A Photometric Redshift Estimation Algorithm Based on the BP Neural Network Optimized by Genetic Algorithm

In addition to the spectral redshift of galaxies, the photometric redshift estimation of galaxies has important implications for the study of large-scale structures and evolution of the universe. In this paper, it chose about 150 000 galaxies' photometric and spectral data in the latest SDSS DR13 of the Sloan survey project within the spectral redshift range of Z<0.8. The SOM self organizing neural networks were used to cluster galaxies in early type galaxies and late type galaxies. And then the photometric redshift of the galaxies was predicted by the BP neural network optimized by genetic algorithm. The prediction results were compared with the spectral redshift of galaxies. The mean square error of the redshift estimation of early type galaxies was about 0.0013, and it for the late type galaxies was about 0.0017. Experimental results showed that the BP algorithm optimized by genetic algorithm was more accurate than the BP neural network algorithm, and was more efficient than K nearest neighbor and kernel regression algorithms.