Publications

Machine Learning Methods for Stellar Collisions. I. Predicting Outcomes of SPH Simulations

González Prieto et al. | ApJ | 2026

Here we present a new grid of 27,720 SPH calculations of main-sequence star collisions, spanning a wide range of masses, ages, relative velocities, and impact parameters. Using this grid, we train machine learning models to predict both collision outcomes (merger vs disruption, or flyby) and final remnant masses. We compare the performance of nearest neighbors, support vector machines, and neural networks, achieving classification balanced accuracy of 98.4%, and regression relative errors as low as 0.11% and 0.15% for the final stars 1 and 2, respectively. We make our trained models publicly available as part of the package collAIder, enabling rapid predictions of stellar collision outcomes in N-body models of dense star cluster dynamics.