一種基于二維算法的新穎的多目標光纖光譜數(shù)據(jù)處理流程
發(fā)布時間:2018-03-31 03:12
本文選題:望遠鏡 切入點:儀器 出處:《天文學(xué)報》2016年01期
【摘要】:郭守敬望遠鏡(Large Sky Area Multi-Object Fiber Spectroscopic Telescope,LAMOST)、斯隆數(shù)字巡天(Sloan Digital Sky Survey,SDSS)、英澳望遠鏡(AngloAustralia Telescope,AAT)等大多數(shù)多目標光纖光譜望遠鏡現(xiàn)用的數(shù)據(jù)處理流程都是基于一維算法的.以LAMOST為例提出多目標光纖光譜數(shù)據(jù)處理流程方法.在LAMOST現(xiàn)用數(shù)據(jù)處理流程中,在預(yù)處理過程之后,通過基于一維模型的抽譜算法從二維觀測目標光譜數(shù)據(jù)中得到一維抽譜結(jié)果作為中間數(shù)據(jù).后續(xù)的處理步驟都基于一維模型的算法.然而,這種數(shù)據(jù)處理流程不符合觀測光譜的形成機理.因此,在每個步驟中都引入了不可忽略的誤差.為了解決這一問題,提出了一種還未被用于LAMOST及其他望遠鏡數(shù)據(jù)處理系統(tǒng)的新穎的數(shù)據(jù)處理流程.重新設(shè)計安排了各個數(shù)據(jù)處理模塊的順序,各關(guān)鍵步驟算法都是基于二維模型的.核心算法將詳細論述.此外,列出了部分實驗結(jié)果來證明二維算法的有效性和優(yōu)越性.
[Abstract]:Large Sky Area Multi-Object Fiber Spectroscopic Telescopel LAMOSTT, Sloan Digital Sky Survey, AngloAustralia TelescopeAATA) and so on, are all based on one-dimensional algorithms.Taking LAMOST as an example, a multiobjective optical fiber spectral data processing method is presented.In the current data processing process of LAMOST, after the pretreatment process, the one-dimensional spectral data are obtained from the two-dimensional observed target spectral data by a one-dimensional spectral algorithm based on the one-dimensional model as the intermediate data.The subsequent processing steps are based on the algorithm of one-dimensional model.However, this data processing process does not conform to the formation mechanism of observational spectra.Therefore, an error that can not be ignored is introduced in each step.In order to solve this problem, a novel data processing flow is proposed which has not been used in LAMOST and other telescope data processing systems.The order of each data processing module is redesigned, and the algorithm of each key step is based on two-dimensional model.The core algorithm will be discussed in detail.In addition, some experimental results are given to demonstrate the validity and superiority of the two-dimensional algorithm.
【作者單位】: 中國科學(xué)技術(shù)大學(xué)電子工程與信息科學(xué)系;
【基金】:國家自然科學(xué)基金項目(11078016)資助
【分類號】:P111
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本文編號:1688879
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