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中关键问题的理解，特别是暗物质晕结构的起源及其重子含量的演化。它将通过开发基于高分辨率模拟的分析和半分析处理方法来解决有关冷暗物质（CDM）类型模型的问题。这些将提供在高红移和低红移的星系属性的详细预测，与空间和地面数据进行比较，特别是来自深河外进化探测器（DEEP）计划。这些模拟使用了自适应细化树（ART）技术，该技术可以自动调整分辨率以跟踪密集区域中的粒子，现在可以使用不同质量的粒子来提高质量和力的分辨率。这使得所有暗物质晕的识别，可以窝藏明亮的星系。此外，该项目将使用第二个工具，半分析合并（SAM），以模拟各种星星形成的结果和CDM星系形成的反馈方案。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、星星形成、超新星反馈、卫星合并以及尘埃的影响。它们模拟尘埃排放，并允许预测从远紫外到亚毫米波长的星系计数和综合背景。这些预测对于解释以前和计划中的空间任务，如COBE、SIRTF、GALEX等的数据将是非常宝贵的。ART和SAM模型的计算机代码将向公众提供。