一种重复观测低信噪比光谱的处理方法
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摘要
目前我国LAMOST光谱巡天已发布超过760万条的天体光谱,对其中大量的低信噪比光谱的处理一直是业内公认的难题。针对天体光谱中重复观测的光谱,提出了一种新的处理方法。该方法的主要内容为:对每一组重复观测光谱,选择其红移值的差距在一定范围内的组别,然后使用一种基于信噪比加权的最优叠加方法来提高光谱的信噪比。通过对LAMOST DR4中所有重复观测光谱进行处理,证明该方法对于提高低信噪比重复观测光谱的信噪比十分有效。使7 571组恒星光谱的信噪比达到参数测量的标准,3 357组类星体和星系光谱的信噪比得到提高,平均提高率为56.38%;并且获得了43 021个双星候选体。
At present,more than 7.6 million celestial spectra has been obtained by the LAMOST spectrum sky surveys,and the processing of low SNR spectra has been regarded as a difficulty in this domain.In this paper,a new method was proposed to deal with repeated observation spectra.The process of the method was:for every group of repetitive observation spectra,selecting that whose difference of red shift was within a certain range,and then an optimal stack algorithm based on SNR weighting was run to increase the SNR.The results of the processing of all repetitive observation spectra in LAMOST DR4 showed that this method was very effective for increasing the SNR for low SNR repetitive observation spectra.This method made the SNR of the7 571 sets of stellar spectra reach the standard of parameter measurement;The SNR of the 3 357 quasar and Galaxy spectra improved at an average improvement rate of 56.38%;And 43 021 binary candidate candidates were obtained.
At present,more than 7.6 million celestial spectra has been obtained by the LAMOST spectrum sky surveys,and the processing of low SNR spectra has been regarded as a difficulty in this domain.In this paper,a new method was proposed to deal with repeated observation spectra.The process of the method was:for every group of repetitive observation spectra,selecting that whose difference of red shift was within a certain range,and then an optimal stack algorithm based on SNR weighting was run to increase the SNR.The results of the processing of all repetitive observation spectra in LAMOST DR4 showed that this method was very effective for increasing the SNR for low SNR repetitive observation spectra.This method made the SNR of the7 571 sets of stellar spectra reach the standard of parameter measurement;The SNR of the 3 357 quasar and Galaxy spectra improved at an average improvement rate of 56.38%;And 43 021 binary candidate candidates were obtained.
引文
[1]ZHAO Yong-heng(赵永恒).Physics(物理),2015,44(4):205.
[2]REN Juan-juan,LUO A-li,ZHAO Yong-heng(任娟娟,罗阿理,赵永恒).Progress in Astronomy(天文学进展),2014,32(3):462.
[3]DAI Wen(戴闻).Physics(物理),2017,46(2):90.
[4]AN Ran,PAN Jing-chang,YI Zhen-ping(安冉,潘景昌,衣振萍).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017,37(1):273.
[5]Badenes C,Hughes J P,Cassamchenai G.Astrophysical Journal,2012,680(2):1149.
[6]Dubner G,Duvidovich L,Giacani E.Astrophysical Journal,2016,819(2):98.
[7]Bhanage R,Borde S,Joshi K.Co-Operative Communication with SNR Weighted Algorithm in Cognitive Radios,2015IEEE International Conference on Pervasive Computing,2015.
[8]Wang D L,Wang J G,Dong X B.Research in Astronomy and Astrophysics,2012,12(1):509.
[9]Subekti A,Rachmana N,Suksmono A B.Physical Review B,2014,12(3):3895.
[10]Hanabata Y,Katagiri H,Hewitt J W.Astrophysical Journal,2014,786(2):86.
[2]REN Juan-juan,LUO A-li,ZHAO Yong-heng(任娟娟,罗阿理,赵永恒).Progress in Astronomy(天文学进展),2014,32(3):462.
[3]DAI Wen(戴闻).Physics(物理),2017,46(2):90.
[4]AN Ran,PAN Jing-chang,YI Zhen-ping(安冉,潘景昌,衣振萍).Spectroscopy and Spectral Analysis(光谱学与光谱分析),2017,37(1):273.
[5]Badenes C,Hughes J P,Cassamchenai G.Astrophysical Journal,2012,680(2):1149.
[6]Dubner G,Duvidovich L,Giacani E.Astrophysical Journal,2016,819(2):98.
[7]Bhanage R,Borde S,Joshi K.Co-Operative Communication with SNR Weighted Algorithm in Cognitive Radios,2015IEEE International Conference on Pervasive Computing,2015.
[8]Wang D L,Wang J G,Dong X B.Research in Astronomy and Astrophysics,2012,12(1):509.
[9]Subekti A,Rachmana N,Suksmono A B.Physical Review B,2014,12(3):3895.
[10]Hanabata Y,Katagiri H,Hewitt J W.Astrophysical Journal,2014,786(2):86.