LEAPS-MPS: A Systematic and Automatic Spectral Analysis of Physical Conditions of Circumgalactic Medium using Genetic Algorithms;

LEAPS-MPS：使用遗传算法对环绕银河系介质的物理条件进行系统自动光谱分析；

• 批准号: 2213494
• 负责人: Min Long
• 金额: $ 24.72万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213494&HistoricalAwards=false
• 关键词: LEAPS; MPS; Systematic; Automatic; Spectral

Astronomical spectroscopy lies at the foundation of our understanding of the universe and its constituents (e.g., stars, planets, galaxies, interstellar medium), and for generations of astronomers, the analysis of spectroscopic data has been carried out by experienced and highly trained end-users - the astronomers themselves. This paradigm will be severely challenged by new generations of spectrographic instruments that will produce literally billions of high-resolution spectra on relatively short timescales and orders of magnitude faster than they can be analyzed. The research team will address this "data firehose" problem by developing an open-source spectral analysis package based on a genetic algorithm (GA) to accurately analyze astronomical spectra automatically with minimal human intervention, and then use it to explore the physical conditions within the gaseous halos of galaxies as a proof-of-concept. This project will support the thesis research of two graduate students and allow the PI to further efforts at his home institution to involve students in computational astrophysics and other STEM fields. This project is co-funded by the Established Program to Stimulate Competitive Research (EPSCoR).Establishing fast and automated global optimum search methods applicable to large and rapid growing datasets in spectral analysis is a complex and long-standing problem. The proposed work will advance knowledge in multiple areas by developing a GA-based global optimization method for the analysis of high-resolution spectra that is applicable to large and rapid growing datasets in modern data environments and then applying it to facilitate understanding of the circumgalactic (CGM) and intergalactic (IGM) media of galaxies using archival spectra of background sources obtained with the HST's Cosmic Origin Spectrograph (COS). The project outcomes will include an extensible and open-source code base software package (Astro-Neo) that will enable astronomers to analyze large quantities of data with improved quality and reproducibility. The core software has been applied successfully in materials science and will be extended to other fields beyond the lifetime of this project. The project outcomes will directly address the challenges of data analysis when data is obtained at Petabyte/year rates and contribute to a new research platform for accelerating discovery.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.

天文学光谱是我们对宇宙及其组成部分的理解的基础（例如，恒星，行星，星系，星际介质），对于几代天文学家，光谱数据的分析已由经验丰富且训练有素的最终用户 - 天文学家本身进行。新一代的光谱学工具将严重挑战这种范式，这些仪器将在相对较短的时间尺度和比较数量级上产生数十亿个高分辨率光谱，比它们可以分析得多。研究团队将通过基于遗传算法（GA）开发开源光谱分析包（GA）来解决这个“数据消防”问题，以最少的人类干预自动准确地分析天文光谱，然后利用它来探索星体气相中的物理条件作为证明。该项目将支持两名研究生的论文研究，并允许PI在其家庭机构进一步努力，使学生参与计算天体物理学和其他STEM领域。该项目由既定的计划（EPSCOR）共同资助。在光谱分析中，适用于大型且快速增长的数据集的快速，自动化的全球最佳搜索方法是一个复杂而长期的问题，是一个复杂而长期存在的问题。拟议的工作将通过开发一种基于GA的全球优化方法来提高知识，以分析高分辨率光谱，该方法适用于现代数据环境中的大型且快速增长的数据集，然后将其应用于使用背景范围的COSMIC（cos osmic cose spectraptry of Spectrace of Spectrace of Spectrace of Spectraptry of Spectraptry of Spectraptraptraptraption）cosmic spectrace（CGM）和近海术（CGM）（CGM）（CGM）媒体（IGM）。该项目的结果将包括一个可扩展和开源代码基本软件包（Astro-Neo），该软件包将使天文学家能够以提高质量和可重复性分析大量数据。核心软件已成功地应用于材料科学，并将扩展到该项目一生之外的其他领域。当以PBABYTE/年率获得数据时，该项目的结果将直接解决数据分析的挑战，并为加速发现的新研究平台做出了贡献。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来评估通过评估来获得支持的。