Journal Article

Morphology classification and photometric redshift measurement of galaxies

Yanxia Zhang, Lili Li and Yongheng Zhao

in Monthly Notices of the Royal Astronomical Society

Published on behalf of The Royal Astronomical Society

Volume 392, issue 1, pages 233-239
Published in print January 2009 | ISSN: 0035-8711
Published online December 2008 | e-ISSN: 1365-2966 | DOI:
Morphology classification and photometric redshift measurement of galaxies

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Based on the Sloan Digital Sky Survey Data Release 5 Galaxy Sample, we explore photometric morphology classification and redshift estimation of galaxies using photometric data and known spectroscopic redshifts. An unsupervised method, k-means algorithm, is used to separate the whole galaxy sample into early- and late-type galaxies. Then, we investigate the photometric redshift measurement with different input patterns by means of artificial neural networks (ANNs) for the total sample and two subsamples. The experimental result indicates that ANNs show better performance when more parameters are applied in the training set, and the mixed accuracy of photometric redshift estimation for the two subsets is superior to σz for the overall sample alone. For the optimal result, the rms deviation of photometric redshifts for the mixed sample amounts to 0.0192, that for the overall sample is 0.0196, meanwhile, that for early- and late-type galaxies adds up to 0.0164 and 0.0217, respectively.

Keywords: techniques: photometric; catalogues; surveys; galaxies: distances and redshifts - galaxies: general; galaxies: photometry

Journal Article.  4674 words.  Illustrated.

Subjects: Astronomy and Astrophysics

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