本文關(guān)鍵詞：基于高性能計(jì)算的LAMOST連續(xù)譜異常光譜的自動(dòng)識(shí)別研究　出處：《山東大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 連續(xù)譜異常 Lick線(xiàn)指數(shù) 模板匹配 連續(xù)譜擬合 高性能計(jì)算平臺(tái)

【摘要】：隨著LAMOST光譜觀(guān)測(cè)工作的持續(xù)開(kāi)展,光譜處理流程效率和準(zhǔn)確率的逐步提高,拍攝的光譜質(zhì)量也在不斷提高。按照每個(gè)像素信噪比大于10作為檢測(cè)光譜達(dá)標(biāo)標(biāo)準(zhǔn),達(dá)到要求的光譜占觀(guān)測(cè)光譜的70%左右,而在正式發(fā)布的數(shù)據(jù)中也存在很多低質(zhì)量光譜,在低質(zhì)量光譜中占比例較大的就是連續(xù)譜異常光譜。連續(xù)譜異常是指恒星光譜在獲得和處理過(guò)程中由于星際消光和流量定標(biāo)等原因造成連續(xù)譜嚴(yán)重偏離黑體譜甚至中斷的現(xiàn)象,這對(duì)光譜的譜線(xiàn)提取以及其他一些光譜研究工作帶來(lái)負(fù)面影響,對(duì)連續(xù)譜異常光譜的研究亟待解決。LAMOST每個(gè)觀(guān)測(cè)夜產(chǎn)生大約20,000-40,000條光譜數(shù)據(jù),數(shù)據(jù)量級(jí)的不斷增大使得對(duì)光譜處理流程自動(dòng)化的要求越來(lái)越高,對(duì)異常光譜的處理也應(yīng)達(dá)到更加高效的要求。本課題就是在此背景下提出來(lái)的,本文提出了一種基于距離度量的連續(xù)譜異常光譜的自動(dòng)檢測(cè)方法,主要工作有:(1)提取恒星光譜連續(xù)譜。分別提取待測(cè)光譜和對(duì)應(yīng)類(lèi)型模板光譜的連續(xù)譜,同時(shí)進(jìn)行流量插值和歸一化處理,使待測(cè)光譜和模板光譜處于相同的量綱下。(2)連續(xù)譜模板匹配。對(duì)待測(cè)光譜進(jìn)行模板匹配,對(duì)(1)中提取的連續(xù)譜,在每個(gè)波長(zhǎng)點(diǎn)計(jì)算待測(cè)光譜及其模板光譜的流量差值,分析流量差值的分布,檢驗(yàn)有多少差值點(diǎn)分布在均值(β)附近的± a個(gè)標(biāo)準(zhǔn)差(δ)的范圍內(nèi),進(jìn)而確定是否有連續(xù)譜異常。(3)對(duì)待測(cè)光譜進(jìn)行恒星光譜類(lèi)型分類(lèi)。由于大部分異常光譜的恒星類(lèi)型未確定,而模板匹配的前提是已知待測(cè)光譜的恒星類(lèi)型,本文采用測(cè)量模板光譜和待測(cè)光譜的Lick線(xiàn)指數(shù)之間歐式距離的方法,定義待測(cè)光譜類(lèi)型為與其距離最近的模板光譜的類(lèi)型。(4)在高性能平臺(tái)下實(shí)現(xiàn)連續(xù)譜自動(dòng)識(shí)別方法的并行計(jì)算。研究了Python在高性能平臺(tái)的并行計(jì)算方法,通過(guò)調(diào)用MPI,將若干個(gè)FITS文件分給多個(gè)處理器核心,每個(gè)核心獨(dú)立順序執(zhí)行,當(dāng)最后一個(gè)核心計(jì)算完畢時(shí),指定某個(gè)進(jìn)程(一般是root)將各個(gè)核心的執(zhí)行結(jié)果收集起來(lái)。總結(jié)來(lái)說(shuō),本文在高性能計(jì)算平臺(tái)下實(shí)現(xiàn)的連續(xù)譜異常光譜自動(dòng)識(shí)別方法,相比于單機(jī)計(jì)算達(dá)到了更加快速高效識(shí)別的效果。
[Abstract]:With the continuous development of the LAMOST spectral observation, the efficiency and accuracy of the spectral processing process are gradually improved, and the spectral quality of the photographed is also increasing. According to the signal to noise ratio greater than 10 of each pixel as the standard of the detection spectrum, the required spectrum accounts for about 70% of the observed spectrum, and there are also many low quality spectra in the officially released data. The larger proportion of the spectrum in the low quality spectrum is the continuous spectrum abnormal spectrum. Continuous spectrum anomaly refers to stellar spectra in obtaining and processing due to interstellar extinction and flow calibration caused by continuous spectrum of serious deviation from the blackbody spectrum even interrupted phenomenon, the spectral line extraction and the negative effects of some other research work on the spectrum, continuous spectrum spectral anomalies to be solved. LAMOST generates about 20000-40000 spectral data per observation night. The increasing data level makes the requirement of the automation of the spectral processing process more and more high, and the treatment of abnormal spectra should also achieve more efficient requirements. This topic is put forward under this background. In this paper, a distance spectrum based automatic detection method for abnormal spectrum of continuous spectrum is presented. The main works are as follows: (1) extraction of continuous spectrum of stars. The continuous spectrum of the measured spectrum and the corresponding type template spectrum is extracted respectively, and the flow interpolation and normalization processing are carried out at the same time, so that the measured spectrum and the template spectrum are in the same dimension. (2) continuous spectral template matching. The spectrum measured by template matching to treat, (1) continuous spectrum extraction, calculation of flow difference spectra and template spectra measured at each wavelength point, analysis of the distribution of the flow difference value, the number of test point distribution in mean difference (P) near the a + standard deviation (delta) range, and to determine whether there is a continuous spectrum anomaly. (3) to classify the spectral types of the stars with the spectrum. The most anomalous spectral types of stars is determined, and the premise of template matching is known to be measured spectral types of stars, this paper uses the method of Euclidean distance between the measured template spectra and measured the Lick line index is defined to be tested for its spectral type type template of the nearest distance. (4) the parallel computation of the method of automatic recognition of continuous spectrum is realized under the high performance platform. The study of the Python in high performance parallel computing platform, by calling MPI, a plurality of FITS files to multiple processor cores, each core independent sequence, when the last core to complete the calculation, specify a process (usually root) the results of the implementation of the core collection. In conclusion, the continuous spectrum automatic recognition method based on high performance computing platform achieves faster and more efficient recognition than single machine computing.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：P144.1;TP38

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本文編號(hào)：1339027