AMOEBA: Automated Molecular Excitation Bayesian Line-Fitting Algorithm
  The hyperfine transitions of the ground-rotational state of the hydroxyl
radical (OH) have emerged as a versatile tracer of the diffuse molecular
interstellar medium. We present a novel automated Gaussian decomposition
algorithm designed specifically for the analysis of the paired on-source and
off-source optical depth and emission spectra of these transitions. In contrast
to existing automated Gaussian decomposition algorithms, AMOEBA (Automated
MOlecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian
approach to model selection, fitting all 4 optical depth and 4 emission spectra
simultaneously. AMOEBA assumes that a given spectral feature can be described
by a single centroid velocity and full width at half-maximum, with peak values
in the individual optical depth and emission spectra then described uniquely by
the column density in each of the four levels of the ground-rotational state,
thus naturally including the real physical constraints on these parameters.
Additionally, the Bayesian approach includes informed priors on individual
parameters which the user can modify to suit different data sets. Here we
describe AMOEBA and evaluate its validity and reliability in identifying and
fitting synthetic spectra with known parameters.
