Mass segregation and fractal substructure in young massive clusters: (I) the McLuster code and method calibration
A.H.W. Kuepper, Th. Maschberger, P. Kroupa, H. Baumgardt

TL;DR
This paper introduces the McLuster code for modeling young massive star clusters and compares various methods for detecting mass segregation and substructure, highlighting their reliability and limitations.
Contribution
The study presents the publicly available McLuster code and systematically evaluates different techniques for quantifying mass segregation and substructure in star clusters.
Findings
Mass function slope method is most reliable for detecting mass segregation.
Projected azimuthal density profile is highly sensitive to substructure.
Q parameter's effectiveness is influenced by radial density gradient and binaries.
Abstract
By analysing models of the young massive cluster R136 in 30 Doradus, set-up using the herewith introduced and publicly made available code McLuster, we investigate and compare different methods for detecting and quantifying mass segregation and substructure in non-seeing limited N-body data. For this purpose we generate star cluster models with different degrees of mass segregation and fractal substructure and analyse them. We quantify mass segregation by measuring, from the projected 2d model data, the mass function slope in radial annuli, by looking for colour gradients in radial colour profiles, by measuring Allison's Lambda parameter, and by determining the local stellar surface density around each star. We find that these methods for quantifying mass segregation often produce ambiguous results. Most reliable for detecting mass segregation is the mass function slope method, whereas…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
