Decomposition and Classification of Spectral Lines in Astronomical Radio Data Cubes

The natural output of imaging spectroscopy in astronomy is a 3D data cube with two spatial and one frequency axis. The spectrum of each image pixel consists of an emission line which is Doppler-shifted by gas motions along the line of sight. These data are essential to understand the gas distribution and kinematics of the astronomical object. We propose a two-step method to extract coherent kinematic structures from the data cube. First, the spectra are decomposed into a sum of Gaussians using a Bayesian method to obtain an estimation of spectral lines. Second, we aim at tracking the estimated lines to get an estimation of the structures in the cube. The performance of the approach is evaluated on a real radio-astronomical observation.