  We present a novel algorithm based on a Bayesian method for 2D tilted-ring
analysis of disk galaxy velocity fields. Compared to the conventional
algorithms based on a chi-squared minimisation procedure, this new
Bayesian-based algorithm suffers less from local minima of the model parameters
even with highly multi-modal posterior distributions. Moreover, the Bayesian
analysis, implemented via Markov Chain Monte Carlo (MCMC) sampling, only
requires broad ranges of posterior distributions of the parameters, which makes
the fitting procedure fully automated. This feature will be essential when
performing kinematic analysis on the large number of resolved galaxies expected
to be detected in neutral hydrogen (HI) surveys with the Square Kilometre Array
(SKA) and its pathfinders. The so-called '2D Bayesian Automated Tilted-ring
fitter' (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to
derive rotation curves of galaxies. We explore 2DBAT performance on (a)
artificial HI data cubes built based on representative rotation curves of
intermediate-mass and massive spiral galaxies, and (b) Australia Telescope
Compact Array (ATCA) HI data from the Local Volume HI Survey (LVHIS). We find
that 2DBAT works best for well-resolved galaxies with intermediate inclinations
(20 deg < i < 70 deg), complementing three-dimensional techniques better suited
to modelling inclined galaxies.
