CASIA OpenIR  > 09年以前成果
Redshift determination for quasar based on similarity measure
Duan, FQ; Wu, FH; Singh, S; Singh, M; Apte, C; Perner, P
Source PublicationPATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS
2005
Volume3686Pages:529-537
SubtypeArticle
AbstractWith the advent of very large redshift surveys, automatic redshift measurement is becoming increasingly important. This paper presents a similarity measure based cross-correlation method for the redshift determination of quasar spectra. Cross-correlation is measured only for the redshift candidates that are determined by the emission line features of the observed spectrum. The similarity measure is defined as the weighted sum of several similarity evidences. Compared with the traditional cross-correlation based methods, our method can be used for higher redshift determination. Compared with the methods based on spectral line matching, our method is less sensitive to the quality of spectral line extraction. Experiment results indicate the high performance of the method.
WOS HeadingsScience & Technology ; Technology
Indexed ByISTP ; SCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000232247900058
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/9188
Collection09年以前成果
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Duan, FQ,Wu, FH,Singh, S,et al. Redshift determination for quasar based on similarity measure[J]. PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS,2005,3686:529-537.
APA Duan, FQ,Wu, FH,Singh, S,Singh, M,Apte, C,&Perner, P.(2005).Redshift determination for quasar based on similarity measure.PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS,3686,529-537.
MLA Duan, FQ,et al."Redshift determination for quasar based on similarity measure".PATTERN RECOGNITION AND DATA MINING, PT 1, PROCEEDINGS 3686(2005):529-537.
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