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.

天文光谱学是我们理解宇宙及其组成的基础（例如，恒星、行星、星系、星际介质），对于一代又一代的天文学家来说，光谱数据的分析一直由经验丰富、训练有素的最终用户--天文学家自己--进行。新一代光谱仪器将在相对较短的时间尺度上产生数十亿个高分辨率光谱，其速度比分析速度快几个数量级，这将对这种模式提出严峻挑战。研究小组将通过开发基于遗传算法（GA）的开源光谱分析包来解决这个“数据消防”问题，以最少的人为干预自动准确分析天文光谱，然后用它来探索星系气体晕内的物理条件作为概念验证。该项目将支持两名研究生的论文研究，并允许PI在他的家乡机构进一步努力，让学生参与计算天体物理学和其他STEM领域。该项目由EPSCoR共同资助。建立适用于光谱分析中快速增长的大型数据集的快速自动全局最优搜索方法是一个复杂而长期的问题。拟议的工作将通过开发一种基于遗传算法的全局优化方法来分析高分辨率光谱，适用于现代数据环境中的大型和快速增长的数据集，然后将其应用于促进使用HST的宇宙起源光谱仪（COS）获得的背景源档案光谱对星系的环星系（CGM）和星系间（IGM）介质的理解，从而推进多个领域的知识。.该项目的成果将包括一个可扩展的开源代码库软件包（Astro-Neo），该软件包将使天文学家能够以更高的质量和可重复性分析大量数据。核心软件已成功应用于材料科学，并将扩展到其他领域超出本项目的生命周期。该项目的成果将直接解决以PB/年的速率获取数据时数据分析的挑战，并为加速发现的新研究平台做出贡献。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。