Predicting intermediate-mass black hole formation in star clusters with machine learning
Konstantinos Kritos, Digvijay Wadekar, Emanuele Berti

TL;DR
This study uses machine learning models trained on synthetic data to predict the presence and mass of intermediate-mass black holes in star clusters, providing insights into their formation and observational signatures.
Contribution
The paper introduces neural network and random forest models trained on synthetic cluster data to forecast black hole masses and formation likelihoods in star clusters.
Findings
Globular clusters are unlikely to host black holes over 100 M_sun.
Some nuclear star clusters may have black holes exceeding 100 M_sun.
Predicted black hole masses are contrasted with observational estimates, suggesting additional formation processes.
Abstract
Whether intermediate-mass black holes reside in nearby star clusters has remained contested for decades. We address this question by training neural network and random forest regressors on synthetic catalogs generated with the {\sc Rapster} cluster evolution code, mapping observable cluster properties such as total mass and half-mass radius onto the mass of the heaviest black hole built up through repeated mergers. Applying these models to nearby globular and nuclear star clusters, we forecast the intermediate-mass black hole population that each system may host. Globular clusters are unlikely to contain black holes more massive than , with an occupation fraction near 0.02, although they can produce remnants within the upper mass gap with masses approaching . Among nuclear star clusters, a handful of cases, including NGC 5102 and NGC 5206, yield…
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