Development of tracking algorithm with deep learning techniques
A range of models inspired by computer vision applications were investigated, which operated on data from tracking detectors in a format resembling images [A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission, Andrii Tykhonov et al,Astroparticle Physics 146, April 2023, 102795 102795].
Although these approaches demonstrated potential, image-based methods encountered difficulties in adapting to the scale of realistic data, primarily due to the high dimensionality and sparsity of the data.
Tracking data are naturally represented as graph by identifying hits as nodes and tracks segments as (in general) directed edges.
So that, we have explored the use of geometric deep learning techniques. Specifically, we have developed an algorithm that leverages the Graph Neural Network approach, which is a subset of geometric deep learning. This approach has been applied to the task of track reconstruction in a simplified model of space experiments.
The details of our toy model simulations, the algorithm's development process, and the preliminary results are described in the accompanying slides
