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.

Cone search capability for table J/MNRAS/485/5345/tablea1 (Predictions for the presence of BHSs with data from the Harris (1996AJ....112.1487H, Cat. VII/202) catalogue)

Cone search capability for table J/MNRAS/485/5345/tablea2 (Predictions for the presence of BHSs with data from Baumgardt & Hilker (2018MNRAS.478.1520B, Cat. J/MNRAS/478/1520) catalogue for Milky Way GC parameters)