GRASSY: A Hardware-Accelerated Spectral Synthesis Engine for Asteroseismic Mode Identification

GRASSY：用于星震模式识别的硬件加速光谱合成引擎

• 批准号: 0904607
• 负责人: Richard Townsend
• 金额: $ 34.07万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904607&HistoricalAwards=false
• 关键词: GRASSY; Hardware; Accelerated; Spectral; Synthesis

A hardware-accelerated graphics processing unit-based computational machine will be designed, developed, and commissioned to support reduction and analysis of time series of stellar spectra. The goal of this project is to study the variability to infer details of the structure of the inside of the observed stars. This tool, referred to as asteroseismology, is a unique method to probe a star's internal structure from measurements of its discrete oscillation spectrum. Asteroseismology was originally developed for studying the sun, but advances in space-based observations are allowing its application to other stars.

将设计、开发和委托一台基于硬件加速图形处理单元的计算机，以支持恒星光谱时间序列的减少和分析。该项目的目标是研究变化，以推断所观察到的恒星内部结构的细节。这一工具被称为星震学，是一种通过测量离散振荡光谱来探测星星内部结构的独特方法。天文地震学最初是为了研究太阳而发展起来的，但天基观测的进步使其能够应用于其他恒星。