arXiv subjects

James C. Lombardi, Jr.

Publications and source records attributed to James C. Lombardi, Jr..

6 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

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↗

arXiv2026

Formation of Black Hole-White Dwarf X-ray Binaries in Globular Clusters

Globular clusters are host to significant populations of dynamically-active stellar remnants that connect to a variety of astrophysical sources. Using simulations performed with the Cluster Monte Carlo dynamics code, we study the formation of ultracompact binaries in which a stellar-mass black hole accretes material from a white dwarf companion in a sub-hour orbit. These binary systems are prime multimessenger targets, as they can be observed as both luminous X-ray sources, and as millihertz gravitational wave sources detectable by the Laser Interferometer Space Antenna (LISA). We find that black hole+giant collisions are the primary mechanism through which such systems form. We model the outcomes of these ``common envelope''-like events using the smoothed particle hydrodynamics code StarSmasher, and verify these collisions yield black hole+white dwarf binaries that enter Roche contact on sub-Gyr timescales via gravitational wave inspiral. We construct a mock catalog of local ultracompact X-ray sources and compare to candidate sources observed in globular clusters in the Milky Way (e.g., 47 Tuc X9) and external galaxies (e.g., RZ 2109 in NGC 4472). Finally, we compute the gravitational wave strain for these sources, and show that of order one source may be resolvable in the Milky Way by LISA, representing a potentially powerful tool for observing new black holes in globular clusters.

astro-ph.HE↗

arXiv2026

Modeling Stellar Collisions in Galactic Nuclei Using Hydrodynamic Simulations and Machine Learning

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 ($\gtrsim 100$-$1000$ km/s) 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$_\odot$ 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 $\gtrsim 0.3$ 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 out-performs 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.

astro-ph.GA↗

arXiv2000

Evolution of Stellar Collision Products in Globular Clusters - II. Off-axis Collision

We continue our exploration of collisionally merged stars in the blue straggler region of the color-magnitude diagram. We report the results of new SPH calculations of parabolic collisions between two main-sequence stars, with the initial structure and composition profiles of the parent stars having been determined from stellar evolution calculations. Parallelization of the SPH code has permitted much higher numerical resolution of the hydrodynamics. We also present evolutionary tracks for the resulting collision products, which emerge as rapidly rotating blue stragglers. The rotating collision products are brighter, bluer and remain on the main sequence longer than their non-rotating counterparts. In addition, they retain their rapid rotation rates throughout their main sequence lifetime. Rotationally-induced mixing strongly affects the evolution of the collision products, although it is not sufficient to mix the entire star. We discuss the implications of these results for studies of blue straggler populations in clusters. This work shows that off-axis collision products cannot become blue stragglers unless they lose a large fraction of their initial angular momentum. The mechanism for this loss is not apparent, although some possibilities are discussed.

astro-ph↗

arXiv1995

ON BLUE STRAGGLER FORMATION BY DIRECT COLLISIONS OF MAIN-SEQUENCE STARS

We report the results of new SPH calculations of parabolic collisions between main-sequence (MS) stars. The stars are assumed to be close to the MS turn-off point in a globular cluster and are therefore modeled as $n=3$, $Γ=5/3$ polytropes. We find that the high degree of central mass concentration in these stars has a profound effect on the hydrodynamics. In particular, very little hydrodynamic mixing occurs between the dense, helium-rich inner cores and the outer envelopes. As a result, and in contrast to what has been assumed in previous studies, blue stragglers formed by direct stellar collisions are not necessarily expected to have anomalously high helium abundances in their envelopes or to have their cores replenished with fresh hydrogen fuel.

astro-ph↗