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.

暗物质是一种不发射、吸收或反射光的神秘物质，但它占宇宙物质的 80% 以上，它的存在是通过它对可见物质施加的引力来推断的，但它的身份仍然是其中之一。由于科学家尚未直接探测到暗物质粒子，因此包括这项提议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。麻省理工学院，和普林斯顿大学将研究如何用单个星系测试暗物质，该团队将在模拟银河系和更小的星系中实施几个动机良好的暗物质模型，首次创建一系列星系形成的受控实验。他们将利用这些模拟来确定哪些间接测试可以利用星系观测来区分暗物质模型，并为针对下一代天文台的测试做出预测。通过实施基于证据的最佳实践来促进合作中的公平性，该团队将在发展更具包容性方面取得重大进展。具体来说，拟议工作的主要成果是：（1）一套新的、经过验证的公共软件模块，在成熟、经过广泛测试的 GIZMO 代码库中实现了 DM 粒子模型的关键类别。星系形成的宇宙流体动力学模拟；(2) 一组具有相同初始条件和完全相同的重子物理的公共模拟类银河系和矮星系；(3) 一组具体的 DM 模型； (4) 新研究生研究人员网络以及具有广泛培训和专业知识的博士后，首次完成暗物质理论模型、星系形成研究和观测预测之间的联系。该奖项反映了 NSF 的法定使命，并通过使用评估来认为值得支持基金会的智力价值和更广泛的影响审查标准。