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

\nAims. Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepening 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.\nMethods. We reconstruct the radial velocity distribution using a three-layer hierarchical Bayesian non-parametric approach; nonparametric 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.\nResults. We tested our method with the massive star-forming region within the giant HII 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.\n

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-12-13
DOI
https://doi.org/10.1051/0004-6361/202451694/pdf
Primary Topic
Astrophysics and Star Formation Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

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

Springer Link (Chiba Institute of Technology)
Astrophysics and Star Formation Studies
article

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

article en

Abstract

\nAims. Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepening 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.\nMethods. We reconstruct the radial velocity distribution using a three-layer hierarchical Bayesian non-parametric approach; nonparametric 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.\nResults. We tested our method with the massive star-forming region within the giant HII 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.\n

Springer Link (Chiba Institute of Technology)
Deutsche Forschungsgemeinschaft, Deutsches Zentrum für Luft- und Raumfahrt, Bundesministerium für Wirtschaft und Energie
Openalex Percentile: Top 78%
Astrophysics and Star Formation Studies
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