Testing Photometric Diagnostics for the Dynamical State and Possible IMBH presence in Globular Clusters

TL;DR

This study uses simulated HST-like images of star clusters with and without IMBHs to evaluate photometric diagnostics, finding the central slope effectively indicates IMBH presence, aiding in identifying suspect clusters.

Contribution

It introduces a method to detect intermediate-mass black holes in globular clusters using surface brightness profiles from realistic simulations.

Findings

Central shallow cusps indicate presence of IMBHs.

Steep cusps rule out IMBHs.

Observed clusters show more variation than models.

Abstract

Surface photometry is a necessary tool to establish the dynamical state of stars clusters. We produce realistic HST-like images from N-body models of star clusters with and without central intermediate-mass black holes (IMBHs) in order to measure their surface brightness profiles. The models contain ~600,000 individual stars, black holes of various masses between 0% to 2% of the total mass, and are evolved for a Hubble time. We measure surface brightness and star count profiles for every constructed image in order to test the effect of intermediate mass black holes on the central logarithmic slope, the core radius, and the half-light radius. We use these quantities to test diagnostic tools for the presence of central black holes using photometry. We find that the the only models that show central shallow cusps with logarithmic slopes between -0.1 and -0.4 are those containing central black holes. Thus, the central logarithmic slope seems to be a good way to choose clusters suspect of containing intermediate-mass black holes. Clusters with steep central cusps can definitely be ruled out to host an IMBH. The measured r_c/r_h ratio has similar values for clusters that have not undergone core-collapse, and those containing a central black hole. We notice that observed Galactic globular clusters have a larger span of values for central slope and r_c/r_h than our modeled clusters, and suggest possible reasons that could account for this and contribute to improve future models.