Speaker
Description
Utilization of machine learning for pattern recognition and track reconstruction in HEP sets promising precedents of how novel data science tools can aid researchers in other fields of physics and astronomy in conducting statistical-inference on large datasets. We present our work in progress on the applications of fast nearest-neighbor search (kNN) to Gaia EDR3—one of the most extensive catalogs of astronomical objects and their properties, including their position and motion in the sky. Mapping positions of stars that are gravitationally bound to Milky Way (MW), but that have not originated in our galaxy could reveal crucial insights about its dark matter halo, which played a fundamental role in its formation. Motions of such stars are modeled differently from MW stars, which allows us to track them across the galaxy. "Tracking" in this context amounts to connecting stars that have a common origin based on their position and motion. The most literal analogy to HEP tracking is given by stellar streams, which are populations of stars that follow a distinct path in the sky, almost like a particle track, but this is not the only possibility. Parallel to the seeding stage of track reconstruction inside colliders, our method identifies potential regions of the galactic halo where stars from different populations may reside, based on their average angular motions over time. This enables us to generically identify any astronomical structure or clustering among stars with similar kinematics that stand out from a group of background objects; thus providing opportunities for our method to not be limited to identification of formally defined star clusters. We will present examples of known star clusters that our method successfully located and discuss the accuracy of their characterization, as well as the robustness of our algorithm given various algorithmic choices.
