XookSuut a code for modeling circular and non-circular flows on 2D velocity maps

TL;DR

XookSuut is a Python tool that uses Bayesian sampling methods to model galaxy velocity maps, providing detailed parameter posteriors for various kinematic models, improving over traditional minimization techniques.

Contribution

It introduces a Bayesian inference framework with MCMC and Nested Sampling for galaxy kinematic modeling, offering a robust alternative to standard methods.

Findings

Enables detailed posterior distributions of galaxy rotation parameters

Supports multiple kinematic models including noncircular flows

Provides improved error analysis and parameter covariance insights

Abstract

We present $\texttt{XookSuut}$, a Python implementation of the $\texttt{DiskFit}$ algorithm, optimized to perform robust Bayesian inference on parameters describing models of circular and noncircular rotation in galaxies. $\texttt{XookSuut}$~surges as a Bayesian alternative for kinematic modeling of 2D velocity maps; it implements efficient sampling methods, specifically Markov Chain Monte Carlo (MCMC) and Nested Sampling (NS), to obtain the posteriors and marginalized distributions of kinematic models including circular motions, axisymmetric radial flows, bisymmetric flows, and harmonic decomposition of the LoS~velocity. In this way, kinematic models are obtained by pure sampling methods, rather than standard minimization techniques based on the $\chi^2$. All together, $\texttt{XookSuut}$~represents a sophisticated tool for deriving rotational curves and to explore the error distribution and covariance between parameters.