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、不吸收、也不反射光，但却占宇宙物质的80%以上。它的存在是通过它对可见物质施加的引力推断出来的，但它的身份仍然是我们这个时代的驱动科学问题之一。由于科学家们还没有直接探测到暗物质粒子，目前许多研究的目标，包括这个提议，都是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。该团队将在模拟银河系和更小的星系时实施几个良好的暗物质模型，首次在星系形成过程中创建一组只有暗物质类型不同的受控实验。他们将利用这些模拟来确定哪些间接测试可以利用对星系的观测来区分暗物质模型，并为那些适合下一代天文台的测试做出预测。该团队将跨越几个传统上孤立的物理学子领域，为新一代多样化的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他们合作中的公平，这个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重大进展。具体来说，提出的工作的主要成果是：(1)一套新的，公共的，经过验证的软件模块，在开发良好的，广泛测试的GIZMO代码库中实现DM粒子模型的关键类，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，公开模拟了具有相同初始条件和完全相同重子物理的类银河系和矮星系；(3)一组具体的、观测上可测试的预测——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，可用于约束或排除DM模型的类别；(4)一个由受过广泛训练和专业知识的新研究生和博士后组成的网络，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。该奖项反映了美国国家科学基金会的法定使命，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。

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.