Finding Black Holes with Black Boxes -- Using Machine Learning to Identify Globular Clusters with Black Hole Subsystems

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 as part of 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 21 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.Comment: 20 pages, 9 figures, 7 tables. Accepted for publication in MNRAS. Source code available at https://github.com/ammaraskar/black-holes-black-boxe

COCOA Code for Creating Mock Observations of Star Cluster Models

We introduce and present results from the COCOA (Cluster simulatiOn Comparison with ObservAtions) code that has been developed to create idealized mock photometric observations using results from numerical simulations of star cluster evolution. COCOA is able to present the output of realistic numerical simulations of star clusters carried out using Monte Carlo or \textit{N}-body codes in a way that is useful for direct comparison with photometric observations. In this paper, we describe the COCOA code and demonstrate its different applications by utilizing globular cluster (GC) models simulated with the MOCCA (MOnte Carlo Cluster simulAtor) code. COCOA is used to synthetically observe these different GC models with optical telescopes, perform PSF photometry and subsequently produce observed colour magnitude diagrams. We also use COCOA to compare the results from synthetic observations of a cluster model that has the same age and metallicity as the Galactic GC NGC 2808 with observations of the same cluster carried out with a 2.2 meter optical telescope. We find that COCOA can effectively simulate realistic observations and recover photometric data. COCOA has numerous scientific applications that maybe be helpful for both theoreticians and observers that work on star clusters. Plans for further improving and developing the code are also discussed in this paper.Comment: 18 pages, 12 figures, accepted for publication in MNRAS. Revised manuscript has a new title, better quality figures and many other improvements. COCOA can be downloaded from: https://github.com/abs2k12/COCOA (comments are welcome

Cataclysmic variables in Globular clusters: First results on the analysis of the MOCCA simulations database

In this first investigation of the MOCCA database with respect to cataclysmic variables, we found that for models with Kroupa initial distributions, considering the standard value of the efficiency of the common-envelope phase adopted in BSE, no single cataclysmic variable was formed only via binary stellar evolution, i. e., in order to form them, strong dynamical interactions have to take place. Our results also indicate that the population of cataclysmic variables in globular clusters are, mainly, in the last stage of their evolution and observational selection effects can change drastically the expected number and properties of observed cataclysmic variables.Comment: 4 pages, 3 figures. Presented at the MODEST 16/Cosmic Lab conference in Bologna, Italy, April 18-22 2016. To be pusblished in Mem. S. A. It. Conference Serie

MOCCA Survey Database I: Dissolution of tidally filling star clusters harbouring BH subsystems

We investigate the dissolution process for dynamically evolving star clusters embedded in an external tidal field by exploring the MOCCA Survey Database I, with focus on the presence and evolution of a stellar-mass black hole subsystem. We argue that the presence of a black hole subsystem can lead to the dissolution of tidally filling star clusters and this can be regarded as a third type of cluster dissolution mechanism (in addition to well-known mechanisms connected with strong mass loss due to stellar evolution and mass loss connected with the relaxation process). This third process is characterized by abrupt cluster dissolution connected with the loss of dynamical equilibrium. The abrupt dissolution is powered by strong energy generation from a stellar-mass black hole subsystem accompanied by tidal stripping. Additionally, we argue that such a mechanism should also work for even tidally under-filling clusters with top-heavy initial mass function. Observationally, star clusters which undergo dissolution powered by the third mechanism would look as a 'dark cluster' i.e. composed of stellar mass black holes surrounded by an expanding halo of luminous stars (Banerjee & Kroupa 2011), and they should be different from 'dark clusters' harbouring intermediate mass black holes as discussed by Askar et al. (2017a). An additional observational consequence of an operation of the third dissolution mechanism should be a larger than expected abundance of free floating black holes in the Galactic halo.Comment: 14 pages, 14 figures, accepted to MNRA

MOCCA-SURVEY database I. Accreting white dwarf binary systems in globular clusters -- IV. cataclysmic variables -- properties of bright and faint populations

We investigate here populations of cataclysmic variables (CVs) in a set of 288 globular cluster (GC) models evolved with the MOCCA code. This is by far the largest sample of GC models ever analysed with respect to CVs. Contrary to what has been argued for a long time, we found that dynamical destruction of primordial CV progenitors is much stronger in GCs than dynamical formation of CVs, and that dynamically formed CVs and CVs formed under no/weak influence of dynamics have similar white dwarf mass distributions. In addition, we found that, on average, the detectable CV population is predominantly composed of CVs formed via typical common envelope phase (CEP) ($\gtrsim70$ per cent), that only $\approx2-4$ per cent of all CVs in a GC is likely to be detectable, and that core-collapsed models tend to have higher fractions of bright CVs than non-core-collapsed ones. We also consistently show, for the first time, that the properties of bright and faint CVs can be understood by means of the pre-CV and CV formation rates, their properties at their formation times and cluster half-mass relaxation times. Finally, we show that models following the initial binary population proposed by Kroupa and set with low CEP efficiency better reproduce the observed amount of CVs and CV candidates in NGC 6397, NGC 6752 and 47 Tuc. To progress with comparisons, the essential next step is to properly characterize the candidates as CVs (e.g. by obtaining orbital periods and mass ratios).Comment: 18 pages, 13 figures; accepted for publication in MNRA