Probabilistic detection of spectral line components
  Resolved kinematical information, such as from molecular gas in star forming
regions, is obtained from spectral line observations. However, these
observations often contain multiple line-of-sight components, making estimates
harder to obtain and interpret. We present a fully automatic method that
determines the number of components along the line of sight, or the spectral
multiplicity, through Bayesian model selection. The underlying open-source
framework, based on nested sampling and conventional spectral line modeling, is
tested using the large area ammonia maps of NGC 1333 in Perseus molecular cloud
obtained by the Green Bank Ammonia Survey (GAS). Compared to classic
approaches, the presented method constrains velocities and velocity dispersions
in a larger area. In addition, we find that the velocity dispersion
distribution among multiple components did not change substantially from that
of a single fit component analysis of the GAS data. These results showcase the
power and relative ease of the fitting and model selection method, which makes
it a unique tool to extract maximum information from complex spectral data.
