南半球开展为期十年的巡天。为确保宇宙学测量的稳健性，需开展计算成本低的建模选择研究，以评估拟议宇宙学分析的性能。本文介绍暗能量科学合作组（DESC）的Augur工具，该工具利用DESC科学专用软件框架为LSST的宇宙学推断提供费舍尔预测。我们通过将该流水线与外部代码生成的预测及通过嵌套采样方法对后验的直接采样结果进行比较来测试它，发现所有方法间吻合度良好。我们还研究了谐波空间中3×2pt研究的一系列建模与超参数选择，为用户提供获取可靠预测的诊断工具。随着更多探针与功能的出现，Augur将持续更新以兼容DESC软件生态系统中的其他工具。
英文摘要
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) has begun its ten-year survey of the entire visible southern hemisphere. To ensure robust cosmological measurements, computationally inexpensive investigations of modeling choices must be made to gauge the performance of proposed cosmological analyses. In this paper, we introduce the $\texttt{Augur}$ tool of the Dark Energy Science Collaboration (DESC), which provides Fisher forecasts for cosmological inference for the LSST using software frameworks designed for DESC science. We test the pipeline by comparing it to forecasts produced by external code and direct sampling of the posterior via nested sampling methods, finding good agreement between all methods. We additionally investigate a range of modeling and hyperparameter choices for a 3$\times$2pt investigation in harmonic space, providing users with diagnostics to obtain reliable forecasts. $\texttt{Augur}$ will be continually updated to be compatible with the other tools in the DESC software ecosystem as additional probes and functionality become available.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
- University of Arizona（亚利桑那大学）
- Ludwig-Maximilians Universität（慕尼黑路德维希-马克西米利安大学）
- Space Telescope Science Institute（太空望远镜科学研究所）
- Utrecht University（乌得勒支大学）
- Leiden University（莱顿大学）
- Stockholm University（斯德哥尔摩大学）
- Imperial College London（伦敦帝国理工学院）
- Fermi National Accelerator Laboratory（费米国家加速器实验室）
- Duke University（杜克大学）
- Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。
