Studying Galaxy Formation and Evolution with New Data, Models, and Methods

用新数据、模型和方法研究星系的形成和演化

• 批准号: 0205944
• 负责人: Joel Primack
• 金额: $ 19.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205944&HistoricalAwards=false
• 关键词: Studying; Galaxy; Formation; Evolution; New

AST 0205944PrimackThe goals of this project are to improve the understanding of key issues in galaxy formation and evolution, especially the origin of the structure of dark matter halos and the evolution of their baryonic contents. It will address the questions that have arisen concerning cold dark matter (CDM) type models by developing analytic and semi-analytic treatments based on, and tested against, high-resolution simulations. These will provide detailed predictions of galaxy properties at both high and low redshifts, to be compared to space and ground based data especially from the Deep Extragalactic Evolutionary Probe (DEEP) program. These simulations feature use of the Adaptive Refinement Tree (ART) technique, which adjusts the resolution automatically to follow particles in dense regions, and can now use particles of different masses to improve mass and force resolution. This permits the identification of all dark matter halos that can harbor bright galaxies. In addition the project will use a second tool, Semi-Analytic Merging (SAM) to model the outcomes of various star formation and feedback scenarios in CDM galaxy formation. These SAM models will be used to study the structural and chemical evolution of galaxies, including gas cooling, star formation, supernova feedback, satellite merging, and the effects of dust. They model dust emission, and allow the prediction of galaxy counts and integrated backgrounds over wavelengths from the far-UV to the sub-mm. These predictions will be invaluable for interpreting data from previous and planned space missions such as COBE, SIRTF, GALEX, etc. The computer codes for the ART and SAM models will be made available to the public..***

AST 0205944Primack 该项目的目标是提高对星系形成和演化关键问题的理解，特别是暗物质晕结构的起源及其重子内容的演化。它将通过开发基于高分辨率模拟并进行测试的解析和半解析处理来解决与冷暗物质（CDM）类型模型有关的问题。这些将提供高红移和低红移下星系特性的详细预测，以便与空间和地面数据（尤其是来自深河外演化探测器（DEEP）计划的数据）进行比较。这些模拟采用自适应细化树 (ART) 技术，该技术可自动调整分辨率以跟踪密集区域中的粒子，并且现在可以使用不同质量的粒子来提高质量和力分辨率。这使得能够识别所有可能蕴藏明亮星系的暗物质晕。此外，该项目将使用第二个工具半解析合并（SAM）来模拟各种恒星形成的结果以及 CDM 星系形成中的反馈场景。这些 SAM 模型将用于研究星系的结构和化学演化，包括气体冷却、恒星形成、超新星反馈、卫星合并和尘埃的影响。它们对尘埃发射进行建模，并允许预测从远紫外到亚毫米波长范围内的星系计数和集成背景。这些预测对于解释以前和计划中的太空任务（例如 COBE、SIRTF、GALEX 等）的数据非常宝贵。ART 和 SAM 模型的计算机代码将向公众开放。***