Discovery and Modeling of Merging Galaxy Clusters

合并星系团的发现和建模

• 批准号: 2308383
• 负责人: David Wittman
• 金额: $ 35.32万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2308383&HistoricalAwards=false
• 关键词: Discovery; Modeling; Merging; Galaxy; Clusters

A cluster of galaxies is an enormous laboratory hosting a variety of physical processes that play out on a stage millions of light-years across. A particularly dramatic process is when two clusters collide and merge. Because most of the mass of each cluster is dark matter, a cluster merger serves as a "dark matter collider" capable of revealing how dark matter particles interact with each other---something no Earthbound lab can currently do. The team will identify merging cluster candidates using a new technique, then study the candidates in greater detail to identify the simplest systems, and finally model them to create an ensemble of well-modeled dark matter laboratories. The team will also expand research opportunities for undergraduates by continuing participation in Cal-Bridge, which helps undergraduates bridge up to PhD programs with long-term support, and by running graduate admissions boot camps open to all.The team will mine large optical cluster catalogs (currently using redMaPPer while establishing a foundation for using future larger catalogs) for candidates with signs of binarity, then study those candidates with X-ray data (both archival and newly pointed) to identify "dissociative" systems where gas has separated from galaxies. The team will conduct a spectroscopic survey of each dissociative system to further assess binarity; quantify the line-of-sight velocity difference between the subclusters; and assess potentially confusing foreground/background structures. The team will then use gravitational lensing to map the mass in good candidate systems, and model binary systems by extracting analogs from cosmological simulations. The proposed work includes upgrading from using dark-matter-only simulations to using hydro simulations, and modeling and observation of shock positions to better constrain the merger scenario.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

星系团是一个巨大的实验室，承载着各种物理过程，这些过程在数百万光年宽的舞台上进行。 一个特别戏剧性的过程是两个星团碰撞并合并。由于每个星团的大部分质量都是暗物质，星团合并就像一个“暗物质对撞机”，能够揭示暗物质粒子如何相互作用——这是目前地球上的实验室无法做到的。该团队将使用新技术识别合并星团候选者，然后更详细地研究候选者以识别最简单的系统，最后对它们进行建模以创建一个模型良好的暗物质实验室集合。该团队还将通过继续参与加州大学桥分校（Cal-Bridge）来扩大本科生的研究机会，该项目帮助本科生在长期支持下衔接博士课程，并举办向所有人开放的研究生招生训练营。该团队将挖掘大型光学集群目录（目前使用 redMaPPer，同时为使用未来更大的目录奠定基础）寻找具有二元性迹象的候选者，然后用 X 射线数据（档案和新指出的）研究这些候选者，以识别气体与气体分离的“解离”系统星系。该团队将对每个解离系统进行光谱调查，以进一步评估二元性；量化子簇之间的视线速度差异；并评估可能令人困惑的前景/背景结构。然后，该团队将使用引力透镜来绘制良好候选系统中的质量，并通过从宇宙学模拟中提取类似物来对双星系统进行建模。拟议的工作包括从仅使用暗物质模拟升级到使用水力模拟，以及对激波位置进行建模和观察，以更好地限制合并场景。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，被认为值得支持。智力价值和更广泛的影响审查标准。