Geometric Graph Neural Network based track finding

An essential component of event reconstruction in particle physics experiments is identifying the trajectory of charged particles in the detector. Traditional methods for track finding are often complex, and tailored to specific detectors and input geometries, limiting their adaptability to new detector designs and optimization processes. To overcome these limitations, we present a novel, end-to-end track finding algorithm that is detector-agnostic and can take into account multiple input types. To achieve this, our approach unifies inputs from multiple sub-detectors and detector types into a single geometric algebra representation, simplifying data handling compared to traditional methods. Then, we leverage an equivariant graph neural network, GATr, to perform track finding across all data from an event simultaneously. We validate the effectiveness of our pipeline on various detector concepts with different technologies for the FCC-ee at CERN, the IDEA, and CLD detectors. This work generalizes track finding across diverse types of input geometric data and tracking technologies, facilitating the development of innovative detector concepts and enabling comprehensive detector optimization.