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

在目前的星系形成范式中，大量冷却的原始气体流落入暗物质晕的中心。随着流的继续和密度的增加，第一代恒星最终形成了一个年轻的星系。随后，冷的流入气体和热的喷出气体之间的相互作用在其周围形成了一个广泛而动态的气体“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。研究人员将领导一个协调的项目，将最先进的流体动力学星系模拟与能够探测CGM的新多波长调查数据相结合，以推进我们对宇宙时间内形成CGM的物理过程的理解。该奖项还将支持一名研究生，以及一个旨在招募、培训、指导和支持从事CGM研究的本科STEM学生的项目。随着我们进入多波长天文调查的时代，这个奖项将促进对气体晕物理的理解。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的机器学习模拟（CAMELS）结果，通过解释深层eROSITA x射线数据，对CGM物理学进行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暴（frb）作为CGM在宇宙时间上的探测器；(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越来越多地被全色和层析成像，从正在进行的最先进的宇宙模拟项目中进行统计预测是必要的，以便对驱动星系演化的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，用于最大化即将到来的多波长天文调查的科学回报。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。

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.