Using Machine Learning to Model Stellar Collisions in our Galactic Center
Abstract
Direct collisions in the inner pc of the Galactic center can alter the orbits and properties of stars. The outcome of a collision depends on a number of parameters, including the masses and ages of the stars, the impact parameter, and the initial relative velocity. We utilize the newly developed verb|collAIder|, a machine learning tool developed to predict the outcomes of stellar collisions, to bridge 3D hydrodynamic simulations of stellar collisions with a dynamical model of the Galactic center. Our results show a substantial population of stripped stars, ∼10% of the initial stellar population, produced by mass loss during high-speed collisions. Stellar mergers are less frequent, with about 5% of the stars experiencing a collision-induced merger. We also find that high-speed, nearly head-on collisions can completely disrupt the stars. These destructive collisions are usually preceded by 10 or more collisions, which gradually reduce the mass of the star before it is ultimately destroyed. We estimate that mass loss during stellar collisions injects roughly (2.5−8)× 105 M⊙ of gas into the surrounding environment. Most of this gas is injected into the inner $0.1$ pc and about 90% is retained within the cluster and may be accreted onto the supermassive black hole.
Related
- Binary Dynamics and Mergers in a Stellar Disk and the Formation of Dusty Objects in the Galactic Center
- Binary Dynamics and Mergers in a Stellar Disk and the Formation of Dusty Objects in the Galactic Center
- Improving constraints on the Yukawa correction at the Galactic Center with multiple stellar orbits
- Machine learning-based classification of type 1 active galactic nuclei
- Evolutionary Map of the Universe (EMU): Selection of active galactic nuclei and star-formation emission with machine-learning techniques
BibTeX
@misc{sand2026using,
title = {{Using Machine Learning to Model Stellar Collisions in our Galactic Center}},
author = {Tristan C. Sand and Sanaea C. Rose and Elena González Prieto and James C. Lombardi, Jr. and Charles F. A. Gibson and Fulya Kıroğlu and Kyle Kremer and Christopher E. O'Connor and Enrico Ramirez-Ruiz and Frederic A. Rasio and Tjitske Starkenburg},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25318},
eprint = {2609.25318},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25318},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Using Machine Learning to Model Stellar Collisions in our Galactic Center
AU - Tristan C. Sand
AU - Sanaea C. Rose
AU - Elena González Prieto
AU - James C. Lombardi, Jr.
AU - Charles F. A. Gibson
AU - Fulya Kıroğlu
AU - Kyle Kremer
AU - Christopher E. O'Connor
AU - Enrico Ramirez-Ruiz
AU - Frederic A. Rasio
AU - Tjitske Starkenburg
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.25318
UR - https://arxiv.org/abs/2609.25318
AB - Direct collisions in the inner pc of the Galactic center can alter the orbits and properties of stars. The outcome of a collision depends on a number of parameters, including the masses and ages of the stars, the impact parameter, and the initial relative velocity. We utilize the newly developed verb|collAIder|, a machine learning tool developed to predict the outcomes of stellar collisions, to bridge 3D hydrodynamic simulations of stellar collisions with a dynamical model of the Galactic center. Our results show a substantial population of stripped stars, ∼10% of the initial stellar population, produced by mass loss during high-speed collisions. Stellar mergers are less frequent, with about 5% of the stars experiencing a collision-induced merger. We also find that high-speed, nearly head-on collisions can completely disrupt the stars. These destructive collisions are usually preceded by 10 or more collisions, which gradually reduce the mass of the star before it is ultimately destroyed. We estimate that mass loss during stellar collisions injects roughly (2.5−8)× 10^5 M_⊙ of gas into the surrounding environment. Most of this gas is injected into the inner $0.1$ pc and about 90% is retained within the cluster and may be accreted onto the supermassive black hole.
ER -