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arxiv: 1602.01643 · v1 · pith:RAQPEQOFnew · submitted 2016-02-04 · 🌌 astro-ph.SR · astro-ph.GA

MUSE crowded field 3D spectroscopy of over 12,000 stars in the globular cluster NGC 6397 - II. Probing the internal dynamics and the presence of a central black hole

classification 🌌 astro-ph.SR astro-ph.GA
keywords clustercomponentanalysisblackcentraldatadispersionglobular
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We present a detailed analysis of the kinematics of the Galactic globular cluster NGC 6397 based on more than ~18,000 spectra obtained with the novel integral field spectrograph MUSE. While NGC 6397 is often considered a core collapse cluster, our analysis suggests a flattening of the surface brightness profile at the smallest radii. Although it is among the nearest globular clusters, the low velocity dispersion of NGC 6397 of <5km/s imposes heavy demands on the quality of the kinematical data. We show that despite its limited spectral resolution, MUSE reaches an accuracy of 1km/s in the analysis of stellar spectra. We find slight evidence for a rotational component in the cluster and the velocity dispersion profile that we obtain shows a mild central cusp. To investigate the nature of this feature, we calculate spherical Jeans models and compare these models to our kinematical data. This comparison shows that if a constant mass-to-light ratio is assumed, the addition of an intermediate-mass black hole with a mass of 600M_sun brings the model predictions into agreement with our data, and therefore could be at the origin of the velocity dispersion profile. We further investigate cases with varying mass-to-light ratios and find that a compact dark stellar component can also explain our observations. However, such a component would closely resemble the black hole from the constant mass-to-light ratio models as this component must be confined to the central ~5arcsec of the cluster and must have a similar mass. Independent constraints on the distribution of stellar remnants in the cluster or kinematic measurements at the highest possible spatial resolution should be able to distinguish the two alternatives.

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    Machine learning regressors trained on Rapster simulations forecast that globular clusters rarely host black holes above 100 solar masses while a few nuclear star clusters may exceed this threshold.