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

Askar, Ammar ; Askar, Abbas LU ; Pasquato, Mario and Giersz, Mirek (2019) In Monthly Notices of the Royal Astronomical Society 485(4). p.5345-5362
Abstract
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... (More)
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. (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
in
Monthly Notices of the Royal Astronomical Society
volume
485
issue
4
pages
5345 - 5362
publisher
Oxford University Press
external identifiers
  • scopus:85067061915
ISSN
0035-8711
DOI
10.1093/mnras/stz628
language
English
LU publication?
yes
id
a4d0d739-e74a-42c7-8cfa-601090c54fae
date added to LUP
2019-04-04 13:58:54
date last changed
2020-02-12 09:58:29
@article{a4d0d739-e74a-42c7-8cfa-601090c54fae,
  abstract     = {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.},
  author       = {Askar, Ammar and Askar, Abbas and Pasquato, Mario and Giersz, Mirek},
  issn         = {0035-8711},
  language     = {eng},
  month        = {06},
  number       = {4},
  pages        = {5345--5362},
  publisher    = {Oxford University Press},
  series       = {Monthly Notices of the Royal Astronomical Society},
  title        = {Finding black holes with black boxes – using machine learning to identify globular clusters with black hole subsystems},
  url          = {http://dx.doi.org/10.1093/mnras/stz628},
  doi          = {10.1093/mnras/stz628},
  volume       = {485},
  year         = {2019},
}