Poster abstract details

Automated Galaxy Cluster Identification for eROSITA in the Era of Big Data
Jacob Ider Chitham, Alexis Finoguenov, Johan Comparat, Matthias Klein, Nicolas Clerc, Charles Kirkpatrick, Ghazaleh Erfanianfar, Andrea Merloni, Kirpal Nandra, Joseph Mohr


The presence of galaxy clusters can be inferred through observations of extended X-ray emission originating from hot intracluster gas trapped in their gravitational potential well. Approximately $100,000$ galaxy clusters are forecasted to be detected with eROSITA (extended ROentgen Survey with an Imaging Telescope Array; Merloni et al. 2012). In order to confirm such clusters and derive accurate information about other observable properties, X-ray detections are often complemented with optical follow up methods that provide a powerful way to study the dynamics of their member galaxies via spectroscopy. In order to improve the completeness of cluster membership assignment at high redshift, the Pan-STARRS component of CODEX (COnstraining Dark Energy with X-rays; Finoguenov et al. in prep) aims to extend the capabilities of cluster identification over three quarters of the sky. Our automated Galaxy cluster identification pipeline for Pan-STARRS is based on the Multi-component Matched Filter Cluster Confirmation Tool for eROSITA developed by Klein et al (2017). The algorithm considers the spatial clustering of galaxies relative to the optical cluster centre and combines photometric information from a range of colours to determine the discrepancy or "colour-distance" for each potential member galaxy with respect to models of the expected red-sequence galaxy population. This is then combined with supervised machine learning techniques based in order to optimise spectroscopic target selection.