Vladimir, Ze'evDark matter constitutes 80% of all matter and is key to understanding the structure of the universe. Dark matter impacts galaxy formation through the gravitational influence it exerts on baryonic matter. However, dark matter and the structures it forms are too complex to describe with an analytical solution. As a result numerical simulations have provided a way to learn about dark matter structures. Within these simulations, dark matter forms filaments, voids, and most notably halos. Halos are roughly spherical regions with a high concentration of dark matter that contain a galaxy which co-evolves alongside it. However, determining which particles form a halo is most commonly oversimplified with the spherical over-density model. This model defines a halo as all the particles present in a sphere around an over-dense region in space. However, a more physically accurate definition for halos has recently been proposed: only the particles that orbit a halo's center of mass constitute the halo. Unfortunately, current methods that determine if a particle is orbiting either lack a high level of accuracy or are computationally expensive. To fill this gap, we have developed a simple machine-learning model using the XGBoost library. Our model is trained on the velocity and position of the dark matter particles which are accessible from all simulations. To improve accuracy, we utilized velocity and position data from two snapshots in simulation time to capture how a particle moves. Our model achieves over 97\% accuracy at all radii on our validation set. In addition, our model's speed outperforms more complex orbiting classifiers on the order of seconds to minutes compared to hours.en-USAstronomyDark MatterMachine LearningOther