Application of Principal Component Analysis to Large-Scale Spectral Line Imaging Studies of the Interstellar Medium
M. H. Heyer, F. P. Schloerb;
ApJ, 1997, 475, 173
ABSTRACT:The multivariate statistical technique of principal component analysis (PCA) is described and demonstrated to be a valuable tool to
consolidate the large amount of information obtained with spectroscopic imaging
observations of the interstellar medium. Simple interstellar cloud models with
varying degrees of complexity and Gaussian noise are constructed and analyzed
to demonstrate the ability of PCA to statistically extract physical
features and phenomena from the data and to gauge the effects of random noise upon
the analysis. Principal components are calculated for high spatial
dynamic range 12CO and 13CO data cubes of the Sh 155 (Cep OB3) cloud
complex. These identify the three major emission components within the cloud and the
spatial differences between 12CO and 13CO emissions. Higher order
eigenimages identify small velocity fluctuations and therefore provide spatial
information to the turbulent velocity field within the cloud.
A size line width
relationship delta v ~ R alpha is derived from spatial and kinematic
characterizations of the principal components of 12CO emission from the Sh 155, Sh 235, Sh
140, and Gem OB1 cloud complexes.
The power-law indices for these clouds
range from 0.42 to 0.55 and are similar to those derived from an ensemble of
clouds within the Galaxy found by Larson (1981) and Solomon et al. (1987).
The size--line width relationship within a given cloud provides an
important diagnostic to the variation of kinetic energy with size scale within
turbulent flows of the interstellar medium.
KEYWORDS: ism: clouds, ism: molecules, ism: kinematics and dynamics, methods: statistical, methods: data analysis
PERSOKEY:turbulence, ,
CODE: heyer97