Structural Analysis of Molecular Clouds: Dendrograms

Abstract

We demonstrate the utility of dendrograms at representing the essential features of the hierarchical structure of the isosurfaces for molecular line data cubes. The dendrogram of a data cube is an abstraction of the changing topology of the isosurfaces as a function of contour level. The ability to track hierarchical structure over a range of scales makes this analysis philosophically different from local segmentation algorithms like CLUMPFIND. Points in the dendrogram structure correspond to specific volumes in data cubes defined by their bounding isosurfaces. We further refine the technique by measuring the properties associated with each isosurface in the analysis allowing for a multiscale calculation of molecular gas properties. Using COMPLETE 13CO(1-0) data from the L1448 region in Perseus and mock observations of a simulated data cube, we identify regions that have a significant contribution by self-gravity to their energetics on a range of scales. We find evidence for self-gravitation on all spatial scales in L1448 though not in all regions. In the simulated observations, nearly all of the emission is found in objects that would be self-gravitating if gravity were included in the simulation. We reconstruct the size-line width relationship within the data cube using the dendrogram-derived properties and find it follows the standard relation: s_v ~ R^0.58. Finally, we show that constructing the dendrogram of CO J=1-0 emission from the Orion-Monoceros region allows for the identification of giant molecular clouds in a blended molecular line data set using only a physically motivated definition (self-gravitating clouds with masses 5x10^4 Msun.Comment: 15 pages, 16 figures. Accepted to ApJ. Paper will full resolution figures available at http://people.ok.ubc.ca/erosolo/dendrograms.pd

SDC13 infrared dark clouds: Longitudinally collapsing filaments?

“…The flux uncertainties in Table 1 reflect the difficulty of estimating the separation between the sources and the underlying filament. It is calculated by taking the average of the clipping and the bijection schemes of the flux estimates (Rosolowsky et al 2008). We characterised the compact sources further by checking their association with Spitzer 24 µm point-like sources, and their fragmentation as seen in the 8 µm extinction map (see Fig.…”

ALMA Observations of a Quiescent Molecular Cloud in the Large Magellanic Cloud

“…To identify structures within the ALMA spectral line cubes, we used the Python package astrodendro, which decomposes emission into a hierarchy of structures (Rosolowsky et al 2008;Shetty et al 2012;Colombo et al 2015). Parameters were chosen so that the algorithm identified local maxima in the cube above the 3σ rms level that were also at least 2.5σ rms above the merge level with adjacent structures.…”

“…All properties are determined using the "bijection" approach discussed by Rosolowsky et al (2008), which associates all emission bounded by an isosurface with the identified structure. From these basic properties we have calculated additional properties, including the effective rms spatial size, σ r = √ σ maj σ min , the spherical radius R = 1.91σ r ,…”

APEX/SABOCA observations of small-scale structure of infrared-dark clouds

“…For consistency, we adopt the sizebased terminology given to these scales in Bergin & Tafalla (2007): a "core" ranges in size between 0.03 and 0.2 pc and a "clump" from 0.3 to 3 pc, and both entities are found within the boundaries of several parsec sized "clouds". The SABOCA angular resolution accesses the "core" scale in our sample of IRDCs.One method that quantitatively describes this hierarchy is a structure tree algorithm, popularly implemented in astronomy with the dendrogram representation (Rosolowsky et al 2008). An advantage to using this technique is that it imposes no assumptions about the shape or emission profile of the tree structures, and it operates on both two and three dimensional datasets.…”