Collaborative Research: CDS&E: Systematic Predictions for Dynamical Signatures of New Dark Matter Physics in Galaxies

合作研究：CDS

• 批准号: 2307788
• 负责人: Lina Necib
• 金额: $ 39.86万
• 依托单位: Massachusetts Institute of Technology
• 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: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307788&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS&E; Systematic; Predictions

Dark matter is a mysterious substance that does not emit, absorb, or reflect light, yet makes up over 80% of the matter in our Universe. Its existence is inferred through the gravitational force it exerts on visible matter, but its identity remains one of the driving scientific questions of our time. Since scientists have not directly detected dark matter particles, the goal of much current research, including this proposal, is to predict ways to indirectly constrain dark matter’s properties. The team of scientists at the University of Pennsylvania, MIT, and Princeton, will study how to test dark matter with individual galaxies. The team will implement several well-motivated models for dark matter in simulations of galaxies like the Milky Way and smaller, creating for the first time a set of controlled experiments in galaxy formation where only the type of dark matter is varied. They will use these simulations to identify which indirect tests can use observations of galaxies to distinguish between dark matter models and make predictions for those tests tailored to next-generation observatories. The team will reach across several traditionally siloed subfields of physics to give a new generation of diverse researchers the broad theoretical and computational background needed for this groundbreaking work. By implementing evidence-based best practices to foster equity within their collaboration, this team will make a significant advance toward growing a more inclusive computational astrophysics community. Specifically, the main outcomes of the proposed work are: (1) a new, public set of validated software modules implementing key classes of DM particle models in the well-developed, extensively tested GIZMO codebase for cosmological-hydrodynamical simulations of galaxy formation; (2) a public set of simulated Milky Way-like and dwarf galaxies with identical initial conditions, and exactly the same baryonic physics, evolved under a variety of DM models; (3) a set of concrete, observationally testable predictions—derived from traditional and machine-learning-based analyses—for current and future observatories that can be used to constrain or rule out classes of DM models; (4) a network of new graduate researchers and postdocs with the broad training and expertise to complete, for the first time, the connection between theoretical models of dark matter, the study of galaxy formation and observational predictions.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.

暗物质是一种神秘的物质，不会发出，吸收或反射光，但占我们宇宙中80％以上的物质。它的存在是通过它在可见物质上施加的引力来推断的，但其身份仍然是我们这个时代的驱动科学问题之一。由于科学家没有直接检测到暗物质颗粒，因此包括此提案在内的许多当前研究的目标是预测间接限制暗物质特性的方法。宾夕法尼亚大学，麻省理工学院和普林斯顿大学的科学家团队将研究如何使用单个星系测试暗物质。该团队将在模拟银河系和较小的星系模拟中实施几个动机的模型，以实现较小的星系模拟，这首先在星系组中进行了一组受控的实验，其中只有暗物质的类型是多种多样的。他们将使用这些模拟来确定哪些间接测试可以使用星系观察来区分暗物质模型，并对针对下一代观察的这些测试做出预测。该团队将跨越几个传统上孤立的物理领域，从而为新一代的潜水员研究人员提供这项开创性工作所需的广泛理论和计算背景。通过实施基于证据的最佳实践来促进其协作中的股权，该团队将在发展一个更具包容性的计算天体物理学社区方面取得了重大进步。具体而言，拟议工作的主要结果是：（1）在经过良好开发的，经过广泛测试的Gizmo代码库中实现DM粒子模型的新的公共验证软件模块集，用于宇宙学的GARAXY形成的宇宙学 - 热动力学模拟； （2）一组公共模拟的银河系状和矮星系具有相同的初始条件，并且在各种DM模型下都演变出相同的初始条件以及完全相同的Baryonic物理学； （3）用于当前和将来的观察值，可用于约束或排除DM模型的类别； （4）一个新的研究生研究人员和博士后网络，并具有广泛的培训和专业知识，以完成深色物质理论模型，银河形成和观察性预测的研究之间的联系。该奖项反映了NSF的法定任务，并通过使用基金会的知识绩效和广泛的影响来评估NSF的法定任务。