Predicting stellar collision outcomes of main sequence stars

Abstract

Stellar collisions in dense galactic nuclei might play an important role in fueling supermassive black holes (SMBHs) and shaping their environments. The gas released during these collisions can contribute to SMBH accretion, influencing phenomena such as active galactic nuclei and tidal disruption events of the remnants. We address the challenge of rapidly and accurately predicting the outcomes of stellar collisionsincluding remnant masses and unbound gasacross a broad parameter space of initial conditions. Existing smoothed-particle-hydrodynamic (SPH) simulation techniques, while detailed, are too resource-intensive for exploratory studies or real-time applications. We develop a machine learning framework trained on a dataset of $\sim 16,000$ SPH simulations of main-sequence star collisions. By extracting physically meaningful parameters (e.g., masses, radii, impact parameters, and virial ratios) and employing gradient-boosted regression trees with Huber loss, we create a model that balances accuracy and computational efficiency. The method includes logarithmic transforms to handle dynamic ranges and regularization to ensure physical plausibility. The model achieves predictions of collision outcomes (remnant masses, and unbound mass) with very low mean absolute errors respect to the typical mass scale. It operates in fractions of a second, enabling large-scale parameter studies and real-time applications. Parameter importance analysis reveals that the impact parameter and the relative velocity dominate outcomes, aligning with theoretical expectations. Our approach provides a scalable tool for studying stellar collisions in galactic nuclei. The rapid predictions facilitate investigations into gas supply for SMBH accretion and the cumulative effects of collisions over cosmic time, particularly relevant to address the growth of SMBHs.