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

在目前的星系形成模式中，巨大的冷流、原始气体落入暗物质晕的中心。随着流动的继续和密度的增加，第一代恒星在最终将成为年轻星系的地方形成。随后，冷流入气体和热喷出气体之间的相互作用产生了环绕它的广泛而动态的气体“大气”，称为环星系介质(CGM)。CGM很重要，因为它可以告诉我们关于星系形成和持续演化的信息。研究人员将领导一个协调计划，将最先进的流体动力学星系模拟与能够探测日冕物质的新的多波长探测数据结合起来，以促进我们对宇宙时间段塑造日冕物质的物理过程的理解。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CGM研究的本科生STEM学生的项目。随着我们进入多波长天文测量时代，这一奖项将促进对气态晕物理的理解。具体地说，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(CAMEL)结果解释最先进的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厘米映射和快速射电爆发(FRB)作为宇宙时间上的CGM探测器，以及(3)通过结合斯隆数字巡天(SDSS)和eROSITA图像开发一种机器学习技术来估计星系组的质量。随着CGM变得越来越多地以全色和层析方式绘制，来自正在进行的最先进的宇宙学模拟项目的统计预测对于对驱动星系演化的CGM过程施加严格的约束是必要的。研究人员还将开发有用的社区工具，用于最大限度地提高即将到来的多波长天文测量的科学回报。这一奖项反映了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。

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.