摘要
In order to find the physical parameters which determine the accuracy of pho- tometric redshifts, we compare the spectroscopic and photometric redshifts （photo-z＇s） for a large sample of -80000 SDSS-2MASS galaxies. Photo-z＇s in this paper are estimated by using the artificial neural network photometric redshift method （ANNz）. For a subset of -40000 randomly selected galaxies, we find that the photometric redshift recovers the spectroscopic redshift distribution very well with rms of 0.016. Our main results are as follows： （1） Using magnitudes directly as input parameters produces more accurate photo-z＇s than using colors; （2） The inclusion of 2MASS （J, H, Ks） bands does not improve photo-z＇s significantly, which indicates that near infrared data might not be important for the low-redshift sample; （3） Adding the concentration index （essentially the steepness of the galaxy brightness profile） as an extra input can improve the photo-z＇s estimation up to -10 percent; （4） Dividing the sample into early- and late-type galaxies by using the concentration index, normal and abnormal galaxies by using the emission line flux ratios, and red and blue galaxies by using color index （g - r）, we can improve the accuracy of photo-z＇s significantly; （5） Our analysis shows that the outliers （where there is a big difference between the spectroscopic and photometric redshifts） are mainly correlated with galaxy types, e.g., most outliers are late-type （blue） galaxies.
In order to find the physical parameters which determine the accuracy of pho- tometric redshifts, we compare the spectroscopic and photometric redshifts （photo-z＇s） for a large sample of -80000 SDSS-2MASS galaxies. Photo-z＇s in this paper are estimated by using the artificial neural network photometric redshift method （ANNz）. For a subset of -40000 randomly selected galaxies, we find that the photometric redshift recovers the spectroscopic redshift distribution very well with rms of 0.016. Our main results are as follows： （1） Using magnitudes directly as input parameters produces more accurate photo-z＇s than using colors; （2） The inclusion of 2MASS （J, H, Ks） bands does not improve photo-z＇s significantly, which indicates that near infrared data might not be important for the low-redshift sample; （3） Adding the concentration index （essentially the steepness of the galaxy brightness profile） as an extra input can improve the photo-z＇s estimation up to -10 percent; （4） Dividing the sample into early- and late-type galaxies by using the concentration index, normal and abnormal galaxies by using the emission line flux ratios, and red and blue galaxies by using color index （g - r）, we can improve the accuracy of photo-z＇s significantly; （5） Our analysis shows that the outliers （where there is a big difference between the spectroscopic and photometric redshifts） are mainly correlated with galaxy types, e.g., most outliers are late-type （blue） galaxies.
基金
Supported by the National Natural Science Foundation of China
