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

TL;DR

This paper introduces a non-parametric Bayesian method to identify binary stars in young clusters using single-epoch spectroscopy, enabling detection of binaries with minimal observations and improving with more data.

Contribution

The study presents a novel hierarchical Bayesian non-parametric approach for identifying binary stars from radial velocity data, effective even with single-epoch observations.

Findings

Successfully identified binaries in M17 region

Effective detection with single-epoch data

Enhanced accuracy with multiple epochs

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.