Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

Abstract

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify Globular Clusters (GCs) that may have a history of merging from observational data. Methods. We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After dimensionality reduction on the feature space, the resulting datapoints are fed to various classification algorithms. Using repeated random subsampling validation we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results. The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

7 pages, 5 figures, A&A accepted (research note)

References in corpus (11)

• Astrophysics Source Code Library
• Introduction to astroML: Machine Learning for Astrophysics
• Merging of globular clusters within inner galactic regions. II. The Nuclear Star Cluster formation
• Machine learning for transient discovery in Pan-STARRS1 difference imaging
• How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging
• Anomaly detection for machine learning redshifts applied to SDSS galaxies
• Feature importance for machine learning redshifts applied to SDSS galaxies
• Kinematical fingerprints of star cluster early dynamical evolution
• The construction of non-spherical models of quasi-relaxed stellar systems
• Combining human and machine learning for morphological analysis of galaxy images
• Using machine learning to classify the diffuse interstellar bands

Cited by in corpus (7)

• A critical look at the merger scenario to explain multiple populations and rotation in iron-complex globular clusters
• The not so simple stellar system Omega Cen. II. Evidence in support of a merging scenario
• A stellar census in globular clusters with MUSE. A new perspective on the multiple main sequences of $\omega$ Centauri
• Applications of Machine-Learning Algorithms for Infrared Colour Selection of Galactic Wolf-Rayet Stars
• Introducing a new multi-particle collision method for the evolution of dense stellar systems. Crash-test N-body simulations
• Measuring the spectral index of turbulent gas with deep learning from projected density maps
• Predicting Images for the Dynamics Of stellar Clusters (π-DOC): a deep learning framework to predict mass, distance and age of globular clusters