Identifying kinematic structures in simulated galaxies using unsupervised machine learning

TL;DR

This paper introduces an unsupervised machine learning method, auto-GMM, to identify and decompose intrinsic kinematic structures in simulated galaxies, aiding understanding of galaxy formation.

Contribution

The paper presents a novel auto-GMM algorithm that effectively isolates galaxy components based on kinematic phase space in cosmological simulations.

Findings

Successfully identifies four galaxy structures: cold disks, warm disks, bulges, halos.

Effectively decomposes most galaxy structures in simulations.

Fails to identify structures in barred galaxies due to complex kinematics.

Abstract

Galaxies host a wide array of internal stellar components, which need to be decomposed accurately in order to understand their formation and evolution. While significant progress has been made with recent integral-field spectroscopic surveys of nearby galaxies, much can be learned from analyzing the large sets of realistic galaxies now available through state-of-the-art hydrodynamical cosmological simulations. We present an unsupervised machine learning algorithm, named auto-GMM, based on Gaussian mixture models, to isolate intrinsic structures in simulated galaxies based on their kinematic phase space. For each galaxy, the number of Gaussian components allowed by the data is determined through a modified Bayesian information criterion. We test our method by applying it to prototype galaxies selected from the cosmological simulation IllustrisTNG. Our method can effectively decompose most galactic structures. The intrinsic structures of simulated galaxies can be inferred statistically by non-human supervised identification of galaxy structures. We successfully identify four kinds of intrinsic structures: cold disks, warm disks, bulges, and halos. Our method fails for barred galaxies because of the complex kinematics of particles moving on bar orbits.