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

在目前星系形成的范例中，大量的冷却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，第一代恒星形成，最终成为一个年轻的星系。冷的流入气体和热的喷出气体之间的相互作用产生了一个广泛的和动态的气体“大气”，称为环星系介质（CGM）。CGM很重要，因为它可以告诉我们星系的形成和持续演化。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新的多波长调查数据相结合，这些调查能够探测CGM，以促进我们对在宇宙时间内塑造它的物理过程的理解。该奖项还将支持研究生以及旨在招募，培训，指导和支持CGM研究本科STEM学生的计划。随着我们进入多波长天文观测时代，这一奖项将推进对气体晕物理学的理解。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物理学进行新的深度学习约束，（2）探索使用无线电21厘米映射和快速射电暴（FRB）作为宇宙时间内CGM的探测器，以及（3）开发一种机器学习技术，通过结合斯隆数字巡天（SDSS）和eROSITA图像来估计星系群的质量。随着CGM越来越多地被绘制成全色和断层图像，来自正在进行的最先进的宇宙学模拟项目的统计预测是必要的，以严格限制CGM驱动星系演化的过程。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到来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，并已被认为是值得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的支持。

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.