Collaborative Research: A Data-Driven Approach to the Multi-Wavelength Circumgalactic Revolution

协作研究：数据驱动的多波长环银河系革命方法

• 批准号: 2206055
• 负责人: Daisuke Nagai
• 金额: $ 33.27万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2206055&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Approach

In the current paradigm of galaxy formation vast streams of cool, primordial gas fall into the centers of dark matter halos. As the flows continue and density increases, the first generation of stars form in what will eventually become a young galaxy. The subsequent interaction between the cool inflowing gas and the hot ejected gas gives rise to an extensive and dynamic gaseous "atmosphere" surrounding it, called the circumgalactic medium (CGM). The CGM is important for what it can tell us about both the formation and continuing evolution of galaxies. The researchers will lead a coordinated program combining state-of-the-art hydrodynamical galaxy simulations with data from new multi-wavelength surveys capable of probing the CGM to advance our understanding of the physical processes that have shaped it over cosmic time. The award will also support a graduate student as well as a program designed to recruit, train, mentor, and support undergraduate STEM students in CGM research. This award will advance the understanding of the physics of gaseous halos as we enter the era of multi- wavelength astronomical surveys. Specifically, the researchers will (1) place novel, deep-learning constraints on CGM physics by interpreting deep eROSITA x-ray data in light of the state-of-the-art Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) results, (2) explore the use of radio 21 cm mapping and fast radio bursts (FRBs) as probes of CGM over cosmic time, and (3) develop a machine learning technique to estimate the mass of galaxy groups by combining Sloan Digital Sky Survey (SDSS) and eROSITA images. As the CGM becomes increasingly mapped both panchromatically and tomographically, statistical forecasts from ongoing, state-of-the-art cosmological simulation projects are necessary to place rigorous constraints on CGM processes that drive galaxy evolution. The researchers will also develop useful community tools that can be used to maximize the scientific returns of upcoming multi-wavelength astronomical surveys.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.

在当前的星系形成范式中，大量冷的原始气体流落入暗物质晕的中心。随着流动的继续和密度的增加，第一代恒星在最终成为年轻星系的地方形成。随后流入的冷气体和喷出的热气体之间的相互作用产生了围绕其的广泛且动态的气体“大气层”，称为环银河介质（CGM）。 CGM 非常重要，因为它可以告诉我们有关星系的形成和持续演化的信息。研究人员将领导一项协调计划，将最先进的流体动力学星系模拟与来自新的多波长调查的数据相结合，这些数据能够探测 CGM，以增进我们对在宇宙时间内塑造它的物理过程的理解。该奖项还将支持一名研究生以及一项旨在招募、培训、指导和支持 CGM 研究方面的本科 STEM 学生的计划。随着我们进入多波长天文勘测时代，该奖项将增进对气态晕物理学的理解。具体来说，研究人员将 (1) 根据最先进的宇宙学和天体物理学与机器学习模拟 (CAMELS) 结果解释深度 eROSITA X 射线数据，对 CGM 物理施加新颖的深度学习约束，（ 2) 探索使用射电 21 厘米测绘和快速射电爆发 (FRB) 作为宇宙时间 CGM 的探测器，以及 (3) 开发一种机器学习技术来估计星系群的质量通过结合斯隆数字巡天 (SDSS) 和 eROSITA 图像。随着全色和层析成像越来越多地绘制 CGM，需要对正在进行的、最先进的宇宙学模拟项目进行统计预测，以对驱动星系演化的 CGM 过程施加严格的限制。研究人员还将开发有用的社区工具，可用于最大限度地提高即将进行的多波长天文调查的科学回报。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。