Theory of Large-Scale Structure: Understanding Non-linearities, Bias, and Systematics in the Baryon Acoustic Oscillations as a Probe of Dark Energy

大尺度结构理论：理解重子声振荡作为暗能量探针的非线性、偏差和系统学

• 批准号: 0807649
• 负责人: Eiichiro Komatsu
• 金额: $ 32.79万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0807649&HistoricalAwards=false
• 关键词: Theory; Large; Scale; Structure; Understanding

AST-0807649KomatsuThis study will develop theoretical tools to extract the maximum cosmological information from the observed power spectrum of galaxies at high redshifts, obtaining the primordial spectrum on scales and with accuracies not achievable by studies of the cosmic microwave background. These results will have far-ranging implications for a variety of planned and proposed studies, both ground- and space-based. Theoretical work of this sort on non-linear gravitational growth and astrophysical effects was a primary recommendation of the Dark Energy Task Force. The tools to be developed will take into account the key non-linear effects: (a) non-linear matter clustering, (b) non-linear and stochastic galaxy bias, and (c) non-linear redshift space distortion. These effects will be the dominant systematic errors in future dark energy constraints from galaxy surveys. This project will improve the accuracy of perturbation theory, and find the maximum wave-number below which one can use perturbation theory reliably.The work integrates research and education naturally by involving two graduate students in cutting-edge research. The simulation data will provide an attractive and efficient tool for providing research experiences to undergraduate students.

这项研究将开发理论工具，从观测到的星系高红移功率谱中提取最大宇宙学信息，获得宇宙微波背景研究无法达到的尺度和精度上的原始谱。 这些结果将对各种计划和拟议的地面和空间研究产生深远的影响。 这类关于非线性引力增长和天体物理效应的理论工作是暗能量任务组的主要建议。 将要开发的工具将考虑到关键的非线性效应：（a）非线性物质聚集，（B）非线性和随机星系偏差，以及（c）非线性红移空间畸变。 这些影响将是未来星系巡天暗能量约束的主要系统误差。 本课题旨在提高微扰理论的精度，并求出能够可靠地使用微扰理论的最大波数。本课题通过让2名研究生参与尖端研究，将研究与教育自然地结合起来。 模拟数据将提供一个有吸引力的和有效的工具，为本科生提供研究经验。