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

TL;DR

This paper introduces a machine learning approach trained on a large grid of SPH simulations to accurately predict stellar collision outcomes and remnant masses, significantly aiding dense star cluster modeling.

Contribution

It presents a comprehensive dataset and trained ML models for stellar collision predictions, improving speed and accuracy over traditional analytic formulas.

Findings

Achieved 98.4% classification accuracy in collision outcome prediction.

Regression errors as low as 0.11% for remnant mass predictions.

Provided publicly available models for use in N-body simulations.

Abstract

Stellar collisions can occur frequently in dense cluster environments, and play a crucial role in producing exotic phenomena from blue stragglers in globular clusters to high-energy transients in galactic nuclei. Successive collisions and mergers of massive stars could also lead to the formation of massive black holes, serving as seeds for supermassive black hole in the early universe. While analytic fitting formulae exist for predicting collision outcomes, they do not generalize across different energy scales or stellar evolutionary phases. Smoothed particle hydrodynamics (SPH) simulations are often used to compute the outcomes of stellar collisions, but, even at low resolution, their computational cost makes running on-the-fly calculations during an $N$-body simulation quite challenging. 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.