Finding black holes with black boxes - Using machine learning to identify globular clusters with black hole subsystems

Ammar Askar, Abbas Askar, Mario Pasquato, Mirek Giersz

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish (US)
    Pages (from-to)5345-5362
    Number of pages18
    JournalMonthly Notices of the Royal Astronomical Society
    Volume485
    Issue number4
    DOIs
    StatePublished - Mar 13 2019

    Keywords

    • Globular clusters: general
    • Methods: data analysis
    • Methods: numerical
    • Methods: statistical
    • Stars: black holes

    ASJC Scopus subject areas

    • Astronomy and Astrophysics
    • Space and Planetary Science

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