Collaborative Research: Galactic Archaeology from Careful Modeling of Old Stars

整个宇宙星星形成的历史隐藏在组成我们银河系最古老恒星的元素中。 天文学家开始通过对贫金属（古老）红巨星的仔细观察来揭开这段历史。 要取得进一步的进展，需要对观测结果进行数值建模。 这些研究人员将通过创建更精确的模型来增强我们的理解。 他们的工作很重要，因为在未来几年，对我们银河系的大型观测光谱调查将推动我们知识的距离和年龄前沿。 研究人员计划使用快速、物理驱动的分析工具来帮助分析大量数据集。 他们将得出恒星当前的物理参数和化学成分。目前，大多数模型在恒星光谱分析中使用近似，称为局部热力学平衡（LTE）。 研究人员将使用更现实，但计算昂贵的非LTE模型。研究人员将显著提高在贫金属红巨星中发现的衍生元素丰度的准确性，让我们更好地了解星星形成的历史。佛罗里达大学和芝加哥大学之间的这个合作项目将使快速，非LTE恒星参数和化学丰度的均匀推导，LTE在全频谱合成和使用机器学习技术来有效地模拟非LTE网格。这些结果将在工业规模上应用于涵盖光学和红外波长的红巨星大型调查。该项目将产生用户友好的、公开可用的代码，允许其他天文学家将非LTE分析应用于他们的光谱。此外，该项目将为来自佛罗里达学校的代表性不足和少数民族K-12学生提供机会，利用当地教学天文台和望远镜进行研究。它还将通过在芝加哥大学的夏季研究项目中对代表性不足的少数民族本科生进行公平和包容性的指导来提高天文学的多样性。该奖项反映了NSF的法定使命，并被认为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来支持。

The history of star formation of the entire universe is hidden in the elements making up the oldest stars in our Milky Way Galaxy. Astronomers start to unravel that history by careful observations of metal-poor (old) red giant stars. Making further progress requires numerically modeling the observations. These investigators will enhance our understanding by creating more exact models. Their work is important because in the coming years, large observational spectroscopic surveys of our Milky Way Galaxy will push the distance and age frontiers of our knowledge. The investigators plan to help analyze enormous datasets using fast, physically motivated analysis tools. They will derive the stars’ current physical parameters and chemical compositions. Currently, most models use an approximation in stellar spectral analysis, called Local Thermodynamic Equilibrium (LTE). The investigators will use more realistic, but computationally expensive, non-LTE models. The investigators will significantly improve the accuracy of derived elemental abundances found in the metal-poor red giant stars, giving us a better understanding of the history of star formation.This collaborative project between the University of Florida and the University of Chicago will enable fast, homogeneous derivation of non-LTE stellar parameters and chemical abundances by implementing non-LTE in full spectrum synthesis and using machine learning techniques to efficiently emulate the non-LTE grids. The results will be applied on industrial scales to large surveys of red giant stars covering both optical and infrared wavelengths. The project will produce user-friendly, publicly available codes that allow other astronomers to apply non-LTE analyses to their spectra. Additionally, the project will develop opportunities for under-represented and minority K-12 students from Florida schools to undertake research using the local teaching observatory and telescopes. It will also improve diversity in astronomy through equitable and inclusive mentoring of under-represented minority undergraduate students in summer research projects at the University of Chicago.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.