Non-parametric identification of single-lined binary candidates in young clusters using single-epoch spectroscopy

Abstract

Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepen our understanding of these processes. We propose a method to investigate the observed radial velocity distribution of massive stars in young clusters with the goal of identifying binary systems. We reconstruct the radial velocity distribution using a three-layers hierarchical Bayesian non-parametric approach: non-parametric methods are data-driven models able to infer arbitrary probability densities under minimal mathematical assumptions. When applying our statistical framework, it is possible to identify variable stars and binary systems because these deviate significantly from the expected intrinsic Gaussian distribution for radial velocities. We test our method with the massive star forming region within the giant H$_\mathrm{II}$ region M17. We are able to confidently identify binaries and variable stars with as little as single-epoch observations. The distinction between variable and binary stars improves significantly when introducing additional epochs.