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

暗物质是一种神秘的物质，它不发射、吸收或反射光线，但占我们宇宙中物质的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，但它的身份仍然是我们这个时代推动科学研究的问题之一。由于科学家还没有直接探测到暗物质粒子，目前许多研究的目标，包括这一提议，都是为了预测间接限制暗物质性质的方法。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用单个星系测试暗物质。该团队将在银河系和更小的星系的模拟中实施几个动机良好的暗物质模型，首次在星系形成过程中创建一套受控实验，其中只有暗物质的类型是不同的。他们将使用这些模拟来确定哪些间接测试可以使用对星系的观测来区分暗物质模型，并为那些针对下一代天文台量身定做的测试做出预测。该团队将跨越几个传统上孤立的物理学子领域，为新一代不同的研究人员提供这项开创性工作所需的广泛的理论和计算背景。通过实施基于证据的最佳实践来促进合作中的公平，该团队将朝着发展一个更具包容性的计算天体物理学社区的方向取得重大进展。具体地说，拟议工作的主要成果是：(1)一套新的、公开的经过验证的软件模块，在发展良好、经过广泛测试的小工具代码库中实现了DM粒子模型的关键类别，用于星系形成的宇宙学-流体动力学模拟；(2)在各种DM模型下演化的具有相同初始条件和完全相同的重子物理的一组公共模拟类银河系和矮小星系；(3)一套具体的、可观测验证的预测--来自传统的和基于机器学习的分析--用于当前和未来的观测站，可用于限制或排除DM模型的类别；(4)一个新的研究生研究人员和博士后网络，具有广泛的培训和专业知识，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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.