Kalkayotl 2.0 Bayesian phase-space modelling of star-forming regions, stellar associations, and open clusters

Abstract

Context: Star-forming regions, stellar associations, and open clusters are fundamental stellar systems where predictions from star-formation theories can be robustly contrasted with observations. Aims: We aim to provide the astrophysical community with a free and open-source code to infer the phase-space (i.e. positions and velocities) parameters of stellar systems with $\lesssim$1000 stars based on \textit{Gaia} astrometry and possibly observed radial velocities. Methods: We upgrade an existing Bayesian hierarchical model and extend it to model 3D (positions) and 6D (positions and velocities) stellar coordinates and system parameters with a flexible variety of statistical models, including a linear velocity field. This velocity field allows for the inference of internal kinematics, including expansion, contraction, and rotation. Results: We extensively validated our statistical models using realistic simulations that mimic the properties of the \textit{Gaia} Data Release 3. We applied \textit{Kalkayotl} to $\beta$-Pictoris, the Hyades, and Praesepe, recovering parameter values compatible with those from the literature. In particular, we found an expansion age of $19.1\pm1.0$ Myr for $\beta$-Pictoris and rotational signal of $32\!\pm\!11\,\rm{m\,s^{-1}\,pc^{-1}}$ for the Hyades and that Praesepe's rotation reported in the literature comes from its periphery. Conclusions: The robust and flexible Bayesian hierarchical model that we make publicly available here represents a step forward in the statistical modelling of stellar systems. The products it delivers, such as expansion, contraction, rotation, and velocity dispersions, can be directly contrasted with predictions from star-formation theories.