Kavli Affiliate: Pau Amaro Seoane
| First 5 Authors: Pau Amaro Seoane, Pau Amaro Seoane, , ,
| Summary:
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.
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