Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra—a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of E0, S0, Sa, Sb types with redshift ranging from 0 to 1.2, and the test set of stellar spectra from Pickles' atlas and SDSS spectra of normal galaxies with SNR of 13. Experiments show that our SMM-RBFNN is more efficient in both the training and testing stages than the BPNN (back propagation neural networks), and more importantly, it can achieve a good classification accuracy of 99.22% and 96.52%, respectively for stars and normal galaxies.