Detecting intermediate mass black holes in globular clusters with
  machine learning
  Mergers of stellar-mass black holes were recently observed in the
gravitational wave window opened by LIGO. This puts the spotlight on dense
stellar systems and their ability to create intermediate-mass black holes
(IMBHs) through repeated merging. Unfortunately, attempts at direct and
indirect IMBH detection in star clusters in the nearby universe have proven
inconclusive as of now. Indirect detection methods attempt to constrain IMBHs
through their effect on star cluster photometric and kinematic observables.
They are usually based on looking for a specific, physically motivated
signature. While this approach is justified, it may be suboptimal in its usage
of the available data. Here I present a new indirect detection method, based on
machine learning, that is unaffected by these restrictions. I reduce the
scientific question whether a star cluster hosts an IMBH to a classification
problem in the machine learning framework. I present preliminary results to
illustrate how machine learning models are trained on simulated datasets and
measure their performance on previously unseen, simulated data.
