The PAU survey: classifying low-z SEDs using Machine Learning clustering

Abstract

We present an application of unsupervised Machine Learning clustering to the PAU survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups in the data SEDs. We find 12 groups from a total number of 5234 targets in the survey at 0.01 < z < 0.28. Among the groups, 3545 galaxies (68 per cent) show emission lines in the SEDs. These groups also include 1689 old galaxies with no active star formation. We have fitted the SED to every single galaxy in each group with CIGALE. The mass, age, and specific star formation rates (sSFR) of the galaxies range from 0.15 <11; 6 <11.26, and -14.67