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

2D Bayesian automated tilted-ring fitting of disk galaxies in large HI galaxy surveys: 2DBAT