Graph-based interpretation of the Molecular Interstellar Medium Segmentation

TL;DR

This paper introduces SCIMES, a spectral clustering method for segmenting molecular clouds in interstellar medium data, improving robustness and physical relevance over traditional pixel-based approaches.

Contribution

SCIMES applies spectral clustering to dendrograms of molecular emission, enabling more accurate and physically meaningful segmentation of interstellar molecular clouds.

Findings

SCIMES closely reproduces visually identified clouds.

It is robust against parameter changes and noise.

It avoids over-segmentation in high-resolution data.

Abstract

We present a generalization of the Giant Molecular Cloud (GMC) identification problem based on cluster analysis. The method we designed, SCIMES (Spectral Clustering for Interstellar Molecular Emission Segmentation) considers the dendrogram of emission in the broader framework of graph theory and utilizes spectral clustering to find discrete regions with similar emission properties. For Galactic molecular cloud structures, we show that the characteristic volume and/or integrated CO luminosity are useful criteria to define the clustering, yielding emission structures that closely reproduce "by-eye" identification results. SCIMES performs best on well-resolved, high-resolution data, making it complementary to other available algorithms. Using 12CO(1-0) data for the Orion-Monoceros complex, we demonstrate that SCIMES provides robust results against changes of the dendrogram-construction parameters, noise realizations and degraded resolution. By comparing SCIMES with other cloud decomposition approaches, we show that our method is able to identify all canonical clouds of the Orion-Monoceros region, avoiding the over-division within high resolution survey data that represents a common limitation of several decomposition algorithms. The Orion-Monoceros objects exhibit hierarchies and size-line width relationships typical to the turbulent gas in molecular clouds, although "the Scissors" region deviates from this common description. SCIMES represents a significant step forward in moving away from pixel-based cloud segmentation towards a more physical-oriented approach, where virtually all properties of the ISM can be used for the segmentation of discrete objects.