Discovery and Modeling of Merging Galaxy Clusters

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的舞台上上演。 一个特别戏剧性的过程是当两个集群碰撞和合并时。因为每个星系团的大部分质量都是暗物质，所以星系团合并就像一个“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所做不到的。该团队将使用一种新技术来识别合并的集群候选者，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，以创建一个良好建模的暗物质实验室集合。该团队还将继续参与Cal-Bridge项目，为本科生提供长期支持，帮助本科生衔接博士课程，并举办向所有人开放的研究生入学靴子训练营，从而扩大本科生的研究机会。（目前正在使用redMapPer，同时为使用未来更大的星表奠定基础），然后用X射线数据（包括存档的和新指出的）研究这些候选者，以确定气体从星系中分离出来的“离解”系统。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;量化子集群之间的视线速度差异;并评估潜在的混乱前景/背景结构。然后，该团队将使用引力透镜来映射好的候选系统中的质量，并通过从宇宙学模拟中提取类似物来模拟二元系统。建议的工作包括从使用暗物质的唯一模拟升级到使用水力模拟，以及对冲击位置进行建模和观测，以更好地约束合并情景。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.