光谱描述语言和粗集

Describing Language of Spectra and Rough Set

根据经验进行研究是天文界研究光谱的习惯 ,从来没有一个合适的理论框架用于描述光谱 ,这和以往的观测数据量比较少有关系。随着天文望远镜的发展和现代技术水平的提高 ,人类获得的光谱数据量正在飞速增长。每天可以收集到多达两万多条光谱的LAMOST望远镜就是其中的代表。面对海量的数据库 ,只依靠一些简单的经验和规则已经不能满足研究的需求。在对大量的光谱做过仔细的观察和研究之后 ,本文从光谱的形态细节入手 ,通过定义基元而建立起一种对光谱整体做出完整描述的语言———光谱描述语言 ,以试图为今后的研究提供一个理论框架。同时本文还在光谱描述语言的基础上引入了数据挖掘的一种技术———粗集理论。通过粗集理论提取出对光谱分类有用的一些规则 ,这可以看作是光谱描述语言的一个应用。

It is the traditional way to analyze spectra by experiences in astronomical field. And until now there has never been a suitable theoretical frame to describe spectra, which is may be owing to small spectra datasets that astronomers can got by low-level instruments. With the high-speed development of telescopes, especially on behalf of LAMOST, a large telescope which can collect more than 20 000 spectra in an observing night,spectra datasets are becoming larger and larger very fast. Facing these., voluminous datasets,the traditional spectra-processing way simply depending on experiences becomes unfit. In this paper, we develop a brand-new language-describing language of spectra (DLS) to describe spectra of celestial bodies by defining BE (Basic element). And based on DLS, we introduce the method of RSDA (Rough set and data analysis), which is a technique of data mining. By RSDA we extract some rules of stellar spectra, and this experiment can be regarded as an application of DLS.