Fine-grained photometric classification using multi-model fusion method with redshift estimation

Abstract

The modern sky surveys accelerates astronomical data collection. We proposed a multi-model fusion method aimed at comprehensive and fine-grained astronomical source classification. This method incorporates a redshift estimation model using the mixture density network into a source classification model. Based on 1.2 million sources from the SDSS and the ALLWISE, we performed three-class experiments for stars, quasars, and galaxies, four-class experiments to further classify galaxies into normal and emission-line galaxies (NGs; ELGs), and seven-class experiments where ELG were refined into active galactic nuclei (AGNs), broad-line galaxies (BLs), star-forming galaxies (SFs), and starburst galaxies (SBs). In all experiments, our proposed method is superior to direct classification. In three- and four-class, we obtains 0.77% and 1.14% improvement in accuracy, demonstrating the effectiveness of adding redshift estimation. Meanwhile, three machine learning algorithms were stacked into one by us to finish fine-grained classification, which achieved an accuracy of 78.5%, with F1 scores of 99.2% for stars, 97% for quasars, 64.3% for NGs, 60.8% for AGNs, 68.3% for BLs, 87.2% for SBs, and 71.3% for SFs. The NMAD and $R^2$ for the redshift estimation part of our method are 0.18 and 0.916, while it has only 2.65% outliers. The method we proposed further mines the information contained in the photometry to achieve comprehensive and fine-grained classification, which will be beneficial for immediate analysis in large-scale surveys. Besides, this method can leverage feature importance to stimulate new insights for astronomers.