TY - JOUR
T1 - Finding black holes with black boxes - Using machine learning to identify globular clusters with black hole subsystems
AU - Askar, Ammar
AU - Askar, Abbas
AU - Pasquato, Mario
AU - Giersz, Mirek
N1 - Funding Information:
AA(2) is supported by the Carl Tryggers Foundation through the grant CTS 17:113. MP and this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 664931. MG acknowledges support from National Science Center (NCN), Poland, through the grant UMO-2016/23/B/ST9/02732. This work benefitted from support by the International Space Science Institute (ISSI), Bern, Switzerland, through its International Team programme ref. no. 393 The Evolution of Rich Stellar Populations & BH Binaries (2017–18).
Funding Information:
We would like to thank the reviewer for an insightful report that helped improve the quality and readability of the paper. We would like to appreciate the work of Jawad Ahmad, Ikram Ullah Lali, and Muhammad Latif who pointed out a mistake in Table A2 of the preprint while reproducing our results. We are very thankful that they communicated their findings to us. We are extremely grateful to Michela Mapelli for providing useful comments and suggestions that helped to improve the manuscript. Many thanks are warranted for the scikit-learn community for creating such an extensive and useful open source project available for public consumption. AA(2) is supported by the Carl Tryggers Foundation through the grant CTS 17:113. MP and this project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 664931. MG acknowledges support from National Science Center (NCN), Poland, through the grant UMO-2016/23/B/ST9/02732. This work benefitted from support by the International Space Science Institute (ISSI), Bern, Switzerland, through its International Team programme ref. no. 393 The Evolution of Rich Stellar Populations & BH Binaries (2017-18).
Publisher Copyright:
© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
PY - 2019/3/13
Y1 - 2019/3/13
N2 - 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.
AB - 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.
KW - Globular clusters: general
KW - Methods: data analysis
KW - Methods: numerical
KW - Methods: statistical
KW - Stars: black holes
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U2 - 10.1093/mnras/stz628
DO - 10.1093/mnras/stz628
M3 - Article
AN - SCOPUS:85067061915
VL - 485
SP - 5345
EP - 5362
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
SN - 0035-8711
IS - 4
ER -