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Mergers of stellar-mass black holes were recently observed in thegravitational wave window opened by LIGO. This puts the spotlight on densestellar systems and their ability to create intermediate-mass black holes(IMBHs) through repeated merging. Unfortunately, attempts at direct andindirect IMBH detection in star clusters in the nearby universe have proveninconclusive as of now. Indirect detection methods attempt to constrain IMBHsthrough their effect on star cluster photometric and kinematic observables.They are usually based on looking for a specific, physically motivatedsignature. While this approach is justified, it may be suboptimal in its usageof the available data. Here I present a new indirect detection method, based onmachine learning, that is unaffected by these restrictions. I reduce thescientific question whether a star cluster hosts an IMBH to a classificationproblem in the machine learning framework. I present preliminary results toillustrate how machine learning models are trained on simulated datasets andmeasure their performance on previously unseen, simulated data.

Detecting intermediate mass black holes in globular clusters with machine learning