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Open Astronomy

formerly Baltic Astronomy

Editor-in-Chief: Barbuy, Beatriz


IMPACT FACTOR 2018: 0.350

CiteScore 2018: 0.24

SCImago Journal Rank (SJR) 2018: 0.202
Source Normalized Impact per Paper (SNIP) 2018: 0.144

ICV 2017: 121.03

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2543-6376
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Volume 23, Issue 3-4

Issues

Stochasticity in Star Clusters: Reduced Random Sampling Method

P. de Meulenaer
  • Center for Physical Sciences and Technology, Savanorių 231, Vilnius LT-02300, Lithuania
  • Vilnius University Observatory, Čiurlionio 29, Vilnius LT-03100, Lithuania
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ D. Narbutis / T. Mineikis
  • Center for Physical Sciences and Technology, Savanorių 231, Vilnius LT-02300, Lithuania
  • Vilnius University Observatory, Čiurlionio 29, Vilnius LT-03100, Lithuania
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ V. Vansevičius
  • Center for Physical Sciences and Technology, Savanorių 231, Vilnius LT-02300, Lithuania
  • Vilnius University Observatory, Čiurlionio 29, Vilnius LT-03100, Lithuania
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-02-18 | DOI: https://doi.org/10.1515/astro-2017-0182

Abstract

This paper aims to contribute to the debate taking place nowadays on the two extreme schemes of sampling the stellar masses within star clusters, known as Optimal Sampling and Random Sampling. We propose a new method for sampling of stellar masses in star clusters which allows a continuous transition between the Optimal Sampling and the Random Sampling. We use a sample of young star clusters from the literature to calibrate the amount of stochasticity generated by the proposed method.

Keywords: galaxies: star clusters: general

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About the article

Received: 2014-08-18

Accepted: 2014-10-10

Published Online: 2017-02-18

Published in Print: 2014-12-01


Citation Information: Open Astronomy, Volume 23, Issue 3-4, Pages 199–207, ISSN (Online) 2543-6376, DOI: https://doi.org/10.1515/astro-2017-0182.

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© 2014 P. de Meulenaer et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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