COSMIC: A Galaxy Cluster Finding Algorithm Using Machine Learning

TL;DR

COSMIC is a machine learning-based algorithm designed to efficiently detect galaxy clusters, leveraging SDSS data to identify brightest cluster galaxies and estimate cluster richness, showing high completeness and strong correlation with existing catalogs.

Contribution

The paper introduces COSMIC, a novel machine learning algorithm for galaxy cluster detection that improves efficiency and accuracy over previous methods.

Findings

High completeness in cluster detection

Strong correlation with existing optical and X-ray measurements

Robust performance demonstrated on test data

Abstract

Building a comprehensive catalog of galaxy clusters is a fundamental task for the studies on the structure formation and galaxy evolution. In this paper, we present COSMIC (Cluster Optical Search using Machine Intelligence in Catalogs), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, including the identification of the brightest cluster galaxies and the estimation of the cluster richness. We train our models on the galaxy data from the Sloan Digital Sky Survey and WHL galaxy cluster catalog. Validated to a test data in the region of northern Galactic cap, COSMIC algorithm demonstrates a high completeness when cross-matching with previous cluster catalogs. Richness comparison with previous optical and X-ray measurements also demonstrated a tight correlation. Our methodology showcases robust performance in galaxy cluster detection and holds promising prospects for applications in upcoming large-scale surveys.