The spatial evolution of young massive clusters

Abstract

Context. There are a number of methods that identify stellar sub-structure in star forming regions, but these do not quantify the degree of association of individual stars – something which is required if we are to better understand the mechanisms and physical processes that dictate structure. Aims. We present the new novel statistical clustering tool “INDICATE” which assesses and quantifies the degree of spatial clustering of each object in a dataset, discuss its applications as a tracer of morphological stellar features in star forming regions, and to look for these features in the Carina Nebula (NGC 3372). Methods. We employ a nearest neighbour approach to quantitatively compare the spatial distribution in the local neighbourhood of an object with that expected in an evenly spaced uniform (i.e. definitively non-clustered) field. Each object is assigned a clustering index (“I”) value, which is a quantitative measure of its clustering tendency. We have calibrated our tool against random distributions to aid interpretation and identification of significant I values. Results. Using INDICATE we successfully recover known stellar structure of the Carina Nebula, including the young Trumpler 14-16, Treasure Chest and Bochum 11 clusters. Four sub-clusters contain no, or very few, stars with a degree of association above random which suggests these sub-clusters may be fluctuations in the field rather than real clusters. In addition we find: (1) Stars in the NW and SE regions have significantly different clustering tendencies, which is reflective of differences in the apparent star formation activity in these regions. Further study is required to ascertain the physical origin of the difference; (2) The different clustering properties between the NW and SE regions are also seen for OB stars and are even more pronounced; (3) There are no signatures of classical mass segregation present in the SE region – massive stars here are not spatially concentrated together above random; (4) Stellar concentrations are more frequent around massive stars than typical for the general population, particularly in the Tr14 cluster; (5) There is a relation between the concentration of OB stars and the concentration of (lower mass) stars around OB stars in the centrally concentrated Tr14 and Tr15, but no such relation exists in Tr16. We conclude this is due to the highly sub-structured nature of Tr16. Conclusions. INDICATE is a powerful new tool employing a novel approach to quantify the clustering tendencies of individual objects in a dataset within a user-defined parameter space. As such it can be used in a wide array of data analysis applications. In this paper we have discussed and demonstrated its application to trace morphological features of young massive clusters.

S2D2: Small-scale Significant substructure DBSCAN Detection

“…The same sample from Carina was also recently studied by Buckner et al (2019;henceforth B19) with the tool INDICATE, which is not a structure detection tool, but a diagnostic of the local clustering trends in a sample. INDICATE assesses the clustering tendency of each star in a sample and assigns it an index, where larger indices imply stars with a higher degree of spatial association.…”

“…The complexity involved in the dynamical evolution of such systems is huge, represented by highly non-linear models strongly dependent on the specific initial conditions (see e.g., Aarseth et al 2008;Clarke 2010). Spatial analyses of observations and simulations have been applied to density and radius estimation, membership, and multiplicity determination, as well as mass segregation (see e.g., Casertano & Hut 1985;Gomez et al 1993;Larson 1995;Maíz-Apellániz et al 2004;Cartwright & Whitworth 2004;Allison et al 2009;Parker & Goodwin 2015;Maschberger & Clarke 2011;Buckner et al 2019). In recent years, analyses have been extended to the spatio-kinematical phase space, allowing us to estimate the kinematical state of clusters and associations, generate catalogues, or distinguish between different populations within the Milky Way (see e.g., Fűrész et al 2006;Parker et al 2014;Wright et al 2014;Alfaro & González 2016;Parker & Wright 2018;Cantat-Gaudin et al 2018).…”

The spatial evolution of young massive clusters

“…Our aim with this paper series is the development and application of local statistic tools, optimised for stellar cluster analysis. In Paper I (Buckner et al 2019) we introduced the tool INDICATE (INdex to Define Inherent Clustering And TEndencies) to assess and quantify the degree of spatial clustering of each object in a dataset, demonstrating its effectiveness as a tracer of morphological stellar features in the Carina Nebula (NGC 3372) using positional data alone. In this paper we demonstrate that when combined with kinematic data from Gaia DR2, INDICATE is a powerful tool to analyse the star formation history of a cluster in a robust manner.…”

The velocity structure of Cygnus OB2

“…Spatial and dynamical structure are perhaps the most important aspects defining a star forming region, but can be difficult to interpret. A number of statistical methods have been developed to quantify different aspects of the spatial structure of these regions (Allison et al 2009;Cartwright 2009;Maschberger & Clarke 2011;Buckner et al 2019), but they don't touch upon its velocity structure. However the Velocity Structure Analysis Tool (VSAT) (Arnold & Goodwin 2019) does, and it is used in this paper to investigate the velocity structure of Cygnus OB2, which has previously had relatively little statistical kinematic analysis.…”