Unsupervised clustering and analysis of WISE spiral galaxies

Abstract

ABSTRACT The Wide-Field Infrared Explorer (WISE) survey captured massive amounts of infrared astronomical object data, and different astronomical objects will show different characteristics. Especially spiral galaxies that are richer in colour than other astronomical objects. In addition, the Sloan Digital Sky Survey (SDSS) has obtained a large number of visible light images and their parameter. So this paper mainly explores the colour characteristics of WISE spiral galaxies, and uses SDSS, 2MASS, WISE, and MANGA parameters to analyse the differences between different classes and the commonality of the same class, so that provides help for astronomers to study infrared spiral galaxies. First, the RA and Dec. of the spiral galaxy samples were crossed in GalaxyZoo2. The flux data of w1, w2, and w3 bands in WISE were crossed for image synthesis. The Bootstrap Your Own Latent contrastive learning framework and K-means clustering were used to unsupervised classify the infrared spiral galaxy images. After multiple experiments, five classes of images were selected by referring to the best clustering results. Finally, the parameter of galaxies in SDSS, 2MASS, WISE, and MANGA catalogues were crossed, including redshift, 12 magnitudes that from visible light band to mid-infrared band, stellar formation rate, stellar metallicity, stellar velocity dispersion, etc. These parameters were qualitatively and quantitatively analyzed. The effectiveness of unsupervised clustering algorithms for handling unlabeled data is demonstrated and two special classes of galaxies are found. The analysis result shows that the distribution characteristics of different parameters of different classes of infrared spiral galaxies are different.