Machine learning methods to predict the formation of binary compact objects

In this thesis I tested different machine learning methods (Random forest, XGBoost) to predict the formation of binary compact objects solely based on initial condition of stellar binaries (masses, metallicity and orbital parameters) and on the parameters of binary evolution (i.e., common envelope efficiency). I trained the machine learning methods on simulations performed with rapid binary population synthesis code (BPS) code SEVN. I found that the tested methods can predict the formation of merging (within the Hubble time) and non-merging binary compact objects. The performance of predicting merging compact objects is found to be 50%, which is equivalent to random chance. This suggests that the methods cannot accurately predict the formation of merging compact objects. However, the methods show good performance in distinguishing between systems that produce bound and non-bound systems.