Collaborative Research: The Circumgalactic Dictionary: An Interpretation Guide For Circumgalactic Medium Observations

像我们银河系这样的星系形成的历史记录在星系中可见恒星之间的气体性质中。 这种气体存在于低密度的云层中，很难研究。这种周围的气体通常是通过观察它在背景类星体的光下的轮廓来检测的，被称为“类星体吸收线”。 研究人员将开发工具，帮助天文学家更好地解释这种气体的化学历史。他们将开发工具，利用类星体的吸收线来确定气体的成分、温度和密度。 这些工具将更好地确定气体云在这些星系中的位置以及气体云如何移动。 通过比较这些气体云的性质，他们试图了解星系将氢和氦转化为更复杂原子的历史。 在这项研究中，研究人员将对研究生和本科生进行各种天文和机器学习技术的培训。研究人员将接触K-12学生和整个社区。研究人员将从星系形成模拟中生成一系列星系质量和性质的合成类星体吸收光谱。他们将以与天文观测相同的方式分析这些模拟光谱。 通过将观察到的光谱与合成吸收系统相关联，他们将确定吸收的条件。在模拟中，粒子将通过它们的物理特性（相位，化学成分，3D位置，运动学）和历史（例如来自银河系流出或原始流入的一部分）进行标记。使用最先进的算法，包括机器学习方法，他们将提供可观测量与气体的真实物理性质和历史之间的概率映射。由此产生的绘图代码将在真实的观测上进行测试，并提供给天文学界，使他们能够解释当前和未来的观测。该项目将结合联合收割机在星系模拟、观测类星体光谱学和机器学习方面的专业知识，并在天体物理学代表性不足的地理区域提供新的研究和推广机会。该项目由天文学部和刺激竞争性研究的既定计划（EPSCoR）共同资助该奖项反映了NSF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。

The history of the formation of a galaxy like our own Milky Way is recorded in the properties of gas between the visible stars in the galaxy. The gas is in low density clouds and is difficult to study. This surrounding gas is usually detected by observing its silhouette in the light of background quasars, known as “quasar absorption lines”. The investigators will develop tools to help astronomers to better interpret the chemical history of this gas. They will develop tools to use quasar absorption lines to determine the gas composition, temperature and density. These tools will better determine where the gas clouds are in these galaxies and how clouds of gas are moving. By comparing the properties of these clouds of gas, they seek to understand the history of galaxy’s transformation of hydrogen and helium into more complex atoms. During this study, the investigators will train graduate and undergraduate students in a variety of astronomical and machine learning techniques. The investigators will reach out to K-12 students and the community at large.The investigators will generate synthetic quasar absorption spectra from galaxy formation simulations for a range of galaxy masses and properties. They will analyze these simulated spectra in the same manner as astronomical observations. By correlating observed spectra with synthetic absorption systems, they will determine the conditions responsible for the absorption. In the simulations the particles will be tagged by their physical properties (phase, chemical composition, 3D location, kinematics) and history (e.g. from a galactic outflow or part of pristine in-fall). Using state-of-the-art algorithms including a machine learning approach, they will provide a probabilistic mapping between the observables and the true physical properties and history of the gas. The resulting mapping code will be tested on real observations and provided to the astronomical community to enable them to interpret current and future observations. This project will combine expertise in galaxy simulations, observational quasar spectroscopy, and machine learning, and provide new research and outreach opportunities in a geographical region underrepresented in astrophysics.This project is jointly funded by the Astronomy Division and the Established Program to Stimulate Competitive Research (EPSCoR).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.