Poster
A. S. C. Carvalho, A. Krone-Martins, A. C. da Silva
Abstract
Galaxy Clusters are essential objects to study galaxy evolution as well as the dark sector of the universe. However, one of the biggest challenges of this study is to know which galaxies belong to the cluster and which galaxies are field galaxies, using the less possible ammount of a priori information on what a cluster is and not knowing the precision and accuracy of the individual distance of each galaxy. UPMASK, or Unsupervised Photometric Membership Assignment in Stellar Clusters, is a method created to study star clusters, when the distance is not known or poorly determined. This method uses heuristics and statistical analysis to separate a cluster from the field, without any basis on theoretical models, and consequently without strong a priori statements, of what a cluster is made of. It operates with minimal information from astrometry and photometry. In this poster we will show the results of a modified version of UPMASK optimized to study galaxy clusters. We present our findings about the potential of applying the UPMASK to the study of galaxy clusters without prior knowledge about their galaxy redshift or model distributions. We test the method using simulations that include the luminosity bands of DES and the forthcoming ESA/Euclid space mission.
IAU Symposium 348: 21st Century Astrometry: crossing the Dark and Habitable frontiers
Vienna, Austria
2018 August
