Modeling Stellar Collisions in Galactic Nuclei Using Hydrodynamic Simulations and Machine Learning
Persistent URL
Author(s)
Rose, Sanaea C.
Lombardi, James C.
Allegheny College
González Prieto, Elena
Kıroğlu, Fulya
Rasio, Frederic A.
Date Issued
March 20, 2026
Abstract
Nuclear star clusters represent some of the most extreme collisional environments in the Universe. A nuclear star cluster like that of the Milky Way harbors a supermassive black hole at its center, which accelerates stars to high speeds (greater than or similar to 100-1000 km s-1) in a region where millions of other stars reside. Direct collisions occur in such high-density environments, where they can shape the stellar populations and influence the evolution of the cluster. We present a suite of a couple hundred high-resolution smoothed-particle hydrodynamics (SPH) simulations of collisions between 1 M circle dot stars, at impact speeds representative of galactic nuclei. We use our SPH dataset to develop physically motivated fitting formulae for predicting collision outcomes. While collision-driven mass loss has been examined in detail in the literature, we present a new framework for understanding the effects of "hit-and-run" collisions on a star's trajectory. We demonstrate that the change in stellar velocity follows the tidal-dissipation limit for grazing encounters, while the deflection angle is well-approximated by point-particle dynamics for periapses greater than or similar to 0.3 times the stellar radii. We use our SPH dataset to test two machine learning (ML) algorithms, k-nearest neighbors and neural networks, for predicting collision outcomes. We find that the neural network outperforms k-nearest neighbors and delivers results on par with and in some cases exceeding the accuracy of our fitting formulae. We conclude that both fitting formulae and ML have merits for modeling collisions in dense stellar environments; however, ML may prove more effective as the parameter space of initial conditions expands.
Journal
The Astrophysical Journal
Citation
Sanaea C. Rose et al 2026 ApJ 1000 162
Publisher
American Astronomical Society
Version of Article
Version of Record
DOI
10.3847/1538-4357/ae459b
ISSN
0004-637X
1538-4357
Rights
© 2026. The Author(s). Published by the American Astronomical Society.
Type of Publication
Journal Article
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