Speaker
Description
Tracking algorithms typically assume helical trajectories to simplify the task of reconstruction. However, numerous theories predict interactions which lead to non-helical tracks. Graph neural networks can split the task of finding and fitting tracks, allowing them to find non-helical tracks from physics beyond the Standard Model, such as quirks. Yet, particles could exhibit behavior beyond what theory has predicted. With only model dependent search strategies, we can only find physics that has already been anticipated by theory. A model-agnostic reconstruction technique would afford us the opportunity to make single event discoveries, free of background, and would not require predictions from theory. We present a method of training the GNN4ITK pipeline to reconstruct a broad general set of non-helical tracks. Our work shows that the pipeline has a high aptitude to operate as a generalized track finder and presents itself as a promising approach for making background free single event discoveries.