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这个项目的目标是提高对星系形成和进化的关键问题的理解，尤其是暗物质Halos结构的起源以及其Baryonic含量的演变。它将通过开发基于基于高分辨率模拟的分析和半分析治疗方法来解决有关冷暗物质（CDM）类型模型的问题。这些将为高红移和低红移的星系性能提供详细的预测，将其与空间和地面数据进行比较，尤其是从深层外层外进化探针（深）程序中进行比较。这些模拟具有自适应改进树（ART）技术的使用，该技术会自动调整分辨率以跟随密集区域的颗粒，现在可以使用不同质量的颗粒来改善质量和力分辨率。这允许识别所有可以藏有明亮星系的暗物质光环。此外，该项目将使用第二个工具半分析合并（SAM）来对CDM Galaxy形成中各种恒星形成和反馈方案的结果进行建模。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，卫星合并以及灰尘的影响。它们对灰尘发射进行了建模，并允许对从远处到亚MM的波长进行星系计数和集成背景的预测。这些预测对于解释Cobe，Sirtf，Galex等以前和计划的太空任务的数据将是无价的。ART和SAM模型的计算机代码将提供给公众。***