arXiv subjects

Charles F. A. Gibson

Publications and source records attributed to Charles F. A. Gibson.

3 recordsLinked to original sources

arXiv2026

Using Machine Learning to Model Stellar Collisions in our Galactic Center

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, $\sim$$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 $(2.5-8)\times 10^5$ M$_\odot$ 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.

astro-ph.SR↗

arXiv2026

Measurement of spin-orbit misalignment in binary black holes via the total spin

The degree of spin-orbit alignment in merging binary black holes is a powerful probe of their formation history. However, assessing spin-orbit misalignment in individual events remains challenging, as most parameters commonly used to characterize precession tend to be poorly constrained. In this work, we introduce the angle between the total spin and orbital angular momentum, $θ_{LS}$, as an alternative measure of spin-orbit misalignment. Using synthetic observations, we show that $θ_{LS}$ retains more information to discriminate between the aligned- and isotropic-spins hypotheses than commonly used alternatives, including $χ_{\rm p}$. We then study this parameter on binary black hole mergers observed in the GWTC-5.0 catalog, and identify multiple events that are inconsistent with having aligned spins. In several of the examples, this is not apparent from the posteriors of the effective spin parameters ($χ_{\rm p}$ or $χ_\mathrm{eff}$) nor the individual spin-orbit tilts ($θ_1$ or $θ_2$) alone, and thus had not been previously identified. We highlight GW241127_061008 as a compelling case for a second-generation merger, with a large, tilted primary spin, unequal masses, and a large primary mass near the pair-instability-supernova mass gap. Finally, we perform population inference using $\cosθ_{LS}$ and confirm that the binary black hole population is inconsistent with either a purely isotropic or a purely aligned-spin distribution, and requires substantial spin-orbit misalignment.

astro-ph.HE↗

arXiv2026

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

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.

astro-ph.HE↗