The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Picture of Abbas Askar

Abbas Askar

Postdoc

Picture of Abbas Askar

Finding black holes with black boxes – using machine learning to identify globular clusters with black hole subsystems

Author

  • Ammar Askar
  • Abbas Askar
  • Mario Pasquato
  • Mirek Giersz

Summary, in English

Machine learning is a powerful technique, becoming increasingly popular in astrophysics. In this paper, we apply machine learning to more than a thousand globular cluster (GC) models simulated with the mocca-Survey Database I project in order to correlate present-day observable properties with the presence of a subsystem of stellar mass black holes (BHs). The machine learning model is then applied to available observed parameters for Galactic GCs to identify which of them that are most likely to be hosting a sizeable number of BHs and reveal insights into what properties lead to the formation of BH subsystems. With our machine learning model, we were able to shortlist 18 Galactic GCs that are most likely to contain a BH subsystem. We show that the clusters shortlisted by the machine learning classifier include those in which BH candidates have been observed (M22, M10, and NGC 3201) and that our results line up well with independent simulations and previous studies that manually compared simulated GC models with observed properties of Galactic GCs. These results can be useful for observers searching for elusive stellar mass BH candidates in GCs and further our understanding of the role BHs play in GC evolution. In addition, we have released an online tool that allows one to get predictions from our model after they input observable properties.

Department/s

  • Lund Observatory

Publishing year

2019-06-01

Language

English

Pages

5345-5362

Publication/Series

Monthly Notices of the Royal Astronomical Society

Volume

485

Issue

4

Document type

Journal article

Publisher

Oxford University Press

Topic

  • Astronomy, Astrophysics and Cosmology

Status

Published

ISBN/ISSN/Other

  • ISSN: 0035-8711