Robust profile decomposition for large extragalactic spectral-line
  surveys
  We present a novel algorithm that is based on a Bayesian Markov Chain Monte
Carlo (MCMC) technique for performing robust profile analysis of a data cube
from either single-dish or interferometric radio telescopes. It fits a set of
models comprised of a number of Gaussian components given by the user to
individual line-of-sight velocity profiles, then compares them and finds an
optimal model based on the Bayesian Inference Criteria computed for each
model.The decomposed Gaussian components are then classified into bulk or
non-circular motions as well as kinematically cold or warm components. The
fitting based on the Bayesian MCMC technique is insensitive to initial
estimates of the parameters, and suffers less from finding the global minimum
in models given enough sampling points and a wide range of priors for the
parameters. It is found to provide reliable profile decomposition and
classification of the decomposed components in a fully automated way, together
with robust error estimation of the parameters as shown by performance tests
using artificial data cubes. We apply the newly developed algorithm to the HI
data cubes of sample galaxies from the Local Volume HI galaxy Survey (LVHIS).
We also compare the kinematically cold and warm components, and bulk velocity
fields with previous analyses made in a classical method.
