Resolving AGN accretion and feedback in cool core clusters with parsec-scale simulations

通过秒差距尺度模拟解决冷核心星团中 AGN 吸积和反馈问题

• 批准号: 1210890
• 负责人: Greg Bryan
• 金额: $ 30.31万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1210890&HistoricalAwards=false
• 关键词: Resolving; AGN; accretion; feedback; cool; Resolving; AGN; accretion; feedback; cool

This project is a systematic investigation of cooling gas and active galactic nucleus (AGN) feedback in cool core clusters, using adaptive mesh refinement simulations to resolve all cooling instabilities down to nearly the parsec scale. The first phase will focus only on radiative cooling, temporarily neglecting feedback in order to determine how gas cools in a cooling core. It will consider the effect of magnetic fields and anisotropic heat conduction, and evaluate the influence of gas temperature and potential well depth on the cooling catastrophe. The second phase will include AGN feedback, resolving down as far as the sphere of influence of the central supermassive black hole, in order to focus on different models for implementing jet-driven feedback. Detailed predictions of the X-ray, optical and radio emission properties of the cool core will serve as the diagnostics for testing the models.The research activities used to achieve the goals of this project will all involve training and public outreach, including a number of projects suitable for undergraduate students. The team has a good track record in these areas, and all members will continue to be involved in local activities, including a partnership with the American Museum of Natural History to generate high-quality visualizations. The adaptive mesh simulation code used in this study, called Enzo, is widely used by many researchers world-wide, and some effort will be invested to make it more complete, accurate and easy-to-use. The PI will hold regular (virtual) developer meetings to help to guide code development.

该项目是对冷却气体和活性银河核（AGN）反馈的系统研究，使用自适应网状细化模拟，将所有冷却不稳定性降低到几乎是PARSEC量表。 第一阶段将仅着眼于辐射冷却，暂时忽略反馈，以确定气体如何在冷却芯中冷却。 它将考虑磁场和各向异性热传导的影响，并评估气温和潜在的井深度对冷却灾难的影响。 第二阶段将包括AGN的反馈，并将其降低到中央超级黑洞的影响范围，以专注于实施喷气驱动反馈的不同模型。 对Cool Core的X射线，光学和无线电排放特性的详细预测将作为测试模型的诊断。用于实现该项目目标的研究活动都将涉及培训和公众推广，包括许多适合本科生的项目。 该团队在这些领域都有良好的记录，所有成员将继续参与当地活动，包括与美国自然历史博物馆建立高质量可视化的合作伙伴关系。 本研究中使用的自适应网状仿真代码（称为Enzo）被全球许多研究人员广泛使用，并将投入一些努力，以使其更完整，准确且易于使用。 PI将举行常规（虚拟）开发人员会议，以帮助指导代码开发。