Reliable photometric membership (RPM) of galaxies in clusters - I. A machine learning method and its performance in the local universe

Abstract

We introduce a new method to determine galaxy cluster membership based solely on photometric properties.We adopt amachine learning approach to recover a clustermembership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to Mr∗ + 3, we limit our work to 30 clusters with zphot-cl ≤ 0.045. Masses (M200) are larger than ~ 0.6 × 1014M⊙ (most above 3 × 1014M⊙). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C ~ 92 per cent and purity, P ~ 87 per cent) within R200, so that we named our code reliable photometric membership.We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.