Morphology Classification and Photometric Redshift Measurement of Galaxies TeX Export

@article{citeulike:3381101, abstract = {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 the two subsamples. The experimental result indicates that ANNs show better performance when the more parameters are applied in the training set, and the mixed accuracy \$\sigma\_{mix}=\sqrt{{\sigma\_{early}}^2+{\sigma\_{late}}^2}\$ of photometric redshift estimation for the two subsets is superior to \$\sigma\_{z}\$ 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.}, archivePrefix = {arXiv}, author = {Zhang, Yanxia and Li, Lili and Zhao, Yongheng}, citeulike-article-id = {3381101}, citeulike-linkout-0 = {http://arxiv.org/abs/0810.0867}, citeulike-linkout-1 = {http://arxiv.org/pdf/0810.0867}, day = {6}, eprint = {0810.0867}, keywords = {classification, morphology, phot-z}, month = {Oct}, posted-at = {2008-10-07 06:17:54}, priority = {2}, title = {Morphology Classification and Photometric Redshift Measurement of Galaxies}, url = {http://arxiv.org/abs/0810.0867}, year = {2008} }

TY - JOUR ID - citeulike:3381101 L3 - citeulike-article-id:3381101 N2 - 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 the two subsamples. The experimental result indicates that ANNs show better performance when the more parameters are applied in the training set, and the mixed accuracy $\sigma_{mix}=\sqrt{{\sigma_{early}}^2+{\sigma_{late}}^2}$ of photometric redshift estimation for the two subsets is superior to $\sigma_{z}$ 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. TI - Morphology Classification and Photometric Redshift Measurement of Galaxies KW - classification KW - morphology KW - phot-z AU - Zhang, Yanxia AU - Li, Lili AU - Zhao, Yongheng PY - 2008/Oct/06/ UR - http://arxiv.org/abs/0810.0867 ER -