Looking for phase-space structures in star-forming regions: An MST-based methodology

TL;DR

This paper introduces a new MST-based method to detect kinematic groupings in star-forming regions by analyzing phase space structures, specifically targeting radial velocity segregations.

Contribution

The paper presents a novel MST-based kinematic segregation index for identifying phase space structures in star-forming regions, enhancing detection of spatially concentrated velocity groups.

Findings

Method successfully detects kinematic groupings in simulated data.

The approach visualizes the spectrum of kinematic behaviors effectively.

Numerical tests confirm the method's potential for real data analysis.

Abstract

We present a method for analysing the phase space of star-forming regions. In particular we are searching for clumpy structures in the 3D subspace formed by two position coordinates and radial velocity. The aim of the method is the detection of kinematic segregated radial velocity groups, that is, radial velocity intervals whose associated stars are spatially concentrated. To this end we define a kinematic segregation index, $\tilde{\Lambda}$(RV), based on the Minimum Spanning Tree (MST) graph algorithm, which is estimated for a set of radial velocity intervals in the region. When $\tilde{\Lambda}$(RV) is significantly greater than 1 we consider that this bin represents a grouping in the phase space. We split a star-forming region into radial velocity bins and calculate the kinematic segregation index for each bin, and then we obtain the spectrum of kinematic groupings, which enables a quick visualization of the kinematic behaviour of the region under study. We carried out numerical models of different configurations in the subspace of the phase space formed by the coordinates and the radial velocity that various case studies illustrate. The analysis of the test cases demonstrates the potential of the new methodology for detecting different kind of groupings in phase space.