C2-GaMe: Classification of Cluster Galaxy Membership with Machine Learning

Abstract

We present Classification of Cluster GAlaxy MEmbers (C${}^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using phase space information as input. We train and test C${}^2$-GaMe with the galaxies from UniverseMachine mock catalog based on Multi-Dark Planck 2 N-body simulations. We show that probabilistic classification is superior to deterministic classification in estimating the physical properties of clusters, including density profiles and velocity dispersion. We propose a set of estimators to get an unbiased estimation of cluster properties. We demonstrate that C${}^2$-GaMe can recover the distribution of orbiting and infalling galaxies' position and velocity distribution with $<1\%$ statistical error when using probabilistic predictions in the presence of interlopers in the projected phase space. Additionally, we demonstrate the robustness of trained models by applying them to a different simulation. Finally, adding a specific star formation rate and the ratio of the galaxy's halo mass to the cluster's halo mass as additional features improves the classification performance. We discuss potential applications of this technique to enhance cluster cosmology and galaxy quenching.

Accepted in Astronomy and Computing. Some figures are downsampled from journal version to appear correctly in Chrome

References in corpus (16)

• Array Programming with NumPy
• Simulating galaxy formation with black hole driven thermal and kinetic feedback
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Rotation-Dependent Catastrophic Disruption of Gravitational Aggregates
• Virial Scaling of Massive Dark Matter Halos: Why Clusters Prefer a High Normalization Cosmology
• On the efficiency and reliability of cluster mass estimates based on member galaxies
• The Unorthodox Orbits of Substructure Halos
• Mass Calibration and Cosmological Analysis of the SPT-SZ Galaxy Cluster Sample Using Velocity Dispersion ${\sigma }_v$ and X-ray $Y_{\text{X}}$ Measurements
• Machine Learning in Astronomy: a practical overview
• Interloper treatment in dynamical modelling of galaxy clusters
• The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy
• ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
• KLLR: A scale-dependent, multivariate model class for regression analysis
• Reliable Photometric Membership (RPM) of Galaxies in Clusters. I. A Machine Learning Method and its Performance in the Local Universe
• Accurate Model of the Projected Velocity Distribution of Galaxies in Dark Matter Halos
• Measuring Cosmological Distances Using Cluster Edges as a Standard Ruler