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  本文關(guān)鍵詞：基于Hadoop平臺的天光殘留成分的自動識別與檢測　出處：《山東大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 減天光 Hadoop 天光殘留檢測 郭守敬望遠(yuǎn)鏡(LOMOST)

【摘要】：天光作為一種主要的噪聲,疊加在目標(biāo)天體光譜之中,降低了光譜的信噪比。LAMOST作為我國最大的光纖光譜望遠(yuǎn)鏡,其擁有一套完整的觀測運(yùn)行系統(tǒng)以及數(shù)據(jù)處理的流程,其中減天光作為光譜數(shù)據(jù)處理中重要的步驟之一,目的在于減去目標(biāo)光譜中疊加的天光噪聲,減天光處理過程的有效性將直接影響目標(biāo)光譜的信噪比。若經(jīng)過減天光處理之后光譜中仍含有大量強(qiáng)度高的天光殘差將不利于對目標(biāo)光譜進(jìn)行后續(xù)的分析。目前,自動識別減天光異常恒星光譜的研究較少,只能通過人工檢測的方法去尋找減天光異常的光譜,這將大大降低了檢測的效率。此外,LAMOST項目在每個觀測夜可觀測數(shù)以萬記的光譜數(shù)據(jù),因此為了提高對海量光譜數(shù)據(jù)的處理能力,需要一個可靠和高效的處理平臺。而Hadoop作為一個分布式的數(shù)據(jù)處理平臺,可以實(shí)現(xiàn)對海量光譜中出現(xiàn)減天光異常光譜進(jìn)行可靠、高效的識別與檢測。綜上,本課題主要完成以下工作:(1)首先簡要敘述LAAMOST光譜的處理流程,并分析影響減天光結(jié)果的因素,找出減天光異常光譜的特征,然后提出一種簡單有效的方法能夠自動識別LAMOST經(jīng)過Pipeline處理后仍然存在減天光異常的恒星光譜并檢測其位置。(2)基于Hadoop平臺對光譜數(shù)據(jù)進(jìn)行預(yù)處理,然后利用中值濾波算法實(shí)現(xiàn)分布式的連續(xù)譜歸一化處理,其目的在于扣除光譜中的連續(xù)譜信息,僅僅保留光譜數(shù)據(jù)中需要的譜線和噪聲信息。實(shí)驗結(jié)果表明,該算法可以有效的保留譜線信息,并且應(yīng)用Hadoop平臺大大提高了對海量光譜的處理效率。(3)利用Hadoop的高效性來檢測天光線附近是否有一定強(qiáng)度的類似發(fā)射線或吸收線的殘留來判定該天光線位置是否出現(xiàn)減天光異常,最后得出光譜中所有的減天光異常的天光位置。通過對LAMOST光譜數(shù)據(jù)的實(shí)驗表明,這種方法可以有效識別出減天光異常的光譜和發(fā)現(xiàn)不同殘留強(qiáng)度的天光線異常位置,并且該方法簡單易懂,識別效率高,可以應(yīng)用于大量的減天光異常光譜的識別與檢測問題。
[Abstract]:As a main noise, skylight is superimposed on the target celestial spectrum, which reduces the signal-to-noise ratio of the spectrum. LAMOST is the largest optical fiber spectral telescope in China. It has a complete observation operation system and data processing process, in which reducing sky light as one of the important steps in spectral data processing, the purpose is to subtract the superposition of sky noise in the target spectrum. The effectiveness of the process of reducing sky light will directly affect the SNR of target spectrum. If there are still a large number of high-intensity sky light residuals in the spectrum after subtractive sky light processing, it will be unfavorable to the subsequent analysis of target spectrum. . There are few researches on automatic recognition of the spectrum of abnormal stars, which can only be found by manual detection, which will greatly reduce the efficiency of detection. The LAMOST project can observe thousands of spectral data per observation night, so in order to improve the processing capacity of the massive spectral data. A reliable and efficient processing platform is needed, and Hadoop, as a distributed data processing platform, can reliably deal with the abnormal spectrum of reducing sky light in the massive spectrum. In summary, the main work of this paper is as follows: 1) first of all, the processing process of LAAMOST spectrum is briefly described, and the factors that affect the results of reducing sky light are analyzed. Find out the characteristics of the abnormal spectrum of subtractive sky light. Then, a simple and effective method is proposed to automatically identify the spectra of stars whose LAMOST has been processed by Pipeline and detect their positions. The spectral data is preprocessed based on Hadoop platform. Then the median filtering algorithm is used to realize the distributed continuous spectral normalization processing, which aims to deduct the continuous spectral information from the spectrum, and only retain the spectral line and noise information needed in the spectral data. The experimental results show that. The algorithm can effectively preserve the spectral line information. And the application of Hadoop platform greatly improves the processing efficiency of mass spectrum. The high efficiency of Hadoop is used to detect whether there is a similar emission line or the residue of absorption line near the sky light line to determine whether the position of the day light is abnormal or not. Finally, all the abnormal positions of reducing sky light in the spectrum are obtained, and the experimental results of LAMOST spectrum data show that. This method can effectively identify the spectrum of the sky light anomaly and find out the abnormal position of the sky light with different residual intensity, and the method is simple and easy to understand, and the recognition efficiency is high. It can be applied to the recognition and detection of a large number of abnormal spectra of reducing sky light.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P111.2

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