Report on Optimal Substructure Techniques for Stellar, Gas and Combined Samples

TL;DR

This paper reviews clustering algorithms and substructure detection techniques for analyzing the spatial and kinematic properties of stars and gas in molecular clouds, aiding the study of star formation regions.

Contribution

It provides a comprehensive overview of optimal substructure techniques applied to stellar and gas samples within an astrophysical context, specifically for the StarFormMapper project.

Findings

Reviewed various clustering algorithms for astrophysical data

Analyzed substructure detection methods for stars and gas

Applied techniques to study star cluster evolution

Abstract

This document aims at reviewing the different types of clustering algorithms and substructures detection techniques in order to study the spatial and kinematic clustering of stars and detect the gas components in molecular clouds. It is the deliverable: Report on Optimal Substructure Techniques for Stellar, Gas and Combined Samples, for the EU H2020 (COMPET-5-2015 - Space) project (A Gaia and Herschel Study of the Density Distribution and Evolution of Young Massive Star Clusters), Grant Agreement Number: 687528, with abbreviated code name StarFormMapper (SFM) project. The document is organized in the following sections: 1. General Introduction 2. Clustering of Discrete Distributions 3. Clustering of Continuous Distributions 4. Clustering in Astrophysics 5. StarFormMapper 6. Summary and Conclusions