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 OH 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 four optical-depth and four 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 that the user can modify to suit different data sets. Here we describe Amoeba and establish its validity and reliability in identifying and fitting synthetic spectra with known (but hidden) parameters, finding that the code performs very well in a series of practical tests. Amoeba's core algorithm could be adapted to the analysis of other species with multiple transitions interconnecting shared levels (e.g., the 700 MHz lines of the first excited rotational state of CH). Users are encouraged to adapt and modify Amoeba to suit their own use cases.