Towards Multiple-Star Population Synthesis

TL;DR

This paper presents a Monte Carlo-based numerical recipe to synthesize multiple-star populations that match observed multiplicity data, accounting for observational effects and providing a tool for theoretical and simulation comparisons.

Contribution

It introduces a novel Monte Carlo method for constructing realistic multiple-star populations that align with observed multiplicity distributions and addresses observational biases.

Findings

Achieves observed average multiplicity of 1.53 with a real population average slightly over 2.0.

Provides recipes for correcting selection effects and uncertainties in multiplicity measurements.

Offers a numerical model useful for star formation theory and dynamical cluster simulations.

Abstract

The multiplicities of stars, and some other properties, were collected recently by Eggleton & Tokovinin, for the set of 4559 stars with Hipparcos magnitude brighter than 6.0 (4558 excluding the Sun). In this paper I give a numerical recipe for constructing, by a Monte Carlo technique, a theoretical ensemble of multiple stars that resembles the observed sample. Only multiplicities up to 8 are allowed; the observed set contains only multiplicities up to 7. In addition, recipes are suggested for dealing with the selection effects and observational uncertainties that attend the determination of multiplicity. These recipes imply, for example, that to achieve the observed average multiplicity of 1.53, it would be necessary to suppose that the real population has an average multiplicity slightly over 2.0. This numerical model may be useful for (a) comparison with the results of star and star cluster formation theory, (b) population synthesis that does not ignore multiplicity above 2, and (c) initial conditions for dynamical cluster simulations.