Using Machine Learning to Identify Extragalactic Globular Cluster Candidates from Ground-Based Photometric Surveys of M87

Abstract

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's well-studied GC system to implement supervised machine learning (ML) classification algorithms - specifically random forest and neural networks - to identify GCs from foreground stars and background galaxies using ground-based photometry from the Canada-France-Hawai'i Telescope (CFHT). We compare these two ML classification methods to studies of "human-selected" GCs and find that the best performing random forest model can reselect 61.2% $\pm$ 8.0% of GCs selected from HST data (ACSVCS) and the best performing neural network model reselects 95.0% $\pm$ 3.4%. When compared to human-classified GCs and contaminants selected from CFHT data - independent of our training data - the best performing random forest model can correctly classify 91.0% $\pm$ 1.2% and the best performing neural network model can correctly classify 57.3% $\pm$ 1.1%. ML methods in astronomy have been receiving much interest as Vera C. Rubin Observatory prepares for first light. The observables in this study are selected to be directly comparable to early Rubin Observatory data and the prospects for running ML algorithms on the upcoming dataset yields promising results.

14 pages, 9 figures, accepted to MNRAS

References in corpus (13)

• The Gaia mission
• A Supermassive Black Hole in an Ultracompact Dwarf Galaxy
• A nearby repeating fast radio burst in the direction of M81
• MegaPipe: the MegaCam image stacking pipeline at the Canadian Astronomical Data Centre
• Robust Machine Learning Applied to Astronomical Datasets I: Star-Galaxy Classification of the SDSS DR3 Using Decision Trees
• The Next Generation Virgo Cluster Survey. VI. The Kinematics of Ultra-compact Dwarfs and Globular Clusters in M87
• Formation histories of stars, clusters and globular clusters in the E-MOSAICS simulations
• Global Properties of the Globular Cluster Systems of Four Spiral Galaxies
• The SLUGGS Survey: a catalog of over 4000 globular cluster radial velocities in 27 nearby early-type galaxies
• StarcNet: Machine Learning for Star Cluster Identification
• Estimating Photometric Redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques
• Three Ultraluminous X-ray Sources Hosted by Globular Clusters in NGC 1316
• A Machine Learning Approach For Classifying Low-mass X-ray Binaries Based On Their Compact Object Nature