ESTIMATING PHOTOMETRIC REDSHIFTS OF QUASARS VIA THE k -NEAREST NEIGHBOR APPROACH BASED ON LARGE SURVEY DATABASES

We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the...

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Bibliographic Details
Published in:The Astronomical journal 2013-08, Vol.146 (2), p.1-10
Main Authors: Zhang, Yanxia, Ma, He, PENG, NANBO, Zhao, Yongheng, Wu, Xue-Bing
Format: Article
Language:English
Subjects:
ACCURACY
ALGORITHMS
ASTRONOMY
ASTROPHYSICS
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Bands
COMPARATIVE EVALUATIONS
DISTANCE
Estimating
GALAXIES
Infrared
Learning
PHOTOMETRY
QUASARS
RED SHIFT
SKY
Sky surveys (astronomy)
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Summary:We apply one of the lazy learning methods, the k-nearest neighbor (kNN) algorithm, to estimate the photometric redshifts of quasars based on various data sets from the Sloan Digital Sky Survey (SDSS), the UKIRT Infrared Deep Sky Survey (UKIDSS), and the Wide-field Infrared Survey Explorer (WISE; the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample, and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN performs best when k is different with a special input pattern for a special data set. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results generally show that the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photomeude redshift estimation, which is met by many machine learning methods. Compared with the performance of various other methods of estimating the photometric redshifts of quasars, kNN based on KD-Tree shows superiority, exhibiting the best accuracy.
ISSN:0004-6256
1538-3881
1538-3881
DOI:10.1088/0004-6256/146/2/22